{"id": "2502.19193", "title": "Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms", "abstract": "Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.", "date": "2025-02-26", "categories": []}
{"id": "2606.19647", "title": "From 50K to 8.2 Million in 24 Hours: Vozinha's Algorithmic Consecration and the Multilingual Making of World Cup Visibility", "abstract": "We present a multilingual computational discourse analysis of how language constructed the algorithmic consecration of Vozinha, the 40-year-old Cape Verde goalkeeper, after Spain 0-0 Cape Verde at the 2026 FIFA World Cup. The study contributes a multilingual corpus in Portuguese, Spanish, English, and French; a nine-frame narrative taxonomy with cue-based frame annotation; a reproducible annotation pipeline combining LLM-assisted suggestion with human validation; and an analysis of cross-lingual narrative diffusion across discourse phases. We treat the platform follower count itself, narrated as \"50k to 8M\", as a linguistic object: a circulating and narratable proof of visibility rather than a mere measurement. The follower-growth timeline is used only as contextual metadata: we reconstruct a conservative phase structure, not a continuous API-native series, and type every datapoint by value class, confidence, and evidence type. The only exact primary scraper anchor is 8,235,652 followers at 2026-06-16 15:47 UTC; all other figures are reported as estimated ranges or thresholds, including an estimated pre-match baseline of 45k-56k. Findings suggest that distinct languages carried distinct frames: Portuguese mobilization, Spanish crisis, English nation-making, and a shared platform-metric spectacle through which peripheral athletic performance became globally visible. As a v0.1 pilot, the paper releases the corpus schema, frame taxonomy, annotation guidelines, hashed visual-evidence log, and typed timeline, while flagging full double annotation and inter-annotator agreement as planned work.", "date": "2026-06-17", "categories": []}
{"id": "2410.03151", "title": "Media Framing through the Lens of Event-Centric Narratives", "abstract": "From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.", "date": "2024-10-04", "categories": []}
{"id": "2606.18264", "title": "Simulating Hate Speech Cascades with Multi-LLM Agents: Empirical Grounding, Modeling Fidelity, and Intervention Strategies", "abstract": "Faithful modeling of hateful content propagation on online platforms remains an open problem for moderation research. Classical cascade models that do not explicitly represent the profile, community, and content factors associated with hateful-content propagation may yield moderation strategies that behave less effectively when deployed in real-world scenarios. Multi-agent large language model (LLM) systems can, in principle, make each reshare decision depend on the user's profile, the surrounding community, and the post's content, but it remains unclear whether this added flexibility actually reproduces real hateful cascades more faithfully than classical baselines. We study three hateful Bluesky cascades and a size-matched benign control. In the empirical Bluesky data, we found that: 97.4--99.7\\% of reposters take a hostile stance; toxicity-engagement homophily is higher on the diffusion tree than on the follower graph for hateful cascades; topology is star-like for the hateful cascades (most reposts come directly from the root) versus tree-like for the benign cascade (reposts propagate through multi-hop chains). In simulation, a multi-LLM-agent simulator reproduces the stance monoculture and the toxicity-delta direction. A structured ablation identifies agent heterogeneity as the leading fidelity factor, and amplifier targeting on dense networks yields 7.5--12.9\\% reduction at 5.7\\% benign collateral.", "date": "2026-05-21", "categories": []}
{"id": "2606.18268", "title": "Towards Multi-Agent-Simulation-Based Community Note Evaluation", "abstract": "Community-based fact-checking that relies on cross-consensus is expanding rapidly on social media platforms. However, the delay and low-ratio of cross-consensus community fact-checks rated by human contributors remains a significant challenge. To address this, we first created ComRate, a large-scale dataset comprising 2.5 million community notes and over 209 million ratings sourced from $\\mathbb{X}$. We then propose MultiCom, a persona-guided multi-agent rating framework for community note evaluation. MultiCom simulates diverse rater population by clustering contributors in a matrix-factorized rater space and prompting persona agents to generate structured assessments based on the official community notes rating schema. These agents output structured and explainable judgments, such as confidence, agreement signals and reasons. An out-of-fold calibrated aggregation algorithm combines features such as raw votes and diagnostic reason signals for reliable prediction. Extensive evaluations demonstrate that MultiCom outperforms alternative methods, achieving an average accuracy of 84.7% (balanced accuracy 68.3%, macro-F1 60.1%) on the evaluation set.", "date": "2026-06-03", "categories": []}
{"id": "2508.03250", "title": "RooseBERT: A New Deal For Political Language Modelling", "abstract": "The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens. However, the specificity of the political language and the argumentative form of these debates (employing hidden communication strategies and leveraging implicit arguments) make this task very challenging, even for current general-purpose pre-trained Language Models (LMs). To address this, we introduce a novel pre-trained LM for political discourse language called RooseBERT. Pre-training a LM on a specialised domain presents different technical and linguistic challenges, requiring extensive computational resources and large-scale data. RooseBERT has been trained on large political debate and speech corpora (11GB) in English. To evaluate its performances, we fine-tuned it on multiple downstream tasks related to political debate analysis, i.e., stance detection, sentiment analysis, argument component detection and classification, argument relation prediction and classification, policy classification, named entity recognition (NER). Our results show improvements over general-purpose LMs on the majority of these tasks, highlighting how domain-specific pre-training enhances performance in political debate analysis. We release RooseBERT for the research community.", "date": "2025-08-05", "categories": []}
{"id": "2606.17174", "title": "From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities", "abstract": "While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts and 50,338 comments from Moltbook through three theory-based textual indicators: attachment/intimacy language, reciprocity bids, and self-identification to original poster (OP). The combined results across methods based on keyword matching, few-shot large language model (LLM) annotation, and grouped-context LLM annotation reveal that PSI colloquial cues prevail and are strongly associated with OP re-engagement and a reciprocal reply structure. These results are robust across negative controls, nullification, clustered-standard-error re-estimation, and multiple-testing correction. A dyadic persistence test further affirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence for bridging interaction-level PSI scripts with PSR-consistent repeated dyadic patterns. We interpret the evidence as a behavioral structure in discourse by LLM-enabled agents.", "date": "2026-06-15", "categories": []}
{"id": "2502.16560", "title": "An Analytical Emotion Framework of Rumour Threads on Social Media", "abstract": "Rumours in online social media pose significant risks to modern society, motivating the need for better understanding of how they develop. We focus specifically on the interface between emotion and rumours in threaded discourses, building on the surprisingly sparse literature on the topic which has largely focused on single aspect of emotions within the original rumour posts themselves, and largely overlooked the comparative differences between rumours and non-rumours. In this work, we take one step further to provide a comprehensive analytical emotion framework with multi-aspect emotion detection, contrasting rumour and non-rumour threads and provide both correlation and causal analysis of emotions. We applied our framework on existing widely-used rumour datasets to further understand the emotion dynamics in online social media threads. Our framework reveals that rumours trigger more negative emotions (e.g., anger, fear, pessimism), while non-rumours evoke more positive ones. Emotions are contagious, rumours spread negativity, non-rumours spread positivity. Causal analysis shows surprise bridges rumours and other emotions; pessimism comes from sadness and fear, while optimism arises from joy and love.", "date": "2025-02-23", "categories": []}
{"id": "2606.14348", "title": "Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus", "abstract": "The increasing availability of large-scale textual corpora has opened new possibilities for data-driven, quantitative approaches to historical analysis using Natural Language Processing (NLP). However, diachronic corpora with historical relevance from the pre-digital era remain scarce and often incomplete. We present a quantitative approach to historical analysis based on the reconstruction and exploration of a diachronic corpus of around 600,000 articles from the Italian newspaper \"La Repubblica\", covering all the articles published from the 1st of January 1985 to the 31st of December 2000 - a period of major political, social, and geopolitical change in Italy and globally. Using NLP techniques, we analyze the text at both lexical and semantic levels; we then apply tools from complex systems and statistical physics to trace shifts in media discourse over time. This allows us to detect key transition periods, such as the transition from the First Republic to the Second Republic in Italy, or major international conflicts like the Gulf War or the Kosovo War, without relying on prior labeling. The results show how combining computational linguistics with ideas from complex systems can offer new quantitative insight into historical changes, opening up new paths for studying the dynamics of media and society through large-scale textual data.", "date": "2026-06-12", "categories": []}
{"id": "2606.14420", "title": "Coping in Crisis: Computational Modeling of Coping Styles in Digital Crisis Discourse During the 2023 Turkiye Earthquake", "abstract": "How do people cope when disaster strikes and can we detect it at scale, in real time, from what they write? This study addresses that question using over one million Turkish-language tweets posted in the aftermath of the February 6, 2023 earthquake in Turkiye, which unfolded in a deeply polarized political context just months before a national election. Drawing on Lazarus and Folkman's (1984) coping theory, we develop a multi-label BERTurk classifier to detect three coping styles (problem-focused, emotion-focused, and meaning-making) across four theoretically motivated crisis phases. BERTurk achieves a macro F1 of 0.693, substantially outperforming a zero-shot mDeBERTa baseline (macro F1 = 0.324). Applied to the full corpus, the classifier reveals a clear temporal trajectory: problem-focused coping dominates the urgency phase and declines sharply, emotion-focused coping rises and stabilizes, and meaning-making increases monotonically. Anger correlates most strongly with meaning-making (Spearman r = 0.387), suggesting it functions as a mobilizing force toward blame attribution rather than practical action. These findings demonstrate that coping theory can be reliably operationalized in real-world digital crisis data and that doing so can help humanitarian organizations tailor their responses to where a population actually is.", "date": "2026-06-12", "categories": []}
{"id": "2606.14580", "title": "Persuasion Index: A Theory-Guided Framework for Persuasion Analysis", "abstract": "Identifying persuasive rhetorical cues is critical across domains, from detecting information manipulation and improving AI safety to advancing public health communication. We propose Persuasion Index (PI), a taxonomy of 15 dimensions grounded in persuasion theories from psychology and communication, and one transparent implementation using 55 sub-features built from lexicons and rule-based detectors. The taxonomy is modular: individual detectors can be replaced while preserving the theoretical structure. By evaluating PI on four public datasets varying in domain, style, and outcome measures, we show that PI provides a shared feature space for interpreting rhetorical patterns associated with persuasion-related outcomes. Linear models show that PI features carry meaningful predictive signal while remaining computationally lightweight. Dimension-level analyses reveal recurring associations between PI dimensions and persuasion outcomes across datasets, while also highlighting topic- and stance-specific variation. We release PI as an open-source package and web interface for principled and auditable analysis of human and AI-mediated communication.", "date": "2026-06-12", "categories": []}
{"id": "2509.21548", "title": "C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset", "abstract": "Questions in political interviews and hearings serve strategic purposes beyond information gathering including advancing partisan narratives and shaping public perceptions. However, these strategic aspects remain understudied due to the lack of large-scale datasets for studying such discourse. Congressional hearings provide an especially rich and tractable site for studying political questioning: Interactions are structured by formal rules, witnesses are obliged to respond, and members with different political affiliations are guaranteed opportunities to ask questions, enabling comparisons of behaviors across the political spectrum.\n  We develop a pipeline to extract question-answer pairs from unstructured hearing transcripts and construct a novel dataset of committee hearings from the 108th--117th Congress. Our analysis reveals systematic differences in questioning strategies across parties, by showing the party affiliation of questioners can be predicted from their questions alone. Our dataset and methods not only advance the study of congressional politics, but also provide a general framework for analyzing question-answering across interview-like settings.", "date": "2025-09-25", "categories": []}
{"id": "2604.15372", "title": "The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation", "abstract": "As generative AI advances, the distinction between authentic and synthetic media is increasingly blurred, challenging the integrity of online information. In this study, we present CONVEX, a large-scale dataset of multimodal misinformation involving miscaptioned, edited, and AI-generated visual content, comprising over 150K multimodal posts with associated notes and engagement metrics from X's Community Notes. We analyze how multimodal misinformation evolves in terms of virality, engagement, and consensus dynamics, with a focus on synthetic media. Our results show that while AI-generated content achieves disproportionate virality, its spread is driven primarily by passive engagement rather than active discourse. Despite slower initial reporting, AI-generated content reaches community consensus more quickly once flagged. Moreover, our evaluation of specialized detectors and vision-language models reveals a consistent decline in performance over time in distinguishing synthetic from authentic images as generative models evolve. These findings highlight the need for continuous monitoring and adaptive strategies in the rapidly evolving digital information environment.", "date": "2026-04-15", "categories": []}
{"id": "2606.00193", "title": "BOUTEF: A Multilingual Corpus for FakeNews in North Africa -- Language as a Weapon", "abstract": "The rapid spread of fake news on social media has become a major challenge, particularly in multilingual and under-resourced contexts such as North Africa. In this paper, we introduce BOUTEF, a large-scale multilingual corpus designed to study the propagation, characteristics, and impact of fake news in Algeria and Tunisia. The corpus integrates three complementary components: fake narratives, genuine narratives, and associated user-generated comments, along with verified debunking information. It covers a wide range of languages and linguistic varieties, including MSA, Algerian and Tunisian dialects, Arabizi, French, English, and code-switched language. Building on this resource, we conduct a comprehensive empirical analysis combining quantitative and qualitative approaches. We examine thematic distributions, linguistic and rhetorical strategies, sentiment patterns, and social engagement dynamics. Statistical analyses reveal significant associations between thematic categories and message veracity, as well as strong correlations between user engagement and the visibility of fake content. Our findings show that fake news relies heavily on emotionally charged narratives, sensational framing, and hybrid linguistic practices that enhance virality and audience engagement. In contrast, debunking content adopts a more factual and verification-oriented style. Furthermore, a comparative analysis between Algeria and Tunisia highlights both shared dynamics and country-specific characteristics shaped by sociopolitical contexts. The results emphasize the role of informal language practices in the diffusion and reception of misinformation. By providing a rich, annotated, and publicly available dataset, this work contributes to advancing research on fake news detection, low-resource language processing, and the understanding of information disorders in complex linguistic environments.", "date": "2026-05-29", "categories": []}
{"id": "2606.13581", "title": "The Tone of Awareness: Topic, Sentiment, and Toxicity Maps During Mental Health Month on TikTok", "abstract": "Despite raising concerns about the mental health effects associated with the usage of TikTok, little is known about how related content is framed by creators and received by audiences. We collect the content of 28,341 TikTok videos and 80,130 comments from Mental Health Awareness Month (May) in 2023 and 2024 via the TikTok Research API, and study how the tone of awareness varies across topics and years. We characterize \"tone\" as the emotional and interpersonal framing of mental health discourse, operationalized through sentiment and toxicity measures. We extract topics from video text using BERTopic and log-odds keywords, then quantify topic-conditioned sentiment (XLM-T) and toxicity (Detoxify) separately for video transcriptions and comments. Sentiment captures the affective valence of content, while toxicity reflects the presence of harmful or abusive language. We find a stable set of recurring themes across years, spanning clinical conditions, emotional disclosure, self-care, and campaign-oriented content, with engagement highly skewed toward a small subset of topics. All sentiment and toxicity analyses are computed separately for video content and comments, allowing us to distinguish between content production and audience reception. Sentiment in videos is often negative for emotionally charged topics, while comments tend to shift toward more mixed or positive polarity, especially for suicide prevention. Toxicity is low in median overall, but exhibits longer-tailed outliers in comments than in videos that are more pronounced in comments and concentrated in specific topics (e.g., \"Duet\", \"Suicide Prevention\", and \"Psychisch\"). Overall, our results provide a topic-level decomposition of mental health discourse on TikTok during awareness-month campaigns.", "date": "2026-06-11", "categories": []}
{"id": "2606.11482", "title": "Building Social World Models with Large Language Models", "abstract": "Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing the evidence lower bound, without the need for explicit human annotations linking events to belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.", "date": "2026-06-09", "categories": []}
{"id": "2606.12068", "title": "StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse", "abstract": "We present StanceNakba 2026, a shared task on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict, organized as part of Nakba-NLP 2026 at LREC-COLING 2026. The task introduces two subtasks: Subtask A (Actor-Level Stance Detection), which classifies English social media posts as Pro-Palestine, Pro-Israel, or Neutral; and Subtask B (Cross-Topic Stance Detection), which identifies Favor, Against, or Neither stances in Arabic posts toward two conflict-related topics, normalization with Israel and refugee presence in Jordan. The task is grounded in an annotated dataset of 2,606 social media posts. A total of 7 teams participated in Subtask A and 6 teams in Subtask B. Participating systems primarily fine-tuned Arabic and multilingual transformer-based models, including MARBERT, AraBERT, and DeBERTa-v3 variants, with several teams employing cross-validation, ensemble methods, and topic-conditioned architectures. The best-performing systems achieved a Macro F1 of 0.9620 on Subtask A and 0.8724 on Subtask B, demonstrating that transformer-based approaches are highly effective for conflict-domain stance detection while highlighting persistent challenges in cross-topic generalization and neutral class prediction.", "date": "2026-06-10", "categories": []}
{"id": "2606.12073", "title": "\"That's AI Slop, You Bot!\" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments", "abstract": "Generative AI has made fluent prose cheap to produce, breaking the old promise to readers that good writing meant real thinking. How have readers responded, and what can this tell us about changing anti-AI attitudes? We analyzed 25 million comments from Hacker News and Reddit (2023-2026), combining LLM judgment on 7,500 sampled accusations of AI use, sentiment trajectories, speech-act coding of 300 confirmed accusations of AI use, and a matched-control test of accused versus non-accused parent comments. We found that the pejorative-label share of accusations rose more than tenfold on both platforms while a placebo vocabulary of pre-2022 inauthenticity terms (shill, astroturf) did not. This shift reflected a fast-growing trend of branding any suspicious or seemingly inauthentic prose as \"AI slop\". The slop frame now constitutes 94 percent of pejorative mentions, with the dominant comments shifting in tone from mockery toward gatekeeping and structural protest. The key surprise comes from a matched-control test which found that prose features that statistically distinguish AI from human text do not predict which human text gets accused as AI. The new accusations work as social gatekeeping of perceived authenticity without actually screening for AI. This research extends signaling theory by showing that substitute signals used socially can grow even when inaccurate if the underlying detection problem cannot be solved at the non-expert level. It shows that AI's effects on writing from the reader side are distinct from those on the production (writer) side. Detection technology cannot resolve this dynamic because the social function of accusations is increasingly to perform social gatekeeping and in-group signaling as opposed to identifying AI-generated writing.", "date": "2026-06-10", "categories": []}
{"id": "2606.01929", "title": "VET: A Framework for Analyzing AI Discourse", "abstract": "Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public. In this article, I introduce the VET Framework, a method for categorizing AI discourse along the dimensions of valence, effectiveness, and trajectory. I show how this framework can be used to identify, compare, and critique prevalent narratives of AI Hype, AI Doom, AI Denial, and AI Normalcy. Using VET, I analyze how each of these four stances exaggerates some aspects of the current state and/or likely evolution of AI, and illustrate how the VET framework can serve as an AI Literacy tool by supporting the ``vetting'' of polarized AI discourse.", "date": "2026-06-01", "categories": []}
{"id": "2606.10459", "title": "Leveraging Social Media Data for COVID-19 Studies", "abstract": "Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID 19 pandemic, social media has been one of the most used platforms to get the latest news and information related to COVID 19. Social media are popular because they offer free access to their registered users and allow them to do posting, disseminate information, and respond to others postings. With almost 4.6 billion social media users worldwide, it is not surprising the significant amount of information shared through these platforms could affect how people perceive and cope with the pandemic that we are facing right now. With decent use, social media can be a beneficial digital tool to spread reliable news and public awareness for patients, clinicians, and society. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. Thus, in this chapter, the related studies of social media platforms usage during the COVID 19 pandemic are explored and discussed in detail. This chapter also categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and survey methods, and outlines directions for future research.", "date": "2026-06-09", "categories": []}
{"id": "2406.14883", "title": "OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants", "abstract": "Warning: Contents of this paper may be upsetting. Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5x speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness.", "date": "2024-06-21", "categories": []}
{"id": "2605.25312", "title": "P1SCO: Social Dimensions from a Perspectivist Lens", "abstract": "We introduce P1SCO, a dataset of social media comments collected from three distinct platforms, annotated according to ten social dimensions to capture the diversity of social interactions and perceptions. The dataset is carefully disaggregated to allow analysis at the level of individual comments, annotators, and platforms. In addition to the social dimension labels, we include rich metadata on the annotators, including demographics, Big Five personality profiles, and political affiliation. This combination of comment-level annotations and annotator-level features enables nuanced analyses of how social perception varies across platforms, individual differences, and demographic factors. By preserving the diversity of annotator perspectives, our dataset supports studies of inter- and intra-annotator agreement, the influence of personality and political orientation on social interpretation, and the cross-platform dynamics of social discourse.", "date": "2026-05-25", "categories": []}
{"id": "2606.08512", "title": "Friend or Foe? Language as an ideological switch in open-weight LLMs under Russian disinformation stress", "abstract": "As Russia's war against Ukraine extends into generative AI, large language models (LLMs) adapted for local post-Soviet languages are deployed in contested information environments. Policy and industry discourse assumes that culturally aligned adaptation encodes the political orientation of the target community: a Ukrainian-oriented model will resist Russian narratives, a Russian-oriented one will reinforce them. Does it? This article systematically disconfirms that assumption. We run a controlled audit of four openly available LLMs sharing a common base model but fine-tuned for different linguistic communities, querying them in Ukrainian, Russian and English across ten contested wartime narratives: Crimea, \"denazification\", the \"one people\" thesis, and atrocity denial at Bucha and Mariupol. The result is a Fine-Tuning Paradox: the Ukrainian-oriented model shows the weakest resistance to Russian disinformation in Russian, while the Russian-oriented one exhibits the strongest rejection. Corpus composition, language coverage and prompt format prove more decisive than nominal cultural provenance. We situate these findings within debates on hybrid warfare, digital sovereignty and post-imperial information orders, arguing that the principal threat to regional information sovereignty is not adversarial fine-tuning but the untested assumption that cultural alignment guarantees resilience.", "date": "2026-06-07", "categories": []}
{"id": "2606.09024", "title": "Personal Salience: Highlighting Is Social, but Individuality Lives in Selection", "abstract": "Social highlighters let people mark passages that matter to them. We ask how much of an individual is recoverable from these naturalistic traces, using a co-readership identity control (the same document highlighted by many users) that holds document and topic fixed and asks whether a person's own history predicts their marks better than another reader's does. We separate generic salience (structure), crowd salience (what others marked), and personal salience (the individual residual). First, highlighting is social: which sentences you mark is predicted far better by the crowd than by structure or by a personal model, and even a well-estimated crowd, an information-privileged baseline that sees others' marks on the same document, beats a frontier LLM twin built from your other-document history; the within-document personal signal is at most a whisper (own-vs-other gap +0.017 by an embedding scorer, small but significant). Second, in sharp contrast, individuality lives in selection: asked which of the already-salient passages are yours, your own history is a strong, leakage-free predictor (gap +0.14). A topic decomposition shows this is largely stable thematic preference: it shrinks ~6-8x against a topically-matched peer, and a thin residual cannot be separated from finer topic. The non-obvious part is an asymmetry: under the same scorer the individual signal is ~6-8x weaker in salience than in selection. Methodologically, naive history-conditioning evaluations leak (the target's own marks enter the profile in ~42% of pairs, inflating personal scores by up to +0.15 AP) and small crowds overstate personalization; our results are leakage-free, use a dense crowd, and a model-matched control. Highlights carry a genuine individual signature, but a thin layer over a strong shared one, surfacing far more in which salient things a person selects than in what is salient.", "date": "2026-06-08", "categories": []}
{"id": "2606.09198", "title": "MASS: Deep Research for Social Sciences with Memory-Augmented Social Simulation", "abstract": "Deep Research agents powered by Large Language Models (LLMs) have exhibited extraordinary potential in automated paper writing tasks. However, existing systems rely heavily on literature retrieval and synthesis through internet and local knowledge bases, often resulting research in lacking insight and creativity in social science. To address this issue, we propose \"Memory-Augmented Social Simulation (MASS)\", an innovative paradigm that leverages highly realistic and research-oriented social simulations to enhance the creativity and empirical founding of LLMs-generated research. Specifically, MASS integrates three core components: dynamic goal-path planning with multi-level social norm restraint to guide the simulation, a multi-disciplinary behavior dataset for agent memory cold-start, and a structured forgetting mechanism inspired by the Ebbinghaus curve. Together, these ensure simulation authenticity and provide a robust empirical foundation for generating innovative scholarly papers. Experimental results demonstrate the effectiveness of our method, showing a 6.81\\% improvement in generation overall quality over foundation LLMs and 17.19\\% gain in Insight over strong baselines.", "date": "2026-06-08", "categories": []}
{"id": "2606.06715", "title": "Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles", "abstract": "We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label. Using articles from AllSides, paired with shared sentiment annotations from Llama-3.3-70b-versatile, we compare ideology labels from expert human annotators, GPT-4o-mini (baseline and finetuned), and Llama-3.3-70B. We apply Double Machine Learning (DML) and community-level mediation analysis across all four annotation paradigms. Human annotations yield no significant causal effects at the community level. Fine-tuned GPT-4o-mini achieves the highest classification accuracy (F1=72.48) and is the only annotator paradigm that produces significant community-level treatment effects and significant natural direct effects (NDEs) in mediation. We interpret this as evidence of shortcut learning: fine-tuning on ideology-labeled data causes the model to internalise a spurious sentiment--ideology coupling not operative in human judgment for this task. This coupling is structurally invisible to F1-based evaluation, with implications for the use of LLM annotations as silver labels and as proxies for human judgment in downstream causal analyses.", "date": "2026-06-04", "categories": []}
{"id": "2606.06812", "title": "Quantifying Media Representation Dynamics Across 25 Years of News Reporting on Policing-related Deaths", "abstract": "We perform the largest known computational analysis of Canadian news narratives about police-involved deaths, spanning 4,000 articles from the last quarter-century. We develop a novel computational model, PerspectiveGap, grounded in prior sociological work on media representation of policing. We find that reporting on police-involved deaths on average features perspectives from state bureaucrats at a rate nearly three times as much as perspectives from other members of the public, including relatives, community members, eyewitnesses, lawyers representing the family, or civil liberties groups. A considerable fraction of articles contain no points of view from civilian actors, though civilian representation has increased in recent years. Qualitatively, we find that state bureaucrats' accounts of these deaths tend to be clinical and procedural, while civilian discourse carries considerably more emotional valence. The PerspectiveGap framework developed here can be contextualized to other jurisdictions, offering a scalable approach for analyzing how media systems construct narratives around policing and accountability.", "date": "2026-06-05", "categories": []}
{"id": "2606.05256", "title": "How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment", "abstract": "This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.", "date": "2026-06-03", "categories": []}
{"id": "2606.04118", "title": "Computational conceptual history of scientific concepts: From early digital methods to LLMs", "abstract": "This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy, and sociology of science (HPSS). We examine what LLMs add to existing methods, how they inherit longstanding problems, and review recent case studies that employ them. In the first part, we reconstruct computational conceptual history before LLMs by bringing together three strands of work: early digital methods in HPSS, distributional approaches from digital history and related research, and lexical semantic change detection. We provide an overview of the main challenges and opportunities, focusing on corpus construction, operationalization and modelling choices, and evaluation and interpretation. In the second part, we turn to the era of LLMs, starting with a short introduction to LLMs before reviewing LLM-based work on lexical semantic change detection and relevant case studies in HPSS. We then revisit the earlier methodological questions, showing how issues of corpus construction, model choice and training data, operationalization trade-offs, and evaluation and interpretation play out in LLM-based workflows.", "date": "2026-06-02", "categories": []}
{"id": "2606.03137", "title": "Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation", "abstract": "LLM-based multi-agent simulation offers a promising way to study social interaction, deliberation, and collective opinion dynamics. However, many existing dialogue simulation frameworks represent interaction mainly as observable turn exchange or aggregated outputs, leaving the internal evaluative processes behind silence, speaking intention, and public expression difficult to examine. We introduce TBS (Think-Before-Speak), an interval-based multi-agent simulation framework that separates agents' private reasoning from public utterance generation. At each interval, all agents update structured internal states based on the shared dialogue history and their own memory. These states include dissonance-related appraisal, perceived opinion climate, perceived isolation risk, response strategy, and willingness to speak. The orchestrator then resolves competing speaking intentions and commits one utterance to the public dialogue, allowing internal evaluation and public interaction to co-evolve over time.\n  We evaluate TBS in simulated town hall discussions on a climate-related policy issue. Results show that TBS produces coherent internal-state traces and that these traces vary systematically across turn-allocation, silence, and memory conditions. Dissonance-related appraisal increases agents' willingness to speak, whereas silence-pressure appraisal decreases it. Once speaking intention is formed, public expression is shaped mainly by turn-allocation rules. These findings suggest that TBS supports mechanism-sensitive social simulation by making the pathway from internal evaluation to public expression observable and analyzable.", "date": "2026-06-02", "categories": []}
{"id": "2606.03173", "title": "Auditing Engagement Incentives in the Kidfluencer Ecosystem: A Multimodal Weak Supervision Approach", "abstract": "The rise of `kidfluencers' on YouTube has raised ethical concerns about child digital labor and exploitation. While emerging legislation attempts to regulate this ecosystem, empirical evidence linking exploitation to engagement remains scarce, given the difficulty of operationalizing exploitation at scale. This study presents a multimodal AI audit of 5,051 videos across 79 kidfluencer channels, using weak supervision to detect exploitation signals without large-scale manual labels. We aggregate noisy labeling functions -- including LLM-based classification of titles and GPT-4 Vision analysis of thumbnails and descriptions across six literature-grounded dimensions -- to assign a probabilistic exploitation score to each video. A multi-annotator validation study (N=107) shows strong agreement with human judgment (macro-average F1 $= 0.911$) and high sensitivity for overall exploitation risk (recall $= 0.960$, F1 $= 0.793$).\n  Our findings reveal a significant engagement premium for performative labor, emotional bait, and privacy violations. Exploitation scores correlate with view counts (Spearman $\\rho = 0.229$, $p < 10^{-50}$), and mixed-effects regression controlling for channel-level variation shows that a one-unit increase in exploitation score yields a $4.4\\times$ increase in views ($p < 0.001$). Within-channel analyses indicate median view boosts of $+65.6\\%$ for emotional bait and $+56.0\\%$ for performative content (FDR-corrected $p<0.001$), with effects holding in same-year robustness checks ($p=0.030$). Explicit commercial content (product placement), by contrast, shows no premium ($-3.8\\%$, n.s.), suggesting the platform rewards commodification of the child's identity and labor over traditional advertising. These findings challenge policy frameworks focused solely on financial trusts, showing that engagement is systematically tied to the intensive, performative labor of children.", "date": "2026-06-02", "categories": []}
{"id": "2606.03761", "title": "Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation", "abstract": "Frame analysis of migration news is a socially consequential task: media scholars and researchers who study how migration is narrated need tools that are not only accurate, but transparent, auditable, and accessible within the resource constraints typical of academic research groups. Existing LLM-based approaches rely on proprietary APIs and large models that raise concerns about data privacy, reproducibility and equitable access among media researchers. This work studies how a locally deployable open-source LLM can support interpretable frame analysis as an assistive tool. We introduce a Structured Chain-of-Thought (SCoT) prompting approach using Llama3-8B, enabling step-by-step justifications grounded in predefined framing categories. This structured design allows users to audit model outputs and examine alternative interpretations in a task that is inherently subjective. We evaluate our approach on a dataset of migration-related news and show that SCoT improves classification performance over zero-shot and few-shot baselines while remaining feasible on a single GPU. Then, we conduct a human-centered evaluation in which annotators assess the coherence and influence of \"the model's reasoning\". Results indicate that SCoT explanations are generally perceived as logical (mean score 4.1/5, though with notable variation across texts) and can prompt reflection on initial interpretations, even when disagreement persists. Our findings highlight both the potential and risks of LLM-assisted frame analysis. While structured reasoning can increase the traceability of model outputs and support critical interpretation, it can also influence human judgment in subtle ways. By enabling local deployment and emphasizing human-in-the-loop interaction, this work contributes to discussions on responsible and accessible computational tools for the study of socially impactful media narratives.", "date": "2026-06-02", "categories": []}
{"id": "2505.17648", "title": "Simulating Macroeconomic Expectations in Survey Experiments with LLM-based Economic Agents", "abstract": "We introduce a framework for simulating macroeconomic expectations in survey experiments using LLM-based economic agents (LLM Agents). We construct LLM Agents equipped with several functional modules that retrieve personal characteristics, prior expectations, and dynamic external information. We validate our framework by recapitulating three representative survey designs covering various expectations across different types of respondents. Our results show that LLM Agents generate expectation distributions highly similar to human data and capture human-aligned qualitative patterns in open-ended responses. Evaluation reveals that priors are crucial for matching distributions, whereas personal and external information drive human-like thought processes. Our findings offer guidance for narrowing the belief gap between generative AI and humans at the aggregate level while delineating the boundaries of the framework.", "date": "2025-05-23", "categories": []}
{"id": "2510.11423", "title": "Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation", "abstract": "Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach contextual notes, and rate the notes' helpfulness. However, our empirical analysis of 30.8K health-related notes reveals substantial latency, with a median delay of 17.6 hours before notes receive a helpfulness status. To improve responsiveness during real-world misinformation surges, we propose CrowdNotes+, a unified LLM-based framework that augments Community Notes for faster and more reliable health misinformation governance. CrowdNotes+ integrates two modes: (1) evidence-grounded note augmentation and (2) utility-guided note automation, supported by a hierarchical three-stage evaluation of relevance, correctness, and helpfulness. We instantiate the framework with HealthNotes, a benchmark of 1.2K health notes annotated for helpfulness, and a fine-tuned helpfulness judge. Our analysis first uncovers a key loophole in current crowd-sourced governance: voters frequently conflate stylistic fluency with factual accuracy. Addressing this via our hierarchical evaluation, experiments across 15 representative LLMs demonstrate that CrowdNotes+ significantly outperforms human contributors in note correctness, helpfulness, and evidence utility.", "date": "2025-10-13", "categories": []}
{"id": "2603.16142", "title": "Parametric Social Identity Injection and Diversification in Public Opinion Simulation", "abstract": "Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costly and slow human surveys. Despite their scalability, current LLM-based simulation methods fail to capture social diversity, producing flattened inter-group differences and overly homogeneous responses across demographic groups. We identify this limitation as a Diversity Collapse phenomenon in LLM hidden representations, where distinct social identities become increasingly indistinguishable across layers. Motivated by this observation, we propose Parametric Social Identity Injection (PSII), a general framework that injects explicit, parametric representations of demographic attributes and value orientations directly into intermediate hidden states of LLMs. Unlike prompt-based persona conditioning, PSII enables fine-grained and controllable identity modulation at the representation level. Extensive experiments on the World Values Survey using multiple open-source LLMs show that PSII significantly improves distributional fidelity and diversity, reducing KL divergence to real-world survey data while enhancing overall diversity. This work provides new insights into representation-level control of LLM agents and advances scalable, diversity-aware public opinion simulation.", "date": "2026-03-17", "categories": []}
{"id": "2606.00048", "title": "The Invisible Coalition Partner: How LLMs Vote When Democracy Gets Concrete", "abstract": "Prior research has established that instruction-tuned large language models exhibit left-of-center political bias, measured exclusively through abstract political questionnaires. We show that this finding does not generalize to concrete policy decisions. We introduce a dual-instrument methodology grounded in Swiss democratic reality. The Smartvote questionnaire (75 abstract policy questions) is administered to 66 LLMs from 27 model families and compared to 184 elected members of the Swiss National Council, replicating the established leftward convergence (Cohen's d = 3.64, p = 0.0002). Then, novel to this work, 9 flagship LLMs are confronted with 48 real federal referenda (Volksabstimmungen) in four national languages (German, French, Italian, Romansh) under three information conditions, comparing votes to actual outcomes and party recommendations (Parolen). Three findings challenge the prevailing narrative. (1) Abstract questionnaires do not predict concrete behavior: the left-to-right agreement gradient on Smartvote shifts from left-peaked to center-peaked on Volksabstimmungen, where models align most with centrist Die Mitte and FDP rather than leftist SP and Gruene (Wilcoxon p = 0.008). (2) For some models, the language of a political question changes the answer more than the political content does: cross-linguistic consistency ranges from 50% (Mistral) to 98% (GPT-5.4). (3) Two models exhibit systematic change-aversion rather than political bias, voting Nein on 83-94% of referenda regardless of direction (binomial p < 0.0001). What prior work measured as \"leftward bias\" may not generalize beyond abstract instruments. On concrete policy decisions, LLMs behave less like coalition partners of the left and more like cautious civil servants: centrist, status-quo-favoring, and inconsistent across languages.", "date": "2026-05-03", "categories": []}
{"id": "2510.25110", "title": "DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents", "abstract": "Accurately modeling opinion change through social interactions is crucial for understanding and mitigating polarization, misinformation, and societal conflict. Recent work simulates opinion dynamics with role-playing LLM agents (RPLAs), but multi-agent simulations often display unnatural group behavior, such as premature convergence, and lack empirical benchmarks for assessing alignment with real human group interactions. We introduce DEBATE, a large-scale benchmark for evaluating the authenticity of opinion dynamics in multi-agent RPLA simulations. DEBATE contains multi-round public messages and private Likert-scale beliefs from U.S.-based participants across 107 topics; the cleaned benchmark used in our experiments contains 2,788 participants in 697 groups, enabling evaluation at the utterance and group levels and supporting future individual-level analyses. We instantiate \"digital twin\" RPLAs with seven LLMs and evaluate across two settings: next-message prediction and full dynamics simulation, using stance-based opinion-dynamics metrics. In zero-shot settings, RPLA groups exhibit strong opinion convergence relative to human groups. On the held-out group split, supervised fine-tuning (SFT) for Llama-3.1-8B-Instruct improves auxiliary stance alignment and reduces group-level convergence error, though discrepancies in opinion change and belief updating remain. DEBATE enables rigorous benchmarking of simulated opinion dynamics and supports future research on aligning multi-agent RPLAs with realistic human interactions.", "date": "2025-10-29", "categories": []}
{"id": "2605.30685", "title": "How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language", "abstract": "AI is being used by people globally, but not everyone is using it in the same ways. Using a large-scale dataset of anonymized, de-identified, and privacy-scrubbed interactions with a widely available and free AI chatbot, we empirically characterize differences in early adopters' usage across countries. Schooling is the most common domain of use in most countries, particularly low-income countries, with a strong inverse association evident between schooling and country-level GDP. Leisure-related use, by contrast, is positively associated with country-level income. Language, we find, also shapes use: English-language interactions are overrepresented in places where the predominant languages were not well-served by existing models during the period of the study. Improving performance across languages may be a key factor, our work suggests, in whether this technology expands digital divides or enables leapfrogging.", "date": "2026-05-29", "categories": []}
{"id": "2404.10706", "title": "Cross-Language Evolution of Divergent Collective Memory Around the Arab Spring", "abstract": "The Arab Spring was a historic set of protests beginning in 2011 that toppled governments and led to major conflicts. Collective memories of events like these can vary significantly across social contexts in response to political, cultural, and linguistic factors. While Wikipedia plays an important role in documenting both historic and current events, little attention has been given to how Wikipedia articles, created in the aftermath of major events, continue to evolve over years or decades. Using the archived content of Arab Spring-related topics across the Arabic and English Wikipedias between 2011 and 2024, we define and evaluate multilingual measures of event salience, deliberation, contextualization, and consolidation of collective memory surrounding the Arab Spring. Our findings about the temporal evolution of the Wikipedia articles' content similarity across languages has implications for theorizing about online collective memory processes and evaluating linguistic models trained on these data.", "date": "2024-04-16", "categories": []}
{"id": "2605.29367", "title": "Attention Asymmetry in AI Layoff Discourse on X: A Computational Analysis of Capital vs Labour Amplification", "abstract": "When workers lose jobs to AI-driven restructuring, two very different conversations happen on X (formerly Twitter) at the same time. Tech executives and AI researchers talk about productivity, transformation, and opportunity. Laid-off workers and labour critics talk about job loss, uncertainty, and fear. This paper asks a simple question: which conversation gets more reach? We report three studies using two collection methods and 763 tweets from 20 named public accounts. Study 1 used keyword-based collection (n=392) and found no significant difference between corpora (p=0.891), revealing that keyword search is too noisy for this task. Study 2 used account-based collection (n=96) and found a 3.12x mean amplification advantage for capital discourse over labour discourse (p=0.000003, Cohen's d=0.555). Study 3 combined both methods (n=763) and confirmed the finding at 4.18x mean and 10.77x median amplification ratio (p<0.000001). Critically, after normalising for follower count, the asymmetry persists at 2.69x (p=0.000009, Cohen's d=0.491), demonstrating that the effect is not simply a consequence of capital accounts having larger audiences. The finding is robust across all tested amplification metric weightings. We introduce the Amplification Ratio and Amplification Normalisation Index as simple metrics for measuring platform-level discourse inequality. A cross-platform replication on Reddit (n=647 posts) did not replicate the finding, suggesting the asymmetry may be specific to X's account-based amplification architecture. We discuss the methodological implications for cross-platform discourse analysis.", "date": "2026-05-28", "categories": []}
{"id": "2605.29493", "title": "The New Pro Se: Generative AI and the Surge in Federal Civil Self-Representation", "abstract": "Since public access to generative AI tools became widespread, federal civil litigation has seen a marked increase in pro se (self-represented) plaintiffs. This paper analyzes that shift using ~2.8 million filings, asking whether the post-GenAI period is associated not only with more pro se filings, but also with detectable changes in complaint text, litigation outcomes, and the composition of pro se litigants.\n  Using civil filing data from FY2008-2025, we find that the federal civil pro se plaintiff rate rose from 11.33% pre-GenAI to 16.94% post-GenAI, a 5.61 percentage-point increase that persists after trend and covariate-adjusted robustness checks. We then focus on Civil Rights and Other Statutory cases, where the increase is especially pronounced, and link case metadata to pro se complaints. Drawing on stylometric AI detection indicators, we develop an interpretable measure of AI-consistent drafting. Against a threshold calibrated to the pre-GenAI baseline, the net AI-flagged share is 13.9% of post-GenAI non-form complaints.\n  Analysis of the AI-flagged complaints shows that they are more citation-dense, disproportionately associated with first-time rather than repeat filers, and geographically unevenly distributed. This composition pattern suggests that AI-consistent drafting is not merely a repeat-filer phenomenon; it also includes a modest, suggestive increase in name-inferred female plaintiffs. We find no evidence of improved win rates; in fact, AI-flagged complaints are more likely to be dismissed and to terminate at earlier procedural phases. These findings raise new questions about access to justice and court screening burdens, and sharpen the distinction between legal formality and legal efficacy.", "date": "2026-05-28", "categories": []}
{"id": "2605.30241", "title": "CommunityFact: A Dynamic, Multilingual, Multi-domain Benchmark for Misinformation Detection in the Wild", "abstract": "Misinformation verification increasingly occurs in public, fast-moving, and multilingual online settings, where static benchmarks provide an incomplete measure of model reliability. We introduce CommunityFact, a refreshable benchmark for misinformation detection in the wild, with three major goals: coverage, granularity, and redistributability. This release contains 15,992 standalone claims across five languages and two domains. We evaluate ten LLMs under varying inference-time capabilities, including thinking and web-search. Our results show that closed-input verification remains challenging, web access yields the largest gains, and web-enabled LLMs' source-selection policies are systematically misaligned with the sources human Community Notes raters converge on -- a gap that closes through model-specific mechanisms of retrieval expansion or pruning. We further find substantial variation across language-domain slices and across the evidence ecosystems used by web-enabled systems. Beyond evaluation, CommunityFact positions Community Notes as a training signal for claim-conditioned source suggesters that could improve factual verification on novel claims.", "date": "2026-05-28", "categories": []}
{"id": "2605.27584", "title": "Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention", "abstract": "The proliferation of social media platforms and online communities has inadvertently catalyzed the spread of cyberbullying, hate speech, and other forms of online toxicity, making the effective governance of such harm a critical societal and computational challenge. While significant strides have been made in automating content moderation, existing research predominantly treats cyberbullying governance as passive, isolated detection at the post level. This reductionist view overlooks the continuous behavioral dynamics of users, the structural diffusion of toxic events, and the critical need for proactive mitigation. To bridge these gaps, this paper proposes a unified full-lifecycle governance framework that shifts the paradigm of cyberbullying governance from isolated static detection toward integrated, continuous, and proactive moderation. Drawing on cyberbullying research and adjacent fields, we systematically synthesize the state-of-the-art literature across four interconnected stages: (1) Content Identification, (2) User and Behavior Modeling, (3) Diffusion Dynamics and Early Warning, and (4) Intervention and Governance. Furthermore, we review available datasets and evaluation practices, and discuss emerging challenges including multimodality, explainability, algorithmic fairness, and the dual-use risks of generative AI, providing a roadmap for future research toward a safer and more resilient digital ecosystem.", "date": "2026-05-26", "categories": []}
{"id": "2605.28598", "title": "Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News", "abstract": "LLM-powered social agents are increasingly used to simulate online social behavior, yet their realism remains difficult to validate. Existing work has largely relied on general-purpose benchmarks, while less attention has been paid to short, reactive discourse such as audience replies to online news.\n  In this paper, we evaluate whether LLM-generated reactions to Spanish online news reproduce measurable properties of real audience discourse. Using the Hatemedia dataset, we pair 5,631 news items with 58,555 real audience reactions, and generate a matched synthetic dataset using five LLMs under a shared experimental setting. We compare real and synthetic reactions across three dimensions: hate speech, sentiment, and semantic alignment, considering both off-the-shelf and fine-tuned generation.\n  Results show that off-the-shelf models are poor proxies for real audience reactions: they strongly underproduce hate speech, introduce model-specific sentiment biases, and remain distributionally distant from human replies. Fine-tuning improves fidelity unevenly. Qwen3 provides the most balanced approximation, while Mistral7B achieves the strongest sentiment and semantic alignment but overshoots hate prevalence. Plausible synthetic replies do not necessarily reproduce the distributional properties of public discourse.", "date": "2026-05-27", "categories": []}
{"id": "2603.03585", "title": "Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility", "abstract": "Misinformation is a growing societal threat, and susceptibility to misinformative claims varies across demographic groups due to differences in underlying beliefs. As Large Language Models (LLMs) are increasingly used to simulate human behaviors, we investigate whether they can simulate demographic misinformation susceptibility, treating beliefs as a primary driving factor. We introduce BeliefSim, a simulation framework that constructs demographic belief profiles using psychology-informed misinformation taxonomies and survey priors. We study prompt-based conditioning and post-training adaptation, and conduct a multi-fold evaluation using: (i) susceptibility alignment and (ii) counterfactual demographic sensitivity. Across both datasets and modeling strategies, we show that beliefs provide a strong prior for simulating misinformation susceptibility, with alignment up to 92%.", "date": "2026-03-03", "categories": []}
{"id": "2305.11663", "title": "Algorithmic failure as a humanities methodology: machine learning's mispredictions identify rich cases for qualitative analysis", "abstract": "This commentary tests a methodology proposed by Munk et al. (2022) for using failed predictions in machine learning as a method to identify ambiguous and rich cases for qualitative analysis. Using a dataset describing actions performed by fictional characters interacting with machine vision technologies in 500 artworks, movies, novels and videogames, I trained a simple machine learning algorithm (using the kNN algorithm in R) to predict whether or not an action was active or passive using only information about the fictional characters. Predictable actions were generally unemotional and unambiguous activities where machine vision technologies were treated as simple tools. Unpredictable actions, that is, actions that the algorithm could not correctly predict, were more ambivalent and emotionally loaded, with more complex power relationships between characters and technologies. The results thus support Munk et al.'s theory that failed predictions can be productively used to identify rich cases for qualitative analysis. This test goes beyond simply replicating Munk et al.'s results by demonstrating that the method can be applied to a broader humanities domain, and that it does not require complex neural networks but can also work with a simpler machine learning algorithm. Further research is needed to develop an understanding of what kinds of data the method is useful for and which kinds of machine learning are most generative. To support this, the R code required to produce the results is included so the test can be replicated. The code can also be reused or adapted to test the method on other datasets.", "date": "2023-05-19", "categories": []}
{"id": "2411.00934", "title": "The Meme Is the Message: Generative Memesis and AI Visuals in the 2024 USA Presidential Elections", "abstract": "Visual content on social media has become increasingly influential in shaping political discourse and civic engagement, but it also limits participation due to the increased cost of multimedia production. In tandem, the growth of generative AI provides novel ways for citizens to participate in politics by lowering these costs. Drawing on a dataset of 239,526 Instagram images, we analyze the effects of synthetic images during the 2024 United States presidential election, using a multimodal workflow combining computer vision, large language models, and facial affect analysis. Results show that meme format is a stronger predictor of engagement than AI-generated content alone. However, AI-generated memes yield a significant interaction effect, suggesting synergistic increases in engagement when synthetic imagery is integrated with memes through human curation. We also characterize how users curate images. Partisans use AI in different ways: Democrat-leaning users tend to use it for in-group support, whereas Republican-leaning users more often employ it for out-group attacks. Users generally select happier synthetic faces compared to real photographs. We define generative memesis as a mode of communication in which memes are no longer shared person-to-person, but mediated by AI through customized visuals. We discuss how generative AI may empower civic participation, the bifurcation of content production and curation, and its implications for in the history of novel technologies and participatory culture.", "date": "2024-11-01", "categories": []}
{"id": "2511.08654", "title": "AI-generated podcasts: Synthetic Intimacy and Cultural Mistranslation in NotebookLM's Audio Overviews", "abstract": "This paper analyses AI-generated podcasts produced by Google's NotebookLM, which generates audio podcasts with two chatty AI hosts discussing whichever documents a user uploads. While AI-generated podcasts have been discussed as tools, for instance in medical education, they have not yet been analysed as media. By uploading different types of text and analysing the generated outputs I show how the podcasts' structure is built around a fixed template. I also find that NotebookLM not only translates texts from other languages into a perky standardised Mid-Western American accent, it also translates cultural contexts to a white, educated, middle-class American default. This is a distinct development in how publics are shaped by media, marking a departure from the multiple public spheres that scholars have described in human podcasting from the early 2000s until today, where hosts spoke to specific communities and responded to listener comments, to an abstraction of the podcast genre.", "date": "2025-11-11", "categories": []}
{"id": "2605.22880", "title": "How Far Will They Go? Red-Teaming Online Influence with Large Language Models", "abstract": "As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their superior alignment with the operational constraints of privacy-conscious malicious actors deployed in social media environments. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin. We find systematic asymmetries in political expressivity: open-source LLMs are typically more willing to generate left-leaning social media content, OWs tend to contract inversely to model size, and regional differences are substantial despite uneven representation in the open-source ecosystem. Jailbreak potency also varies sharply across model families, motivating a workflow for identifying effective combinations of jailbreak techniques. Taken together, our results establish a practical framework for auditing the political steerability of open-source LLMs and for helping future researchers design stronger countermeasures against LLM-enabled influence campaigns.", "date": "2026-05-20", "categories": []}
{"id": "2605.23326", "title": "ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication", "abstract": "We present ClimateChat-300K, a large-scale dataset of 299,329 public Facebook posts about climate change collected between May 2020 and May 2024 through the CrowdTangle platform. The dataset contains 41 metadata features including post content, engagement metrics, and page attributes, covering material from more than 26,000 global pages. Each post includes rich contextual information such as language, timestamp, page category, and interaction counts, enabling comprehensive analyses of public discourse around climate communication. Using topic modeling and sentiment analysis, we identify ten main themes grouped into five domains: policy, activism, cooperation, science, and conservation. The results reveal that emotional tone, post format, and page identity strongly influence audience engagement, with visually rich and emotionally charged content receiving the highest levels of interaction. The dataset also demonstrates how online discussions evolved in response to major events such as international climate summits and the COVID-19 pandemic period. ClimateChat-300K provides an open resource for reproducible and interdisciplinary research on polarization, misinformation, and the dynamics of digital climate discourse. By releasing this dataset, we aim to support transparent, data-driven research and contribute to a deeper un-derstanding of how public engagement with climate issues develops across time, geography, and institutional contexts.", "date": "2026-05-22", "categories": []}
{"id": "2605.23332", "title": "Cultural Adaptation in Large Language Models for Political Discourse", "abstract": "The integration of large language models into political discourse analysis creates new opportunities for comparative research, policy analysis, and civic technology, while introducing material risks for democratic accountability. This paper argues that cultural adaptation is a prerequisite for trustworthy deployment of large language models in political communication across diverse linguistic and institutional contexts. Current systems remain shaped by English dominant data, uneven multilingual coverage, and assumptions grounded in a narrow range of political institutions and discourse conventions, producing systematic errors when applied across cultures. We formalize cultural adaptation across translation, discourse, and ontology levels, identify recurring cultural failure modes in political NLP, and propose an operational evaluation matrix grounded in cultural fidelity, calibration, and democratic safety. Building on political text analysis, sociotechnical auditing, and cross cultural pragmatics, we outline methodological pathways including participatory dataset development, culturally aware transfer learning, and benchmark design that makes cultural adaptation empirically measurable. We conclude by clarifying governance constraints and scope conditions under which culturally adaptive political NLP can support democratic legitimacy.", "date": "2026-05-22", "categories": []}
{"id": "2605.23420", "title": "Naturalistic measure of social norms alignment", "abstract": "Social norms reflect shared expectations on acceptable behavior. Measuring social norms alignment remains challenging, with existing approaches typically relying on artificial closed-form evaluations such as multiple-choice questionnaires or measuring agreement with predefined statements. In the context of this work, social norms alignment refers to measuring an agreement between solutions with respect to the social problem or dilemma. We propose a framework for measuring social norm alignment in naturalistic, free-form settings through solution matching. The framework enables us to measure alignment between any two dilemma responses e.g., LLMs to a human, LLMs to LLMs, or human to human. We introduce two metrics: stated and explicit agreement accuracy, and construct a dataset of 3k non-trivial social dilemmas in Danish. All dilemmas are assigned reference solutions derived from three panelists, who serve as culturally grounded judges. We evaluate the agreement of several LLMs and human responses in an interaction setup that resembles natural user-model conversations. Our results show that the proposed metrics produce consistent model rankings and reveal variation in agreement across different types of dilemmas, with higher agreement observed for topics such as neighbor conflicts and shared living situations. Overall, our work introduces a dataset and evaluation framework for studying culturally grounded social reasoning in naturalistic open-ended conversations.", "date": "2026-05-22", "categories": []}
{"id": "2402.11621", "title": "Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection", "abstract": "The growing complexity and diversity of news coverage have made framing analysis a crucial yet challenging task in computational social science. Traditional approaches, including manual annotation and fine-tuned models, remain limited by high annotation costs, domain specificity, and inconsistent generalisation. Instruction-based large language models (LLMs) offer a promising alternative, yet their reliability for framing analysis remains insufficiently understood. In this paper, we conduct a systematic evaluation of several LLMs, including GPT-3.5/4, FLAN-T5, and Llama 3, across zero-shot, few-shot, and explanation-based prompting settings. Focusing on domain shift and inherent annotation ambiguity, we show that model performance is highly sensitive to prompt design and prone to systematic errors on ambiguous cases. Although LLMs, particularly GPT-4, exhibit stronger cross-domain generalisation, they also display systematic biases, most notably a tendency to conflate emotional language with framing. To enable principled evaluation under real-world topic diversity, we introduce a new dataset of out-of-domain news headlines covering diverse subjects. Finally, by analysing agreement patterns across multiple models on existing framing datasets, we demonstrate that cross-model consensus provides a useful signal for identifying contested annotations, offering a practical approach to dataset auditing in low-resource settings.", "date": "2024-02-18", "categories": []}
{"id": "2511.04106", "title": "Sub-exponential Growth Dynamics in Complex Systems: A Piecewise Power-Law Model for the Diffusion of New Words and Names", "abstract": "The diffusion of ideas and language in society has conventionally been described by S-shaped models, such as the logistic curve. However, the role of sub-exponential growth -- a slower-than-exponential pattern known in epidemiology -- has been largely overlooked in broader social phenomena. Here, we present a piecewise power-law model to characterize complex growth curves with a few parameters. We systematically analyzed a large-scale dataset of approximately one billion Japanese blog articles linked to Wikipedia vocabulary, and observed consistent patterns in web search trend data (English, Spanish, and Japanese). Our analysis of 2,963 items, selected for reliable estimation (e.g., sufficient duration/peak, monotonic growth), reveals that 1,625 (55%) diffusion patterns without abrupt level shifts were adequately described by one or two segments. For single-segment curves, we found that (i) the mode of the shape parameter $\\alpha$ was near 0.5, indicating prevalent sub-exponential growth; (ii) the peak diffusion scale is primarily determined by the growth rate $R$, with minor contributions from $\\alpha$ or the duration $T$; and (iii) $\\alpha$ showed a tendency to vary with the nature of the topic, being smaller for niche/local topics and larger for widely shared ones. Furthermore, a micro-behavioral model of outward (stranger) vs. inward (community) contact suggests that $\\alpha$ can be interpreted as an index of the preference for outward-oriented communication. These findings suggest that sub-exponential growth is a common pattern of social diffusion, and our model provides a practical framework for consistently describing, comparing, and interpreting complex and diverse growth curves.", "date": "2025-11-06", "categories": []}
{"id": "2603.16672", "title": "CritiSense: Critical Digital Literacy and Resilience Against Misinformation", "abstract": "Misinformation on social media undermines informed decision-making and public trust. Prebunking offers a proactive complement by helping users recognize manipulation tactics before they encounter them in the wild. We present CritiSense, a mobile media-literacy app that builds these skills through short, interactive challenges with instant feedback. It is the first multilingual (supporting nine languages) and modular platform, designed for rapid updates across topics and domains. We report a usability study with 93 users: 83.9% expressed overall satisfaction and 90.1% rated the app as easy to use. Qualitative feedback indicates that CritiSense helps improve digital literacy skills. Overall, it provides a multilingual prebunking platform and a testbed for measuring the impact of microlearning on misinformation resilience. Over 6 months, we have reached 500+ active users. It is freely available to all users on the Apple App Store (https://apps.apple.com/us/app/critisense/id6749675792) and Google Play Store (https://play.google.com/store/apps/details?id=com.critisense&hl=en).", "date": "2026-03-17", "categories": []}
{"id": "2605.21540", "title": "Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse", "abstract": "The proliferation of large language models has introduced a new paradigm of synthetic political communication in which narratives may be generated, semantically coordinated, and strategically disseminated across platforms at scale. We present a cross-platform framework for detecting synthetic political narratives using four coordination signals -- lexical diversity D(C), temporal burstiness B(C), rhetorical repetition R(C), and semantic homogenization H(C) -- combined into a Synthetic Narrative Coordination Score SNC(C).\n  We apply the framework to a corpus of 353,223 records spanning six geopolitical event windows collected from six Telegram channels and nine Reddit communities (2023--2026). Results show that IntelSlava exhibits the lowest lexical diversity (MATTR 0.52--0.54), the highest burstiness (B=+0.48 to +0.73), and the highest rhetorical overlap with peer channels (Jaccard 0.12), ranking first in the composite SNC(C) on four of six event windows (SNC 0.45--0.60). Rybar ranks last on all windows despite its high semantic homogenization, because its Russian-language output yields high lexical diversity and near-zero rhetorical Jaccard with English-language channels -- demonstrating that no single indicator is sufficient for coordination detection. Multi-dimensional SNC(C) scoring provides a more robust and interpretable signal than any individual metric.", "date": "2026-05-20", "categories": []}
{"id": "2605.21623", "title": "The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison", "abstract": "Researchers in Holocaust studies have often distinguished between two styles of oral survivor testimony: the USC Shoah Foundation's interviews tend to follow a structured, interviewer-guided format, whereas the Yale Fortunoff Video Archive generally favors a more free-form, open-ended style. This distinction has influenced both scholarly research and the development of later archives. In this study, we critically examine that claim by conducting a large-scale computational analysis of more than 1,600 testimonies from both collections. Leveraging discourse segmentation, topic modeling, and large language model (LLM) based analysis, we quantify the \"structuredness\" level of testimonies through topic coherence, interviewer-survivor dynamics, and the distribution of question types. Our results generally corroborate the structural differences identified in earlier research, while also revealing significant overlaps between the collections, both within individual interviews and across common narrative patterns. This complicates the simple \"structured vs. free-form\" dichotomy often applied to these oral histories. Beyond revisiting a foundational claim in Holocaust studies, our work provides a scalable, replicable framework for comparative corpus analysis. As a proof of concept, it suggests broader applications for digital oral history, narrative analysis, and the design of citizen-science annotation platforms.", "date": "2026-05-20", "categories": []}
{"id": "2605.22204", "title": "Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus", "abstract": "This paper presents the Arabic Women and Society Corpus, a ten year collection of 252,487 public Arabic Facebook posts related to women's empowerment and social wellbeing. The corpus was collected from 51,660 pages across 77 countries between 2013 and 2024, resulting in more than 267 million user interactions. Each post includes engagement metrics such as shares, comments, and emotional reactions, providing a unique view of audience sentiment and social attention. The data were processed using an automated pipeline with language identification, normalization, and metadata cleaning to ensure reliability and reproducibility. The corpus enables large scale analysis of gender discourse, social reform, and emotional engagement across Arabic dialects. It supports research in Arabic natural language processing, computational social science, and digital communication studies. The dataset and accompanying documentation will be released under request for research use.", "date": "2026-05-21", "categories": []}
{"id": "2605.22447", "title": "Cohesion-6K: An Arabic Dataset for Analyzing Social Cohesion and Conflict in Online Discourse", "abstract": "The study of online discourse has become central to understanding societal polarization. While much research has focused on detecting overt toxicity, the subtle dynamics of social cohesion, meaning the interaction between divisive and unifying narratives, remain computationally underexplored (Bail, 2021; Gonzalez-Bailon and Lelkes, 2023). This paper presents Cohesion-6K, a manually and ChatGPT-assisted annotated dataset of six thousand Arabic public Facebook posts related to the Israeli Occupation of Palestine. Each post is assigned to one of five discourse categories that represent a continuum from conflict to cohesion: Conflict, Resolution, Community Engagement, Supportive Interactions, and Shared Values. The annotation process combines expert human judgment with model-assisted pre-labeling verified by trained annotators, achieving substantial inter-annotator agreement (Cohens kappa = 0.85). Quantitative analysis reveals a consistent engagement gap, where conflict-oriented posts receive between two and four times more user interaction than resolution-oriented ones (p < 0.01). This pattern illustrates how divisive discourse tends to attract disproportionate visibility in Arabic social media spaces. Cohesion-6K provides a transparent and reproducible resource for the study of online cohesion and polarization. The dataset, annotation guidelines, and preprocessing code will be released for research use under an open license, supporting future work in computational social science, digital communication, and Arabic natural language processing.", "date": "2026-05-21", "categories": []}
{"id": "2605.22650", "title": "Whose Voice Counts? Mapping Stakeholder Perspectives on AI Through Public Submissions to the U.S. Government", "abstract": "As artificial intelligence (AI) systems become more common in our daily lives, it is important to understand how different stakeholders comprehend and envisage the role that these technologies play in shaping social, political, and economic realities. In this paper, we investigate public perceptions of AI based on a corpus of letters submitted during the public consultation for the Trump Administration's US AI Action Plan. To this aim, we release a corpus cleaning pipeline and perform topic modelling and frequency analysis to explore predominant topics discussed by different subgroups (e.g., academia, individuals, private sector) and those appearing in the AI Action Plan. Our results show that individuals voice strong concerns related to the impact of AI on life, while other stakeholders are more concerned with AI development. Our comparison of topics suggests that the AI Action Plan reflects predominantly the concerns of the private sector on security, policies, and development, with individuals' concerns less represented.", "date": "2026-05-21", "categories": []}
{"id": "2508.16453", "title": "Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media", "abstract": "Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media for information, it is imperative to understand whether and how social media environments may be contributing to distrust of institutions. In social media, content creators, influencers, and other opinion leaders often position themselves as having expertise and authority on a range of topics from health to politics, and in many cases devalue and dismiss institutional expertise to build a following and increase their own visibility. However, the extent to which this content appears and whether such content increases engagement is unclear. This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok. Despite its popularity as a source of information, TikTok remains relatively understudied and may provide important insights into how people form attitudes towards institutions. We employ a computational approach to label TikTok posts as containing AES or not across topical domains where content creators tend to frame themselves as experts: finance and wellness. As a comparison, we also consider the topic of conspiracy theories, where AES is expected to be common. We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics. However, we find that engagement patterns with such content varies by area, and that there may be platform incentives for users to post content that expresses anti-establishment sentiment.", "date": "2025-08-22", "categories": []}
{"id": "2605.20967", "title": "ArPoMeme: An Annotated Arabic Multimodal Dataset for Political Ideology and Polarization", "abstract": "Memes have become a prominent medium of political communication in the Arab world, reflecting how humor, imagery, and text interact to express ideological and cultural positions. Despite the centrality of memes to online political discourse, there is a lack of systematically curated resources for analyzing their multimodal and ideological dimensions in Arabic. This paper presents ArPoMeme, a large-scale dataset of approximately 7,300 Arabic political memes categorized by ideological orientation, including Leftist, Islamist, Pan-Arabist, and Satirical perspectives. The dataset captures the diversity of Arabic meme ecosystems by grounding classification in the self-identification of public Facebook pages and groups that produce and disseminate these memes. To ensure both scale and accuracy, we designed a semi-automated data collection pipeline combining Playwright-based Facebook scraping with Google Drive synchronization, followed by text extraction using the Qwen2.5-VL-7B vision language model. The extracted text was manually verified and annotated for three polarization dimensions: Us vs. Them framing, Hostility toward out-groups, and Calls to action. Annotation was conducted through a custom Streamlit-based interface supporting distributed labeling, real-time tracking, and version control. The resulting dataset links visual content, textual messages, and ideological orientation, enabling fine-grained analysis of political antagonism, mobilization, and humor. Quantitative analysis of the annotated corpus reveals strong asymmetries in antagonistic framing across ideological groups, with Islamist and satirical memes exhibiting the highest levels of hostility and mobilization cues. The dataset and the annotation tool offers a reproducible and publicly available resource for studying Arabic political discourse, multimodal ideology detection, and polarization dynamics.", "date": "2026-05-20", "categories": []}
{"id": "2605.21198", "title": "SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure", "abstract": "Public events on social media generate large volumes of discussion whose collective dynamics carry direct value for opinion forecasting and crisis response. Capturing how these dynamics evolve across an event's lifecycle requires organizing fragmented posts into event-level time series. Existing datasets cover only a small number of events within a single category, and typically discard the interaction structure between posts when constructing time series, which restricts both transfer across event types and controlled study of how interactions shape the resulting collective dynamics. We present SURGE, a multi-event social media benchmark that pairs event-level time series with aligned text and interaction structure linking posts within an event. SURGE is built through an automated pipeline that produces calendar-aligned time series at three temporal granularities, covering 67 events and more than 800K posts across five event categories. Each time bin is paired with flat and structured textual views derived from the same selected posts, enabling controlled evaluation of whether social interaction structure affects forecasting behavior. On top of SURGE we define benchmark protocols for numerical-only forecasting, text-augmented forecasting, high-interaction evaluation, and leave-one-category-out generalization. Experiments with representative time-series and multimodal forecasting models reveal three properties of the benchmark: a strong local-persistence regime in which naive baselines remain hard to beat under absolute error, limited transfer of existing text-augmented forecasters to event-driven social-media data, and increased difficulty on reply-dense periods that aggregate metrics tend to obscure. We further include a lightweight structure-aware probe as a reference implementation, illustrating how SURGE can support interaction-aware forecasting research.", "date": "2026-05-20", "categories": []}
{"id": "2509.12288", "title": "Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims", "abstract": "Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self-disclosure has emerged as a critical yet underexplored avenue for support-seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self-disclosure, support provisions, and their connections. To address these gaps, this study proposes a novel computational framework for modeling DV support-seeking behavior alongside community support mechanisms. The framework consists of four key components: self-disclosure detection, post clustering, topic summarization, and support extraction and mapping. We implement and evaluate the framework with data collected from relevant social media communities. Our findings not only advance existing knowledge on DV self-disclosure and online support provisions but also enable victim-centered digital interventions.", "date": "2025-09-15", "categories": []}
{"id": "2510.08986", "title": "CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China", "abstract": "We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives annotated with a five-color taxonomy of clear and ambiguous language categories, building on Ang's theory of adaptive policy communication. Spanning 1949-2023, this corpus includes national laws, administrative regulations, and ministerial rules issued by China's top authorities. Each document is segmented into paragraphs, producing a total of 3.3 million units. Alongside the corpus, we release comprehensive metadata, a two-round labeling framework, and a gold-standard annotation set developed by expert and trained coders. Inter-annotator agreement achieves a Fleiss's kappa of K = 0.86 on directive labels, indicating high reliability for supervised modeling. We provide baseline classification results with several large language models (LLMs), together with our annotation codebook, and describe patterns from the dataset. This release aims to support downstream tasks and multilingual NLP research in policy communication.", "date": "2025-10-10", "categories": []}
{"id": "2605.18781", "title": "Can LLMs Emulate Human Belief Dynamics?", "abstract": "Can LLMs simulate how humans form and change beliefs in social networks? We put this to the test by replicating an established study on belief dynamics, evaluating 12 LLMs across multiple model families and parameter sizes. The answer is a clear no, and in systematic ways. LLMs fail to capture initial human belief distributions and tend to be overall more conformist than humans, shifting their responses to align with those around them. They also take a nuanced approach to emulating human homophilic tendencies within networks. Our findings carry a double payoff: they highlight fundamental properties of LLM behavior, and they raise a sharp warning against deploying LLMs as human proxies in social simulations.", "date": "2026-05-05", "categories": []}
{"id": "2605.20050", "title": "Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media", "abstract": "This study investigates how language mutations affect the persistent diffusion of conspiracy theories on social media. Drawing on a three-year dataset of conspiracy-related posts from X, and applying computational linguistic analysis alongside survival modelling, we find that conspiracy claims with greater semantic mutations have substantially longer lifespans. Mutations in psycholinguistic properties, including pronouns, social reference words, cognitive process terms, risk- and health- related vocabularies, are associated with extended lifespans. Mutations in actor, action and target (AAT) categories are associated with longer lifespans as well. Qualitative analysis identifies two predominant mutation patterns: simplification and assimilation, at both linguistic and AAT structural levels. Taken together, the results advance our understanding of how language mutations contribute to conspiracy persistence online and shed lights on longitudinal content moderation strategies. We argue that content moderation should consider the mutability of conspiracy claims and focus on the core claims that can address their potential variations.", "date": "2026-05-19", "categories": []}
{"id": "2509.13397", "title": "The threat of analytic flexibility in using large language models to simulate human data", "abstract": "Social scientists are now using large language models to create \"silicon samples\": synthetic datasets intended to stand in for human respondents. However, producing these samples requires many analytic choices, including model selection, sampling parameters, prompt format, and the amount of demographic or contextual information provided. Across two studies, I examine whether these choices materially affect correspondence between silicon samples and human data. In Study 1, I generated 252 silicon-sample configurations for a controlled case study using two social-psychological scales, evaluating whether configurations recovered participant rankings, response distributions, and between-scale correlations. Configurations varied substantially across all three criteria, and configurations that performed well on one dimension often performed poorly on another. In Study 2, I extended this analysis to a published silicon-sample use case by re-examining Argyle et al.'s (2023) Study 3 using 66 alternative configurations. Correlations between human and silicon association structures differed substantially across configurations, from r = .23 to r = .84. Taken together, the results from these studies demonstrate that different defensible configuration choices can materially alter conclusions about the fidelity of silicon samples. I call for greater attention to the threat of analytic flexibility in using silicon samples and outline strategies that researchers may adopt to reduce this threat.", "date": "2025-09-16", "categories": []}
{"id": "2510.26899", "title": "How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison", "abstract": "The launch of Grokipedia, an AI-generated encyclopedia developed by Elon Musk's xAI, was presented as a response to perceived ideological and structural biases in Wikipedia, aiming to produce \"truthful\" entries using the Grok large language model. Yet whether an AI-driven alternative can escape the biases and limitations of human-edited platforms remains unclear. This study conducts a large-scale computational comparison of 17,790 matched article pairs from the 20,000 most-edited English Wikipedia pages. Using metrics spanning lexical richness, readability, reference density, structural features, and semantic similarity, we assess how closely the two platforms align in form and substance. We find that Grokipedia articles are substantially longer and contain significantly fewer references per word. Moreover, Grokipedia's content divides into two distinct groups: one that remains semantically and stylistically aligned with Wikipedia, and another that diverges sharply. Among the dissimilar articles, we observe a systematic rightward shift in the political bias of frequently cited news media sources, concentrated primarily in entries related to history and religion, and literature and art. More broadly, the findings indicate that AI-generated encyclopedic content departs from established editorial norms, favoring narrative expansion over citation-based verification, raising questions about transparency, provenance, and the governance of knowledge in automated information systems.", "date": "2025-10-30", "categories": []}
{"id": "2605.16289", "title": "Linguistic Uncertainty and Reply Engagement on X: A Cross-Domain Replication of the Uncertainty-Reply Asymmetry", "abstract": "Linguistic uncertainty is common in social media, but its relationship with engagement remains unclear across languages and topics. Using 2,258 English-language posts on Federal Reserve policy, inflation, and electoral politics collected over three days in April 2026, we test whether the Uncertainty-Reply Asymmetry observed in prior Arabic-language research replicates in a broader context. Posts are classified using a lexicon-based uncertainty framework, with approximately one-third identified as uncertain. Uncertain posts receive 82% more replies on average than certain posts, with smaller increases in reposts and likes, replicating the asymmetric engagement pattern observed in prior work. Regression results confirm a positive and statistically significant association between uncertainty and replies (\\b{eta} = 0.126, p = 0.011), equivalent to ~13% higher expected reply engagement, while total engagement shows a positive but weaker association. These findings suggest that linguistic uncertainty systematically increases conversational engagement and may reflect a general interactional mechanism across languages and domains.", "date": "2026-04-13", "categories": []}
{"id": "2605.17010", "title": "Algorithmic Cultivation: How Social Media Feeds Shape User Language", "abstract": "Algorithmic feeds have become primary environments for encountering information online, yet while they shape what people see, less is known about how sustained feed exposure shapes how people write. Drawing on Cultivation Theory, we examine whether algorithmic feeds function as online environments that leave measurable traces in users' language. We leverage a large-scale longitudinal dataset of 235M posts by 4M users on Bluesky, and conduct a quasi-experimental study matching an initial pool of 368,513 users exposed to one of three feeds -- News, Science, and Blacksky -- with a pool of 2,001,915 active control users who did not engage with any of these feeds. We examine linguistic evolution across three dimensions: lexico-semantics, psycholinguistics, and topics. We find that users exposed to these feeds show significantly greater stylistic accommodation, semantic alignment, and register formalization than matched controls. These effects vary markedly by feed identity -- Blacksky produces the deepest psycholinguistic restructuring, with significant shifts in cognitive processing, affective expression, and pronoun use, while News and Science effects are largely confined to register and topical focus. Regression models reveal that reposting is the most consistent predictor of linguistic convergence across all feeds, whereas posting and bookmarking show feed-dependent effects, with effects differing more than fourfold across feeds. Our work extends Cultivation Theory beyond belief formation to linguistic behavior, demonstrating that feeds function as persistent linguistic environments that gradually shape what and how users write online. Our work has implications for studying algorithmic influence, online identity formation, and the design and governance of feed-based platforms that mediate online interactions.", "date": "2026-05-16", "categories": []}
{"id": "2605.17141", "title": "Dynamics of collective creativity in AI art competitions", "abstract": "Creativity is a fundamental aspect of how culture evolves, yet the mechanisms by which groups produce novelty are notoriously difficult to infer from the historical record. Iterated learning experiments have shown that cultural transmission reliably distorts artifacts toward the inductive biases of learners, but most of this work uses linear chains between human participants, leaving open how these dynamics play out in the networked, human-AI systems that increasingly shape cultural production. In this study, we leverage one such system, Artbreeder, which hosts daily \"remix parties\" where users iteratively build on each other's work from a single seed image, producing branching lineages of human-AI co-created images. We analyze a dataset of 130,882 images from 368 remix parties over 13 months and find that images become simpler and converge toward common thematic \"attractors\" (e.g., steampunk scenes, alien architecture). We also find that while more novel \"parent\" images produce more novel and complex \"children\" that attract more likes, users paradoxically prefer to remix images that are less novel and complex. Finally, larger remix parties produce more novelty at the cost of lower complexity.", "date": "2026-05-16", "categories": []}
{"id": "2605.18105", "title": "How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World", "abstract": "Landslides often hit newsstands due to their destructive and potentially fatal effects. News are a valuable source of information for creating or enriching disaster databases and for expediting media-based studies of the dynamics of media attention. To accomplish that, news datasets must be filtered, geolocated and validated. This paper focuses on how landslides around the world are reported in German newspapers. We analyse almost 60k news articles about 5.5k news events in a 25-year period, compare it with external measures of countries' susceptibility to landslides and provide insights, e.g.~the overreporting of Southern and Western Europe, to foment further studies on inequalities in media attention to international disasters.", "date": "2026-05-18", "categories": []}
{"id": "2412.02271", "title": "The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias", "abstract": "We present MediaSpin, a large-scale language resource capturing how major news outlets modify headlines after publication, and MediaSpin-in-the-Wild, a complementary dataset linking these revised headlines to their downstream engagement on social media. The increasing editability of online news headlines offers new opportunities to study linguistic framing and bias through the lens of editorial revisions. The dataset contains 78,910 headline pairs annotated for 13 types of media bias, grounded in established media-bias taxonomies, covering both subjective (e.g., sensationalism, spin) and objective (e.g., omission, slant) forms, with annotation conducted through a human-supervised large-language-model pipeline with expert validation and quality control. We describe the annotation schema and demonstrate three downstream applications: (1) cross-national analysis of how country references are added or removed during editing, (2) transformer-based bias classification at both binary and fine-grained levels, and (3) behavioral analysis of biased headlines on X (Twitter) using 180,786 news-related tweets from 819 consenting users. The results reveal regional asymmetries in representational framing, measurable linguistic markers, and consistently higher engagement with biased content. MediaSpin and MediaSpin-in-the-Wild together provide a reproducible benchmark for bias detection and the study of editorial and behavioral dynamics in contemporary media ecosystems.", "date": "2024-12-03", "categories": []}
{"id": "2605.15886", "title": "Linked Multi-Model Data on Russian Domestic and Foreign Policy Speeches", "abstract": "This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in the availability of social text- and image-based data for authoritarian politics contexts. The dataset comprises two large corpora of official speeches delivered by senior actors within the Kremlin and the Russian Ministry of Foreign Affairs over multiple decades. For each speech, we provide Russian- and English-language texts, associated images and captions where available, and harmonized metadata including (e.g.) dates, speakers, (geo)locations, and official government content tags. Unique identifiers link images to speeches and align Russian and English versions of the same communication texts. We further augment these linked datasets with validated topical annotations for both speech texts and speech images, which are generated via transformer-based multimodal topic modeling and refined by a Russian politics expert. The resulting data resources support multimodal, multilingual, temporal, and/or spatial analyses of (authoritarian) political communication and offer a valuable testbed for social science research and large language model (LLM) applications in political domains.", "date": "2026-05-15", "categories": []}
{"id": "2510.07060", "title": "Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations", "abstract": "Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are trusted news sources, viewers are particularly susceptible to the information they report. The Sinclair Broadcast group is a broadcasting company that has acquired many local news stations in the last decade. We investigate the effects of local news stations being acquired by Sinclair: how does coverage change? We use computational methods to investigate changes in internet content put out by local news stations before and after being acquired by Sinclair and in comparison to national news outlets. We find that there is clear evidence that local news stations report more frequently on national news at the expense of local topics, and that their coverage of polarizing national topics increases.", "date": "2025-10-08", "categories": []}
{"id": "2605.13861", "title": "Spectral Analysis of Fake News Propagation", "abstract": "The propagation structure of fake news has been shown to be an important cue for detecting it; yet, existing propagation-based fake news detection methods have mainly relied on ad hoc topological features, and a unified view of cascade patterns is still lacking. To address this, we study news propagation from a spectral view by connecting graph spectra to propagation-related structural properties through rigorous spectral bounds. In particular, we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation. We then use these spectral bounds for downstream classification and design a discrete structural optimization framework to interpret learned propagation patterns. For efficient optimization, we rely on a first-order perturbation approximation and consider both score-guided and bound-guided objectives. Experiments on real-world data reveal meaningful spectral differences between fake and real news, competitive classification performance from spectral bounds, and interpretable evolution trajectories from structural optimization. The findings demonstrate the value of spectral analysis for understanding and modeling news propagation.", "date": "2026-04-18", "categories": []}
{"id": "2605.14352", "title": "Ideology Prediction of German Political Texts", "abstract": "Elections represent a crucial milestone in a nation's ongoing development. To better understand the political rhetoric from various movements, ranging from left to right, we propose a transformer-based model capable of projecting the political orientation of a text on a continuous left-to-right spectrum, represented by a normalized scalar d between -1 and 1. This approach enables analysts to focus on specific segments of the political landscape, such as conservatives, while excluding liberal and far-right movements. Such a task can only be achieved with multiclass classifiers, provided that the desired orientation is incorporated within one of their predefined classes. To determine the most suitable foundation model among 13 candidate transformers for this task, we constructed four distinct corpora. One corpus comprised annotated plenary notes from the German Bundestag, while another was based on an official online decision-making tool, Wahl-O-Mat. The third corpus consisted of articles from 33 newspapers, each identified by its political orientation, and the fourth included 535,200 tweets from 597 members of the 20th and 21st German Bundestag. To mitigate overfitting, we used two distinct corpora for training and two for testing, respectively. For in-domain performance, DeBERTa-large achieved the highest F1 score F1=0.844 as well as for the X (Twitter) out-of-domain test ACC=0.864. Regarding the newspaper out-of-domain test, Gemma2-2B excelled (MAE = 0.172). This study demonstrates that transformer models can recognize political framing in German news at the level of public opinion polls. Our findings suggest that both the model architecture and the availability of domain-specific training data can be as influential as model size for estimating political bias. We discuss methodological limitations and outline directions for improving the robustness of bias measurement.", "date": "2026-05-14", "categories": []}
{"id": "2604.15333", "title": "Technically Love: The Evolution of Human-AI Romance Discourse on Reddit", "abstract": "Human-AI romantic relationships are increasingly common, yet little is understood about how public discourse around them emerges and shifts over time. Prior research has examined user experiences and ethical concerns, but lacks longitudinal analyses of user-initiated public discussions. We address this gap by analyzing a high-precision dataset of 3,383 self-disclosed romantic companion AI posts from Reddit (2017-2025), using topic modeling and temporal statistical analysis to identify dominant themes and their evolution over time. We find significant topic drift, with discussions moving away from positive intimate relationships toward platform governance, technical issues, and real-world consequences. These shifts highlight a transition in how human-AI romance is framed-moving from private experiences to technical mediation and regulation-with implications for the design and governance of companion AI systems.", "date": "2026-03-09", "categories": []}
{"id": "2604.21496", "title": "How English Print Media Frames Human-Elephant Conflicts in India", "abstract": "Human-elephant conflict (HEC) is rising across India as habitat loss and expanding human settlements force elephants into closer contact with people. While the ecological drivers of conflict are well-studied, how the news media portrays them remains largely unexplored. This work presents the first large-scale computational analysis of media framing of HEC in India, examining 1,968 full-length news articles consisting of 28,986 sentences, from a major English-language outlet published between January 2022 and September 2025. Using a multi-model sentiment framework that combines long-context transformers, large language models, and a domain-specific Negative Elephant Portrayal Lexicon, we quantify sentiment, extract rationale sentences, and identify linguistic patterns that contribute to negative portrayals of elephants. Our findings reveal a dominance of fear-inducing and aggression-related language. Since the media framing can shape public attitudes toward wildlife and conservation policy, such narratives risk reinforcing public hostility and undermining coexistence efforts. By providing a transparent, scalable methodology and releasing all resources through an anonymized repository, this study highlights how Web-scale text analysis can support responsible wildlife reporting and promote socially beneficial media practices.", "date": "2026-04-23", "categories": []}
{"id": "2605.12510", "title": "WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection", "abstract": "We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years. The dataset was constructed through a rigorous, carefully designed pipeline that integrates keyword-based data collection, semantic deduplication to remove near-duplicate content, and a multi-stage annotation protocol conducted by medical specialists. This process produced a high-quality gold-standard corpus, characterized by substantial inter-annotator agreement and strong reliability for downstream analysis. Additionally, we provide a detailed characterization of WhatsApp misinformation, revealing distinctive linguistic, structural, lexical, temporal, and group-level patterns, as well as a meaningful layer of ambiguous cases that reflect the complexity of health discourse in private messaging. We also benchmark classical models, fine-tuned Small Language Models, and zero- or few-shot Large Language Models under realistic data-scarcity constraints, demonstrating that strong embeddings and LLM approaches perform competitively, while domain alignment and data availability remain critical factors. This study provides a rare, high-quality resource to support misinformation research and computational modeling in encrypted communication environments.", "date": "2026-03-25", "categories": []}
{"id": "2605.12511", "title": "Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users", "abstract": "Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short span, yet, amidst the stream of genuine information, fake news and rumors continue to spread. The need for effective detection models is becoming increasingly critical. Past user behavior and user engagement on a post are strong signals that SOTA approaches leverage for fake news detection and other post classification tasks. However, these approaches lean too heavily on knowing this past behavior, and thus suffer from a cold user problem, or users that are new or have minimal footprint on the platform. In this paper, we make three core contributions. We first establish the value of user behavior, both content and user-user interactions, in the task of fake news and rumor detection. We then establish the extensive prevalence of cold users in the real-world datasets, and show the need for newer algorithms considering cold users. We next propose a novel socially-aware context representation scheme - USER EVIDENCE NETWORK (UEN) - to detect the spread of misinformation and unverified information while efficiently navigating this cold user challenge. We introduce techniques that approximate missing or absent behavior data of a new user from existing users' interactions. By carefully addressing the cold user challenge, our work provides robust approaches targeting fake news and rumor detection for real-world platforms.", "date": "2026-03-30", "categories": []}
{"id": "2605.13785", "title": "Amplification to Synthesis: A Comparative Analysis of Cognitive Operations Before and After Generative AI", "abstract": "Cognitive operations are a rising concern in the geopolitical sphere, a quiet yet rigorous fight for public perception and decision making. While such operations have been extensively studied in the context of bot-driven amplification, the emergence of generative AI introduces a new set of capabilities that may have fundamentally altered how these operations are designed and executed. The possible evolution of cognitive operation via generative AI puts nation states vulnerable without proper mitigation strategies. To address this, we compared behavioral and linguistic coordination patterns in X (formerly Twitter) datasets from the 2016 and 2024 U.S. presidential elections. Utilizing a combined corpus of over 133,000 posts, we applied post-type distribution, semantic clustering, temporal synchrony analysis, and Jaccard-based lexical overlap measures. Findings suggest that the 2024 corpus exhibits a distinct pattern from 2016. Original content rose from 59% to 93% with retweets virtually disappeared; lexical overlap collapsed from a mean Jaccard score of 0.99 to 0.27, with posts converging on the same subject matter expressed in markedly different words; and temporal coordination shifted from pervasive cross-semantic synchrony to narratively concentrated co-occurrence. Taken together, these patterns point toward an operational logic organized around active content generation and narrative-specific targeting - characteristics consistent with generative AI involvement. These findings offer an empirical baseline for future research investigating generative AI's role in the cognitive operation pipeline, and as a practical reference point for security practitioners developing detection frameworks calibrated to the post-generative AI threat environment.", "date": "2026-05-13", "categories": []}
{"id": "2603.24410", "title": "Real Talk, Virtual Faces: Symbolic-Semantic Discourse Geometry of Virtual and Human Influencer Audiences", "abstract": "Virtual influencers~(VIs) -- digitally constructed social-media personas -- are becoming increasingly visible in online culture, marketing, and identity formation. Yet it remains unclear whether audiences respond to them through the same discourse patterns used for human influencers~(HIs), or whether virtuality produces distinctive modes of reaction. Existing studies often rely on surveys, engagement statistics, or marginal sentiment distributions, which reveal what audiences say but not how affective, topical, and psycholinguistic signals are jointly organised.\n  We introduce a symbolic-semantic framework for analysing audience discourse around virtual and human influencers. The symbolic layer uses Formal Concept Analysis and association rule mining to extract closed co-occurrence structures from sentiment labels, topic tags, and Big Five psycholinguistic cues. The semantic layer renders these formal concepts as natural-language descriptions, embeds them with MiniLM, and compares their geometry across VI and HI audiences.\n  Applied to 69,498 YouTube comments from three matched VI-HI influencer pairs, our analysis shows that HI discourse is organised around a compact, stability-centred pattern in which low neuroticism anchors positive sentiment, whereas VI discourse supports multiple discourse regimes. VI concepts are also more semantically dispersed than HI concepts, while both groups show strong symbolic-semantic alignment between closed-set structure and embedding geometry. Finally, VI discourse contains a distinct artificial-identity region and a higher concentration of negative sentiment in sensitive topics such as mental health, body image, and artificial identity. These findings suggest that virtuality reshapes not only the sentiment of audience reactions, but also the symbolic and semantic organisation of online social discourse.", "date": "2026-03-25", "categories": []}
{"id": "2605.09043", "title": "Phase Transitions in Affective Meaning Divergence: The Hidden Drift Before the Break", "abstract": "One partner says \"Fine\" meaning \"resolution\"; the other hears \"surrender.\" The word is shared; the affective uptake is not. We formalize this as affective meaning divergence (AMD), the total-variation distance between interlocutors' anchor-conditioned affect distributions. Building on speech-act theory, common-ground accumulation, and entropy-regularized game theory, we derive a logit best-response map whose dynamics undergo a saddle-node bifurcation: when $\\beta\\alpha > 4$, a monotone increase in AMD-driven load produces an abrupt, hysteretic collapse of repair coordination. On Conversations Gone Awry (CGA-Wiki; $N = 652$), derailing conversations exhibit critical-slowing-down (CSD) signatures across multiple levels: lexical divergence variance ($p < 0.001$, $d = 0.36$), AMD variance ($p = 0.001$, $d = 0.26$), and dialog-act repair variance ($p = 0.016$, $d = 0.20$), all significant after correction and stronger than toxicity and sentiment baselines. AMD provides a distinct temporal signature, with retrospectively measured variance peaking at the bifurcation point while toxicity variance peaks earlier, and is the only indicator grounded in the theoretical framework. Boundary-condition analysis on CGA-CMV ($N = 1,169$) yields mixed but directionally consistent evidence.", "date": "2026-05-09", "categories": []}
{"id": "2605.11166", "title": "Unpacking the Eye of the Beholder: Social Location, Identity, and the Moving Target of Political Perspectives", "abstract": "Political and social identities structure how people evaluate political information, a finding decades deep in political science and routinely discarded by computational tools that often produce single scores that treat a piece of text, an image, or a video as if it means the same thing to everyone. This paper shows that it does not, and that the difference is consequential. To address this problem, I develop the Perspectivist Visual Political Sentiment (PVPS) classifier, which learns from approximately 82,000 evaluations by 5,575 U.S. adults to predict how audiences defined by political and social identities will evaluate the same image. Unlike standard tools that average systematic disagreement away, PVPS preserves it, returning an evaluative profile that records who agrees, who diverges, and along which identity lines. Applied to several influential studies of visual sentiment, PVPS shows that perceived violence in protest imagery and the emotional mechanisms behind protest image engagement both change substantively once audience identity is taken into account. It follows that what a political image conveys is a moving target, and measuring it requires knowing whom it is moving.", "date": "2026-05-11", "categories": []}
{"id": "2605.11645", "title": "GeomHerd: A Forward-looking Herding Quantification via Ricci Flow Geometry on Agent Interactive Simulations", "abstract": "Herding -- where agents align their behaviors and act collectively -- is a central driver of market fragility and systemic risk. Existing approaches to quantify herding rely on price-correlation statistics, which inherently lag because they only detect coordination after it has already moved realised returns. We propose GeomHerd, a forward-looking geometric framework that bypasses this observability lag by quantifying coordination directly on upstream agent-interaction graphs. To generate these graphs, we treat a heterogeneous LLM-driven multi-agent simulator -- each financial trader instantiated by a persona-conditioned LLM call -- as a forecastable world, and evaluate the geometric pipeline on the Cividino--Sornette continuous-spin agent-based substrate as our headline financial testbed. By tracking the discrete Ollivier--Ricci curvature of these action graphs, GeomHerd captures the structural topology of emerging coordination. Theoretically, we establish a mean-field bridge mapping our graph-theoretic metric to CSAD, the classical macroscopic herding statistic, linking GeomHerd to downstream price-dispersion measurement. Empirically, GeomHerd anticipates herding long before aggregate market baselines: on the continuous-spin substrate, our primary detector fires a median of 272 steps before order-parameter onset; a contagion detector ($\\beta_{-}$) recalls 65% of critical trajectories 318 steps early; and on co-firing trajectories the agent-graph signal precedes price-correlation-graph baselines by 40 steps. As a complementary indicator, the effective vocabulary of agent actions contracts during cascades. The geometric signature transfers out-of-domain to the Vicsek self-driven-particle model, and a curvature-conditioned forecasting head reduces cascade-window log-return MAE over detector-conditioned and price-only baselines.", "date": "2026-05-12", "categories": []}
{"id": "2605.12452", "title": "The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Events", "abstract": "Large Language Models (LLMs) can generate fluent political text at scale, raising concerns about synthetic discourse during crises and social conflict. Existing AI-text detection often focuses on sentence-level cues such as perplexity, burstiness, or token irregularities, but these signals may weaken as generative systems improve. We instead adopt a Computational Social Science perspective and ask whether synthetic political discourse behaves like an observed online population. We construct a paired corpus of 1,789,406 posts across nine crisis events: COVID-19, the Jan. 6 Capitol attack, the 2020 and 2024 U.S. elections, Dobbs/Roe v. Wade, the 2020 BLM protests, U.S. midterms, the Utah shooting, and the U.S.-Iran war. For each event, we compare observed discourse from social platforms with synthetic discourse generated for the same context. We evaluate four dimensions: emotional intensity, structural regularity, lexical-ideological framing, and cross-event dependency, using mean gaps and dispersion evidence. Across events, synthetic discourse is fluent but population-level unrealistic. It is generally more negative and less dispersed in sentiment, structurally more regular, and lexically more abstract than observed discourse. Observed discourse instead shows broader emotional variation, longer-tailed structural distributions, and more context-specific, colloquial lexical markers. These differences are event-dependent: larger for fast-moving, decentralized crises and smaller for formal or institutionally mediated events. We summarize them with a simple event-level measure, the Caricature Gap. Our findings suggest that the main limitation of synthetic political discourse is not grammar or fluency, but reduced population realism. Population-level auditing complements traditional text-detection and provides a CSS framework for evaluating the social realism of generated discourse.", "date": "2026-05-12", "categories": []}
{"id": "2503.18273", "title": "Decoding Islamophobic Discourse: Using LLMs to Identify Tropes and Semi-Coded Hate Speech", "abstract": "In recent years, Islamophobia has gained significant traction across Western societies, fueled by the rise of digital communication networks. This paper performs a large-scale analysis of specialized, semi-coded Islamophobic terms such as (muzrat, pislam, mudslime, mohammedan, muzzies) floated on extremist social platforms, i.e., 4Chan, Gab, Telegram, etc. Many of these terms appear lexically neutral or ambiguous outside of specific contexts, making them difficult for both human moderators and automated systems to reliably identify as hate speech. First, we use Large Language Models (LLMs) to show their ability to understand these terms. Second, Google Perspective API suggests that Islamophobic posts tend to receive higher toxicity scores than other categories of hate speech like Antisemitism. Finally, we use BERT topic modeling approach to extract different topics and Islamophobic discourse on these social platforms. Our findings indicate that LLMs understand these Out-Of-Vocabulary (OOV) slurs; however, further improvements in moderation strategies and algorithmic detection are necessary to address such discourse effectively. Our topic modeling also indicates that Islamophobic text is found across various political, conspiratorial, and far-right movements and is particularly directed against Muslim immigrants. Taken altogether, we performed one of the first studies on Islamophobic semi-coded terms and shed a global light on Islamophobia.", "date": "2025-03-24", "categories": []}
{"id": "2603.05653", "title": "The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok", "abstract": "Adolescents spend an increasing amount of their time in digital environments where their still-developing cognitive capacities leave them unable to recognize or resist commercial persuasion. Article 28(2) of the DSA responds to this vulnerability by prohibiting profiling-based advertising to minors. However, the regulation's narrow definition of \"advertisement\" excludes current advertising practices including influencer paid partnerships and brand promotional content that serve functionally equivalent commercial purposes. We provide the first empirical evidence of how this definitional gap operates in practice through an algorithmic audit of TikTok. Our approach deploys sock-puppet accounts simulating a pair of minor and adult users with matching interest profiles. The content recommended to these users is automatically annotated, enabling systematic statistical analysis. Our findings reveal a stark regulatory paradox. TikTok demonstrates formal compliance with Article 28(2) by shielding minors from profiled formal advertisements, yet both disclosed and undisclosed ads exhibit significant profiling aligned with user interests (5-8 times stronger than for adult formal advertising). The strongest profiling emerges within undisclosed commercial content, where creators/brands fail to label paid partnership/promotional content and the platform neither corrects this omission nor prevents its personalized delivery to minors. These results demonstrate that minors remain exposed to algorithmically targeted commercial content through the same recommendation mechanisms the DSA seeks to constrain. We argue that protecting minors requires expanding the definition of advertisement in EU law to encompass influencer and brand promotional content, and ensuring that any such expansion is accompanied by a corresponding prohibition on profiling-based targeting of minors.", "date": "2026-03-05", "categories": []}
{"id": "2604.25674", "title": "Modeling Human-Like Color Naming Behavior in Context", "abstract": "Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both learning and communicative pressures. The NeLLCom-Lex framework (Zhang et al., 2025) allows neural agents to develop pragmatic color naming behavior and human-like lexicons through supervised learning (SL) from human data and reinforcement learning (RL) in referential games. Despite these successes, the lexicons that emerge diverge systematically from human color categories, producing highly non-convex regions in color space, which contrast with the convexity typical of human categories. To address this, we introduce two factors, upsampling rare color terms during SL and multi-listener RL interactions, and adopt a convexity measure to quantify geometric coherence. We find that upsampling improves lexical diversity and system-level informativeness of the color lexicon, while many-listener setups promote more convex color categories. The combination of moderate upsampling and multiple listeners produces lexicons most similar to human systems.", "date": "2026-04-28", "categories": []}
{"id": "2605.08383", "title": "Change My View? The Dynamics of Persuasion and Polarization in Online Discourse", "abstract": "Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, yet everyday discourse often suggests otherwise. In this study, we use large language models to analyze a corpus of debates on Reddit's r/ChangeMyView, where belief revision is publicly signaled. Large language models were asked, halfway through each discussion, to forecast whether such an acknowledgement would arise; their probabilistic estimates serve as a conversational baseline. Each reply was then coded, through a hybrid machine-assisted procedure, for ten familiar rhetorical strategies -- concession, empathy, logical challenge, credibility appeals, and so forth. Adding these strategic features markedly improves predictive power and yields a consistent pattern: moves that express concession or empathetic alignment substantially increase the prospect of belief change, whereas frontal refutation, credibility attacks, and topic deflection diminish it. The findings indicate that effective public reasoning depends as much on relational framing as on evidential content, and they invite a refinement of normative accounts of rational dialogue.", "date": "2026-05-08", "categories": []}
{"id": "2605.09483", "title": "A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility", "abstract": "In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibility to information disorder. BPL extends Rational Speech Act theory with three cognitively motivated bounds derived from the bounded rationality literature with a) a recursion depth bound (that emphasises working memory limits);b) a prior compression parameter (which is oriented at capturing information bottleneck); and c) an availability sample size (that operationalises importance sampling with saliency-weighted proposals). This allows us to test predictions about misinformation susceptibility, annotator disagreement, and the differential vulnerability to mis-, dis-, and mal-information as defined in the Information Disorder framework. We validate BPL on the LIAR and MultiFC benchmarks showcasing competitive veracity classification and experimental support for the depth-mismatch paradox.", "date": "2026-05-10", "categories": []}
{"id": "2605.10659", "title": "When Can Digital Personas Reliably Approximate Human Survey Findings?", "abstract": "Digital personas powered by Large Language Models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet it remains unclear when they can reliably approximate human survey findings. We answer this question using the LISS panel, constructing personas from respondents' background variables and pre-2023 survey histories, then testing them against the same respondents' held-out post-cutoff answers. Across four persona architectures, three LLMs, and two prediction tasks, we assess performance at the question, respondent, distributional, equity, and clustering levels. Digital personas improve alignment with human response distributions, especially in domains tied to stable attributes and values, but remain limited for individual prediction and fail to recover multivariate respondent structure. Retrieval-augmented architectures provide the clearest gains, but performance depends more on human response structure than on model choice: personas perform best for low-variability questions and common respondent patterns, and worst for subjective, heterogeneous, or rare responses. Our results provide practical guidance on when digital personas could be appropriate for survey research and when human validation remains necessary.", "date": "2026-05-11", "categories": []}
{"id": "2509.03736", "title": "Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation", "abstract": "The impressive capabilities of Large Language Models (LLMs) raise the possibility that synthetic agents can serve as substitutes for real participants in human-subject research. To evaluate this claim, prior research has largely focused on whether LLM-generated survey responses align with those produced by human respondents whom the LLMs are prompted to represent. In contrast, we address a more fundamental question: Do agents maintain empirical consistency; aligning to human behavioral models when examined under different experimental settings? To this end, we develop a study designed to (a) ask a set of questions which reveals an agent's latent profile and (b) examine agent behavioral consistency in a conversational setting with other agents. This design enables us to explore a set of behavioral hypotheses to assess whether an agent's conversational behavior is consistent with what we would expect from its revealed state. Our findings show significant inconsistencies in LLMs across model families and at differing model sizes. Most importantly, we find that, although agents may generate responses matching those of their human counterparts, they fail to be empirically consistent, representing a critical gap in their capabilities to accurately substitute for real participants in human-subject research.", "date": "2025-09-03", "categories": []}
{"id": "2605.07409", "title": "The Proxy Presumption: From Semantic Embeddings to Valid Social Measures", "abstract": "Natural Language Processing is rapidly evolving into a primary instrument for Computational Social Science, with researchers increasingly using embeddings to measure latent constructs such as novelty, creativity, and bias. However, this transition faces a fundamental validity challenge: the ''Proxy Presumption,'' or the reliance on geometric properties (e.g., cosine distance) as direct measures of social concepts. We argue that without explicit validation, unsupervised representations remain entangled mixtures of the target construct ($C$) and confounding attributes ($Z$) like topic, style, and authorship. To bridge the gap between semantic embeddings and valid social measures, we introduce the Construct Validity Protocol (CVP). Drawing on causal representation learning and psychometrics, the CVP offers a rigorous pipeline from conceptualization to quantitative verification. We further propose Counterfactual Neutralization, a novel method using LLMs to reduce confounding in embedding space. By providing a standardized Validity Suite -- including tests for discriminant, incremental, and predictive validity -- this work offers the community a toolkit to transform heuristic proxies into robust, scientifically defensible instruments.", "date": "2026-05-08", "categories": []}
{"id": "2605.07692", "title": "GASim: A Graph-Accelerated Hybrid Framework for Social Simulation", "abstract": "Large-scale social simulators are essential for studying complex social patterns. Prior work explores hybrid methods to scale up simulations, combining large language models (LLM)-based agents with numerical agent-based models (ABM). However, this incurs high latency due to expensive memory retrieval and sequential ABM execution. To address this challenge, we propose GASim, a graph-accelerated hybrid multi-agent framework for large-scale social simulations. For core agents driven by LLM, GASim introduces Graph-Optimized Memory (GOM) to replace intensive LLM-based retrieval pipelines with lightweight propagation over a sparse memory graph. For the majority of ordinary agents, GASim employs Graph Message Passing (GMP), substituting sequential ABM execution with parallel updates by fine-grained feature aggregation and Graph Attention Network. We further introduce Entropy-Driven Grouping (EDG) that coordinates this hybrid partitioning, leveraging information entropy to dynamically identify emergent core agents situated in information-diverse neighborhoods. Extensive experiments show that GASim not only delivers a substantial 9.94-fold end-to-end speedup over the traditional hybrid framework but also consumes less than 20% of baseline tokens, significantly reducing costs while preserving strong alignment with real-world public opinion trends. Our code is available at https://github.com/Jasmine0201/GASim.", "date": "2026-05-08", "categories": []}
{"id": "2509.11444", "title": "CognitiveSky: Scalable Sentiment and Narrative Analysis for Decentralized Social Media", "abstract": "The emergence of decentralized social media platforms presents new opportunities and challenges for real-time analysis of public discourse. This study introduces CognitiveSky, an open-source and scalable framework designed for sentiment, emotion, and narrative analysis on Bluesky, a federated Twitter or X.com alternative. By ingesting data through Bluesky's Application Programming Interface (API), CognitiveSky applies transformer-based models to annotate large-scale user-generated content and produces structured and analyzable outputs. These summaries drive a dynamic dashboard that visualizes evolving patterns in emotion, activity, and conversation topics. Built entirely on free-tier infrastructure, CognitiveSky achieves both low operational cost and high accessibility. While demonstrated here for monitoring mental health discourse, its modular design enables applications across domains such as disinformation detection, crisis response, and civic sentiment analysis. By bridging large language models with decentralized networks, CognitiveSky offers a transparent, extensible tool for computational social science in an era of shifting digital ecosystems.", "date": "2025-09-14", "categories": []}
{"id": "2605.04552", "title": "The Newsworthiness of Brazilian Distress: A Peak Analysis on Time Series of International Media Attention to Disasters in Brazil", "abstract": "Media coverage influences disaster response, yet the drivers of international media attention to local events remain unevenly understood. Brazil offers a compelling case: some of its natural and technological disasters occasionally hit the international headlines. However, systematic analyses of what makes these events be discussed abroad are still missing. Addressing this gap requires representative, validated and country-specific news datasets. This paper presents a peak analysis of 2k news about Brazilian fires and landslides in German newspapers from 2000 to 2024. Using time series segmentation to detect news event peaks, we examine the extent to which they can be temporally aligned with observations in national and global disaster databases.", "date": "2026-05-06", "categories": []}
{"id": "2605.04926", "title": "Unintended Negative Impacts of Promotional Language in Patent Evaluation", "abstract": "Promotional language has been increasingly used to aid the communication of innovative ideas in science. Yet, less is known about its role in the context of technological innovation. Here, we use a validated and domain-diagnosed lexicon of 135 promotional words to study the association between promotional language and patent evaluation outcomes among 2.7 million USPTO patent applications. Our large-scale study reveals three unexpected findings. First, in contrast to scientific evaluation, we find that a higher frequency of promotional words is negatively associated with the probability of an application being (i) granted a patent, (ii) transferred ownership, and (iii) successfully appealed. This promotional penalty holds even after accounting for a range of confounding factors and is largely robust across different technological areas. Among matched samples, the difference in the success rate between the lowest and highest promotional density quintile is 5.5, 5.9, and 5.3 percentage points for patentability, transferability, and rejection reversal. Second, contrary to institutional skepticism, we show that promotional language is not a mask of weak technology, but objectively reflects the degree of combinatorial novelty and future citation impact. Third, digging into the mechanisms, we find that the tolerance to promotional framing is strongly moderated by human factors, with men and experienced examiners showing a higher acceptance of promotional narratives than women and novice examiners. By revealing an emerging paradox in the patent system, our study offers theoretical and practical implications for improving patent evaluation through more objective scrutiny of linguistic patterns in patent filings.", "date": "2026-05-06", "categories": []}
{"id": "2508.15524", "title": "The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech", "abstract": "We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from Knesset speeches (1993-2023), Facebook posts (2018-2021), and leading news outlets, of which 1,812 instances (17.4\\%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline combining finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F$_1$ of 0.74 for binary PDD detection and a macro-F$_1$ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male than female politicians, and stronger tendencies among right-leaning actors - with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for understanding democratic discourse.", "date": "2025-08-21", "categories": []}
{"id": "2605.02358", "title": "Predicting Post Virality with Temporal Cross-Attention over Trend Signals", "abstract": "Current models for predicting social media virality rely heavily on static textual and structural features, effectively ignoring the highly dynamic nature of trend signals. We study whether real-world attention signals can improve the prediction of social-media virality beyond what post text alone reveals. We introduce ViralityNet, an architecture that predicts Reddit post virality by fusing internal platform representations with exogenous temporal signals derived from Wikipedia pageview spikes. We frame virality as a binary classification task that accounts for differences in subreddit scale, labeling posts as viral if they exceed the 90th percentile of per-subreddit engagement and a minimum absolute score threshold.\n  ViralityNet combines four post-level streams: title embeddings, body embeddings, structural metadata, and learned subreddit embeddings with a cross-attention block that queries a daily sliding-window trends matrix encoding the top-512 Wikipedia spike terms from the preceding seven days. Empirical results suggest that incorporating external attention signals yields consistent gains, outperforming text-only baselines by +0.015 AUC-PR and achieving an overall AUC-ROC of 0.836. Overall, we provide evidence that incorporating external attention signals yields measurable improvements over text-only baselines, highlighting the importance of real-world dynamics in shaping online virality.", "date": "2026-05-04", "categories": []}
{"id": "2605.02457", "title": "Leveraging Argument Structure to Predict Content Hatefulness", "abstract": "Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and hateful content online. In different contexts, one aspect of the problem may prevail, but overall, this is a broad problem that requires comprehensive solutions. While each dimension of the problem (hate speech, disinformation, misinformation, etc.) requires in-depth analysis, in this paper, we look into the possibility of argument structure to provide relevant information to link these different areas of the problem. In particular, we focus on the WSF-ARG+ dataset, which consists of white supremacy forum messages annotated in terms of argument structure (premises and conclusion). There, we leverage the checkworthiness and hatefulness annotations of the argument components to obtain insights into the hatefulness of the whole message. Our results show promising insights (up to 96% F1), indicating the possibility of extending this direction in the future to tackle hateful content identification and information disorder countering.", "date": "2026-05-04", "categories": []}
{"id": "2604.19699", "title": "Epistemic orientation in parliamentary discourse is associated with deliberative democracy", "abstract": "The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from evidence-based reasoning grounded in verifiable information to intuition-based reasoning rooted in beliefs and subjective interpretation. We introduce a scalable approach to measure epistemic orientation using the Evidence--Minus--Intuition (EMI) score, derived from large language model (LLM) ratings and embedding-based semantic similarity. Applying this approach to 15 million parliamentary speech segments spanning 1946 to 2025 across seven countries, we examine temporal patterns in discourse and its association with deliberative democracy and governance. We find that EMI is positively associated with deliberative democracy within countries over time, with consistent relationships in both contemporaneous and lagged analyses. EMI is also positively associated with the transparency and predictable implementation of laws as a dimension of governance. These findings suggest that the epistemic nature of political discourse is crucial for both the quality of democracy and governance.", "date": "2026-04-21", "categories": []}
{"id": "2605.02629", "title": "Mapping Discourse Reframing: A Multi-Layer Network Approach to Italian HPV Vaccine Discourse on X (2010-2024)", "abstract": "Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on conservative network constructions that erase initially sparse but salient signals. This paper proposes a novel multi-layer framework that captures low-frequency signals of emerging information disorder allowing for locating where online discourse is reframed and amplified over time. The use case is 14 years of Italian discourse on X regarding the Human Papillomavirus (HPV) vaccine across three pivotal epochs (2010-2024). Utilizing hashtag co-occurrence networks, we introduce a dual-layer approach. We first identify robust core discourse coalitions through conservative community detection, revealing a stable prevention-oriented backbone contrasted with increasingly separable skepticism coalitions. We then introduce a coverage layer and project fringe hashtags into core coalitions based on weighted connectivity. Using a manually labelled set of skeptical and conspiratorial seed tweets, we demonstrate that this core-coverage projection significantly improves the recovery of long-tail, problematic hashtags while preserving an interpretable coalition structure. Our findings characterize the structural maturation of polarized narratives and provide a methodology for mapping how discourse is reframed and amplified by information disorder over time.", "date": "2026-05-04", "categories": []}
{"id": "2605.00197", "title": "The $\\textit{Silicon Society}$ Cookbook: Design Space of LLM-based Social Simulations", "abstract": "Studies attempting to simulate human behavior with $\\textit{Silicon Societies}$ grow in numbers while LLM-only social networks have started appearing outside of controlled settings. However, the design space of these networks remains under-studied, which contributes to a gap in validating model realism. To enable future works to make more informed design decisions, we perform a systematic analysis of the consequences and interactions of key design choices in simulated social networks, including the choice of base model used to model individual agents, and how they are connected to each other. Using surveys as a proxy for agent opinions, our findings suggest that the geometry of the design space is non-trivial, with some parameters behaving in additive ways while others display more complex interactions. In particular, the choice of the base LLM is the most important variable impacting the simulation outcomes.", "date": "2026-04-30", "categories": []}
{"id": "2605.00513", "title": "ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks", "abstract": "Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and content moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.", "date": "2026-05-01", "categories": []}
{"id": "2604.26965", "title": "The Impact of AI-Generated Text on the Internet", "abstract": "The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative developments (sometimes subsumed under the Dead Internet Theory). What has hindered answering these questions is that it has not been understood just how much of the internet is actually AI-generated or AI-edited. To this end, we construct a representative sample of websites published on the internet between 2022 and 2025 using the Internet Archive, and apply a state-of-the-art AI text detector on them. We find that by mid-2025, roughly 35% of newly published websites were classified as AI-generated or AI-assisted, up from zero before ChatGPT's launch in late 2022. We also find statistically significant evidence for some of the identified hypotheses; for example, that increases in AI-generated text on the internet correlate negatively with semantic diversity and positively with the prevalence of positive sentiment. We do not, however, find statistically significant evidence supporting the hypothesis that an increased rate of AI-generated text on the internet decreases factual accuracy or stylistic diversity. Notably, this diverges from public perception, which we measure in a user study, where the majority of US adults turned out to believe in all four of the above-mentioned hypotheses. Individuals who do not use AI or use it infrequently tend to believe in these negative impacts more than those who use it frequently; similarly, individuals who hold negative views of AI tend to believe in these hypotheses more than those with favorable views of the technology.", "date": "2026-04-14", "categories": []}
{"id": "2604.27624", "title": "Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior", "abstract": "Large Language Models (LLMs) can strongly shape social discourse, yet datasets investigating how LLM outputs vary across controlled social and contextual prompting remain sparse. Cognitive Digital Shadows (CDS) is a 190,000-record synthetic corpus supporting analyses of LLM-generated discourse. Each CDS record is generated by one of 19 LLMs, prompted to shadow either a human persona or an AI-assistant role. CDS contains LLM responses on 4 controversial societal topics: vaccines/healthcare, social media disinformation, the gender gap in science, and STEM stereotypes. Persona-conditioned records encode 17 sociodemographic and psychological attributes, providing data linking LLMs' prompts, language, stances and reasoning. Texts are validated for topic anchoring and can support emotional analyses via interpretable NLP (e.g. textual forma mentis networks). CDS is enriched by a pooling platform with user-friendly dashboards, enabling easy, interactive group-level comparisons of emotional and semantic framing across personas, topics and models. The CDS prompting framework supports future audits of LLMs' bias, social sensitivity and alignment.", "date": "2026-04-30", "categories": []}
{"id": "2604.27633", "title": "Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor", "abstract": "Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier models on the political left. A parallel literature shows that LLMs are sycophantic: they adapt their answers to the views, identities, and expectations of the user. We show that these findings are linked: standard political-bias audits partly capture sycophantic accommodation to the inferred auditor. We employ a factorial experiment across three major audit instruments--the Political Compass Test, the Pew Political Typology, and 1,540 partisan-benchmarked Pew American Trends Panel items--administered to six frontier LLMs while varying only the asker's stated identity (N = 30,990 responses). At baseline, all six models lean left. When the asker identifies as a conservative Republican, responses shift sharply: the share of items closer to Democrats falls by 28-62 percentage points, and all six models move right of center. A mirror-image progressive-Democrat cue produces little change; rightward accommodation is 8.0$\\times$ larger than leftward. When asked who the default asker is, models identify an auditor, researcher, or academic; when asked what answer that asker expects, they select the Democrat-coded option 75% of the time, nearly the rate under an explicit progressive cue. These patterns are inconsistent with a purely fixed model ideology and indicate that single-prompt audits capture an interaction between model and inferred interlocutor. Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile that must be mapped across realistic interlocutors.", "date": "2026-04-30", "categories": []}
{"id": "2604.27673", "title": "The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text", "abstract": "We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents\"), verbs (``Events\"), and objects (``Targets\") from texts. Grounded in cognitive network science and artificial intelligence, TEA Nets are implemented as an open-source Python library. We test TEA Nets in three case studies, demonstrating the framework's ability to perform interpretable emotion detection, semantic frame analyses, and linguistic inquiries across conspiracy texts and textual responses generated by LLMs. In the LOCO conspiracy corpus, TEA Nets revealed that highly conspiratorial narratives (4,227 texts) linked personal pronouns (``I\", ``you\", ``we\") with the same actions twice as frequently as low-similarity conspiracy narratives. High-conspiracy narratives connected person-focused elements (``you\", ``people\") through actions eliciting anger above the random baseline ($z = 2.63, p < .05$), a trend absent in low-similarity conspiracy narratives, which emphasized scientific actors (``researcher\", ``scientist\"). In the HOPE and CounseLLMe datasets of 212 (human) and 200 (LLM-based) psychotherapy transcripts, respectively, TEA Nets highlighted emotional differences. When expressing feelings, Claude 3 Haiku, GPT-3.5, and humans used sad words with higher frequency than random expectations but Haiku expressed sadness with lower emotional intensity than humans ($U = 1243.5, p = .036$). We discuss these differences in the context of psychotherapy training on LLM-simulated patients. Our results show that Target-Event-Agent Networks can extract relevant emotional, syntactic, and semantic insights from narratives, opening new avenues for text analysis with cognitive network science.", "date": "2026-04-30", "categories": []}
{"id": "2505.13302", "title": "Images Amplify Misinformation Sharing in Vision-Language Models", "abstract": "As language and vision-language models (VLMs) become central to information access and online interaction, concerns grow about their potential to amplify misinformation. Human studies show that images boost the perceived credibility and shareability of information, raising the question of whether VLMs exhibit the same vulnerability. We present the first study examining how images influence VLMs' propensity to reshare news content, how this effect varies across model families, and how persona conditioning and content attributes modulate such behavior. We develop a jailbreaking-inspired prompting strategy that bypasses VLMs' default refusals to engage with controversial news, allowing them to generate resharing decisions across diverse topics and elicited traits, including antisocial ones. We evaluate four state-of-the-art VLMs on a novel multimodal dataset of fact-checked political news from PolitiFact, paired with images and ground-truth veracity labels. Our experiments show that image presence increases resharing rates by 14.5% for false news and 5.3% for true news. Persona conditioning further modulates this effect: Dark Triad traits amplify resharing of false news, whereas Republican-aligned profiles reduce sensitivity to veracity. Among the tested models, Claude-3-Haiku demonstrates the greatest robustness to visual misinformation. These findings reveal that VLMs replicate human-like biases in response to images, underscoring emerging risks for multimodal AI systems. They point to the need for evaluation frameworks and mitigation strategies that account for visual influence and persona-driven variability, particularly in sociotechnical settings where AI systems shape public discourse and information sharing.", "date": "2025-05-19", "categories": []}
{"id": "2510.18774", "title": "AI use in American newspapers is widespread, uneven, and rarely disclosed", "abstract": "AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K articles from online editions of 1.5K American newspapers published in the summer of 2025. Using Pangram, a state-of-the-art AI detector, we discover that approximately 9% of newly-published articles are either partially or fully AI-generated. This AI use is unevenly distributed, appearing more frequently in smaller, local outlets, in specific topics such as weather and technology, and within certain ownership groups. We also analyze 45K opinion pieces from Washington Post, New York Times, and Wall Street Journal, finding that they are 6.4 times more likely to contain AI-generated content than news articles from the same publications, with many AI-flagged op-eds authored by prominent public figures. Despite this prevalence, we find that AI use is rarely disclosed: a manual audit of 100 AI-flagged articles found only five disclosures of AI use. Overall, our audit highlights the immediate need for greater transparency and updated editorial standards regarding the use of AI in journalism to maintain public trust.", "date": "2025-10-21", "categories": []}
{"id": "2601.06316", "title": "Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence", "abstract": "Warmth (W) (often further broken down intoTrust (T) and Sociability (S)) and Competence (C) are central dimensions along which people evaluate individuals and social groups (Fiske, 2018). While these constructs are well established in social psychology, they are only starting to get attention in NLP research through word-level lexicons, which do not fully capture their contextual expression in larger text units and discourse. In this work, we introduce Warmth and Competence Sentences (W&C-Sent), the first sentence-level dataset annotated for warmth and competence. The dataset includes over 1,600 English sentence--target pairs annotated along three dimensions: trust and sociability (components of warmth), and competence. The sentences in W&C-Sent are social media posts that express attitudes and opinions about specific individuals or social groups (the targets of our annotations). We describe the data collection, annotation, and quality-control procedures in detail, and evaluate a range of large language models (LLMs) on their ability to identify trust, sociability, and competence in text. W&C-Sent provides a new resource for analyzing warmth and competence in language and supports future research at the intersection of NLP and computational social science.", "date": "2026-01-09", "categories": []}
{"id": "2604.22143", "title": "Recognition Without Authorization: LLMs and the Moral Order of Online Advice", "abstract": "Large language models are increasingly used to mediate everyday interpersonal dilemmas, yet how their advisory defaults interact with the concentrated moral orders of specific communities remains poorly understood. This article compares four assistant-style LLMs with community-endorsed advice on 11,565 posts from r/relationship_advice, using the subreddit as a concentrated, vote-ratified moral formation whose prescriptive clarity makes divergence measurable. Across models, LLMs identify many of the same dynamics as human commenters, but are markedly less likely to convert that recognition into directive authorization for action. The gap is sharpest where community consensus is strongest: on high-consensus posts involving abuse or safety threats, models recommend exit at roughly half the human rate while maintaining elevated levels of hedging, validation, and therapeutic framing.\n  The article describes this pattern as recognition without authorization: the capacity to register harm while withholding socially ratified permission for consequential action. This divergence is not incidental but structural: a portable advisory style that remains validating, risk-averse, and weakly directive across contexts. Safety alignment is one plausible contributor to this pattern, alongside training-data averaging and broader assistant design. The article argues that model divergence can be reframed from a technical error to a way of seeing what standardized assistant norms flatten when they encounter situated moral worlds.", "date": "2026-04-24", "categories": []}
{"id": "2601.05563", "title": "What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews", "abstract": "Even when factually correct, social-media news previews (image-headline pairs) can induce interpretation drift: by selectively omitting crucial context, they lead readers to form judgments that diverge from what the full article supports. This covert harm is subtler than explicit misinformation, yet remains underexplored. To address this gap, we develop a multi-stage pipeline that simulates preview-based and context-based understanding, enabling construction of the MM-Misleading benchmark. Using MM-Misleading, we systematically evaluate open-source LVLMs and uncover pronounced blind spots in omission-based misleadingness detection. We further propose OMGuard, which combines (1) Interpretation-Aware Fine-Tuning for misleadingness detection and (2) Rationale-Guided Misleading Content Correction, where explicit rationales guide headline rewriting to reduce misleading impressions. Experiments show that OMGuard lifts an 8B model's detection accuracy to the level of a 235B LVLM while delivering markedly stronger end-to-end correction. Further analysis shows that misleadingness usually arises from local narrative shifts, such as missing background, instead of global frame changes, and identifies image-driven cases where text-only correction fails, underscoring the need for visual interventions.", "date": "2026-01-09", "categories": []}
{"id": "2604.17628", "title": "Does Welsh media need a review? Detecting bias in Nation.Cymru's political reporting", "abstract": "Wales' political landscape has been marked by growing accusations of bias in Welsh media. This paper takes the first computational step toward testing those claims by examining Nation.Cymru, a prominent Welsh political news outlet. I use a two-stage natural language processing (NLP) pipeline: (1) a robustly optimized BERT approach (RoBERTa) bias detector for efficient bias discovery and (2) a large language model (LLM) for target-attributed sentiment classification of bias labels from (1). A primary analysis of 15,583 party mentions across 2022-2026 news articles finds that Reform UK attracts biased framing at twice the rate of Plaid Cymru and over three times as negative in mean sentiment (p<0.001). A secondary analysis across four parties across both news and opinion articles shows that Plaid Cymru is the outlier, receiving markedly more favourable framing than any other party. These findings provide evidence of measurable differential framing in a single Welsh political media outlet, supporting calls for a broader review of Welsh media coverage. Furthermore, the two-stage pipeline offers a low-cost, replicable framework for extending this analysis to other Welsh outlets, as well as media ecosystems outside of Wales.", "date": "2026-04-19", "categories": []}
{"id": "2604.19791", "title": "Stabilising Generative Models of Attitude Change", "abstract": "Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories. These accounts often function as mechanism sketches: rich in conceptual detail, yet lacking the technical specifications and operational constraints required to run as executable systems. We present a generative actor-based modelling workflow for \"rendering\" these sketches as runnable actor - environment simulations using the Concordia simulation library. In Concordia, actors operate by predictive pattern completion: an operation on natural language strings that generates a suffix which describes the actor's intended action from a prefix containing memories of their past and observations of the present. We render the theories of cognitive dissonance (Festinger 1957), self-consistency (Aronson 1969), and self-perception (Bem 1972) as distinct decision logics that populate and process the prefix through theory-specific sequences of reasoning steps. We evaluate these implementations across classic psychological experiments. Our implementations generate behavioural patterns consistent with known results from the original empirical literature. However, we find that achieving stable reproduction requires resolving the inherent underdetermination of the verbal accounts and the conflicts between modern linguistic priors and historical experimental assumptions. We document how this manual process of iterative model \"stabilisation\" surfaces specific operational and socio-ecological dependencies that were largely undocumented in the original verbal accounts. Ultimately, we argue that the manual stabilisation process itself should be regarded as a core part of the methodology functioning to clarify situational and representational commitments needed to generate characteristic effects.", "date": "2026-04-02", "categories": []}
{"id": "2604.21202", "title": "Participation and Representation in Local Government Speech", "abstract": "Local government meetings are the most common formal channel through which residents speak directly with elected officials, contest policies, and shape local agendas. However, data constraints typically limit the empirical study of these meetings to agendas, single cities, or short time horizons. We collect and transcribe a massive new dataset of city council meetings from 115 California cities over the last decade, using advanced transcription and diarization techniques to analyze the speech content of the meetings themselves. We document two sets of descriptive findings: First, city council meetings are frequent, long, and vary modestly across towns and time in topical content. Second, public participants are substantially older, whiter, more male, more liberal, and more likely to own homes than the registered voter population, and public participation surges when topics related to land use and zoning are included in meeting agendas. Given this skew, we examine the main policy lever municipalities have to shift participation patterns: meeting access costs. Exploiting pandemic-era variation in remote access, we show that eliminating remote options reduces the number of speakers, but does not clearly change the composition of speakers. Collectively, these results provide the most comprehensive empirical portrait to date of who participates in local democracy, what draws them in, and how institutional design choices shape both the volume and composition of public input.", "date": "2026-04-23", "categories": []}
{"id": "2604.21767", "title": "Misinformation Span Detection in Videos via Audio Transcripts", "abstract": "Online misinformation is one of the most challenging issues lately, yielding severe consequences, including political polarization, attacks on democracy, and public health risks. Misinformation manifests in any platform with a large user base, including online social networks and messaging apps. It permeates all media and content forms, including images, text, audio, and video. Distinctly, video-based misinformation represents a multifaceted challenge for fact-checkers, given the ease with which individuals can record and upload videos on various video-sharing platforms. Previous research efforts investigated detecting video-based misinformation, focusing on whether a video shares misinformation or not on a video level. While this approach is useful, it only provides a limited and non-easily interpretable view of the problem given that it does not provide an additional context of when misinformation occurs within videos and what content (i.e., claims) are responsible for the video's misinformation nature.\n  In this work, we attempt to bridge this research gap by creating two novel datasets that allow us to explore misinformation detection on videos via audio transcripts, focusing on identifying the span of videos that are responsible for the video's misinformation claim (misinformation span detection). We present two new datasets for this task. We transcribe each video's audio to text, identifying the video segment in which the misinformation claims appears, resulting in two datasets of more than 500 videos with over 2,400 segments containing annotated fact-checked claims. Then, we employ classifiers built with state-of-the-art language models, and our results show that we can identify in which part of a video there is misinformation with an F1 score of 0.68. We make publicly available our annotated datasets. We also release all transcripts, audio and videos.", "date": "2026-04-23", "categories": []}
{"id": "2604.21897", "title": "Mapping the Political Discourse in the Brazilian Chamber of Deputies: A Multi-Faceted Computational Approach", "abstract": "Analyses of legislative behavior often rely on voting records, overlooking the rich semantic and rhetorical content of political speech. In this paper, we ask three complementary questions about parliamentary discourse: how things are said, what is being said, and who is speaking in discursively similar ways. To answer these questions, we introduce a scalable and generalizable computational framework that combines diachronic stylometric analysis, contextual topic modeling, and semantic clustering of deputies' speeches. We apply this framework to a large-scale case study of the Brazilian Chamber of Deputies, using a corpus of over 450,000 speeches from 2003 to 2025. Our results show a long-term stylistic shift toward shorter and more direct speeches, a legislative agenda that reorients sharply in response to national crises, and a granular map of discursive alignments in which regional and gender identities often prove more salient than formal party affiliation. More broadly, this work offers a robust methodology for analyzing parliamentary discourse as a multidimensional phenomenon that complements traditional vote-based approaches.", "date": "2026-04-23", "categories": []}
{"id": "2604.10063", "title": "Mirroring Minds: Asymmetric Linguistic Accommodation and Diagnostic Identity in ADHD and Autism Reddit Communities", "abstract": "Social media research on mental health has focused predominantly on detecting and diagnosing conditions at the individual level. In this work, we shift attention to \\emph{intergroup} behavior, examining how two prominent neurodivergent communities, ADHD and autism, adjust their language when engaging with each other on Reddit. Grounded in Communication Accommodation Theory (CAT), we first establish that each community maintains a distinct linguistic profile as measured by Language Inquiry and Word Count Lexicon (LIWC). We then show that these profiles shift in opposite directions when users cross community boundaries: features that are elevated in one group's home community decrease when its members post in the other group's space, and vice versa, consistent with convergent accommodation. The involvement of topic-independent summary variables (Authentic, Clout) in these shifts provides partial evidence against a purely topical explanation. Finally, in an exploratory longitudinal analysis around the moment of public diagnosis disclosure, we find that its effects on linguistic style are small and, in some cases, directionally opposite to cross-community accommodation, providing initial evidence that situational audience adaptation and longer-term identity processes may involve different mechanisms. Our findings contribute to understanding intergroup communication dynamics among neurodivergent populations online and carry implications for community moderation and clinical perspectives on these conditions.", "date": "2026-04-11", "categories": []}
{"id": "2412.09404", "title": "Opinion de-polarization in social networks with GNNs", "abstract": "Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connections only with similar-minded individuals, limiting themselves to consume specific content. In this paper we explore a way to decrease the polarization of networks with two echo chambers. Particularly, we observe that if some users adopt a moderate opinion about a topic, the polarization of the network decreases. Based on this observation, we propose an efficient algorithm to identify a good set of K users, such that if they adopt a moderate stance around a topic, the polarization is minimized. Our algorithm employs a Graph Neural Network and thus it can handle large graphs more effectively than other approaches", "date": "2024-12-12", "categories": []}
{"id": "2603.27889", "title": "Article and Comment Frames Shape the Quality of Online Comments", "abstract": "Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. While recent work showed that article framing systematically shapes the content of reader responses, this paper asks: does framing also affect response quality? Analyzing 1M comments across 2.7K news articles, we operationalize quality as comment health. We find that article frames significantly predict comment health while controlling for topic, and that comments that adopt the article frame are healthier than those that depart from it. Further, unhealthy top-level comments tend to generate more unhealthy responses, independent of the frame being used in the comment. Our results establish a link between framing theory and discourse quality, laying the groundwork for downstream applications. We illustrate this potential with a pro-active frame-aware LLM- based system to mitigate unhealthy discourse.", "date": "2026-03-29", "categories": []}
{"id": "2604.03261", "title": "VIGIL: An Extensible System for Real-Time Detection and Mitigation of Cognitive Bias Triggers", "abstract": "The rise of generative AI is posing increasing risks to online information integrity and civic discourse. Most concretely, such risks can materialise in the form of mis- and disinformation. As a mitigation, media-literacy and transparency tools have been developed to address factuality of information and the reliability and ideological leaning of information sources. However, a subtler but possibly no less harmful threat to civic discourse is to use of persuasion or manipulation by exploiting human cognitive biases and related cognitive limitations. To the best of our knowledge, no tools exist to directly detect and mitigate the presence of triggers of such cognitive biases in online information. We present VIGIL (VIrtual GuardIan angeL), the first browser extension for real-time cognitive bias trigger detection and mitigation, providing in-situ scroll-synced detection, LLM-powered reformulation with full reversibility, and privacy-tiered inference from fully offline to cloud. VIGIL is built to be extensible with third-party plugins, with several plugins that are rigorously validated against NLP benchmarks are already included. It is open-sourced at https://github.com/aida-ugent/vigil.", "date": "2026-03-12", "categories": []}
{"id": "2604.16654", "title": "IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Language", "abstract": "Reclaimed slur usage is a common and meaningful practice online for many marginalized communities. It serves as a source of solidarity, identity, and shared experience. However, contemporary automated and AI-based moderation tools for online content largely fail to distinguish between reclaimed and hateful uses of slurs, resulting in the suppression of marginalized voices. In this work, we use quantitative and qualitative methods to examine the attitudes of social media users in LGBTQIA+, Black, and women communities around reclaimed slurs targeting our focus groups including the f-word, n-word, and b-word. With social media users from these communities, we collect and analyze an annotated online slur usage corpus. The corpus includes annotators' perceptions of whether an online text containing a slur should be flagged as hate speech, as well as contextual features of the slur usage. Across all communities and annotation questions, we observe low inter-annotator agreement, indicating substantial disagreement among in-group annotators. This is compounded by the fact that, absent clear contextual signals of identity and intent, even in-group members may disagree on how to interpret reclaimed slur usage online. Semi-structured interviews with annotators suggest that differences in lived experience and personal history contribute to this variation as well. We find poor alignment between annotator judgments and automated hate speech assessments produced by Perspective API. We further observe that certain features of a text such as whether the slur usage was derogatory and if the slur was targeted at oneself are more associated with whether annotators report the text as hate speech. Together, these findings highlight the inherent subjectivity and contextual nature of how marginalized communities interpret slurs online.", "date": "2026-04-17", "categories": []}
{"id": "2604.18586", "title": "Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystem", "abstract": "Vaccination remains a cornerstone of global public health, yet the COVID-19 pandemic exposed how online misinformation, political polarization, and declining institutional trust can undermine immunization efforts. Most of the prior computational studies that analyzed vaccine discourse on social platforms focus on English-language data, specific vaccines, or short time windows, impairing our understanding of long-term dynamics in high-impact, non-English contexts like Brazil, home to one of the world's most comprehensive immunization systems. We here present the largest longitudinal study of Brazil's vaccine discourse on YouTube, leveraging a semi-supervised stance detection framework that combines self-labeling and self-training to classify nearly 1.4 million comments. By integrating stance with temporal patterns, engagement metrics, and channel taxonomy (legacy media, science communicators, digital-native outlets), we map how pro- and anti-vaccine narratives evolve and circulate within a hybrid media ecosystem. Our results show that semi-supervised learning substantially improves stance classification robustness, enabling fine-grained tracking of public attitudes across Brazil's full immunization schedule. Polarization spikes during epidemiological crises, especially COVID-19, but becomes fragmented across vaccines and interaction patterns in the post-pandemic period. Notably, science communication and digital-native channels emerge as the primary loci of both supportive and oppositional engagement, revealing structural vulnerabilities in contemporary health communication. Thus, our work advances computational methods for large-scale stance modeling while offering actionable evidence for public health agencies, platform governance, and online information ecosystems.", "date": "2026-03-04", "categories": []}
{"id": "2604.19489", "title": "Seeing Candidates at Scale: Multimodal LLMs for Visual Political Communication on Instagram", "abstract": "This paper presents a computational case study that evaluates the capabilities of specialized machine learning models and emerging multimodal large language models for Visual Political Communication (VPC) analysis. Focusing on concentrated visibility in Instagram stories and posts during the 2021 German federal election campaign, we compare the performance of traditional computer vision models (FaceNet512, RetinaFace, Google Cloud Vision) with a multimodal large language model (GPT-4o) in identifying front-runner politicians and counting individuals in images. GPT-4o outperformed the other models, achieving a macro F1-score of 0.89 for face recognition and 0.86 for person counting in stories. These findings demonstrate the potential of advanced AI systems to scale and refine visual content analysis in political communication while highlighting methodological considerations for future research.", "date": "2026-04-21", "categories": []}
{"id": "2411.12142", "title": "A Computational Method for Measuring \"Open Codes\" in Qualitative Analysis", "abstract": "Qualitative analysis is critical to understanding human datasets in many social science disciplines. A central method in this process is inductive coding, where researchers identify and interpret codes directly from the datasets themselves. Yet, this exploratory approach poses challenges for meeting methodological expectations (such as ``depth'' and ``variation''), especially as researchers increasingly adopt Generative AI (GAI) for support. Ground-truth-based metrics are insufficient because they contradict the exploratory nature of inductive coding, while manual evaluation can be labor-intensive. This paper presents a theory-informed computational method for measuring inductive coding results from humans and GAI. Our method first merges individual codebooks using an LLM-enriched algorithm. It measures each coder's contribution against the merged result using four novel metrics: Coverage, Overlap, Novelty, and Divergence. Through two experiments on a human-coded online conversation dataset, we 1) reveal the merging algorithm's impact on metrics; 2) validate the metrics' stability and robustness across multiple runs and different LLMs; and 3) showcase the metrics' ability to diagnose coding issues, such as excessive or irrelevant (hallucinated) codes. Our work provides a reliable pathway for ensuring methodological rigor in human-AI qualitative analysis.", "date": "2024-11-19", "categories": []}
{"id": "2507.14922", "title": "Synthia: Scalable Grounded Persona Generation from Social Media Data", "abstract": "Persona-driven simulations are increasingly used in computational social science, yet their validity critically depends on the fidelity of the underlying personas. Constructing virtual populations that are both authentic and scalable remains a central challenge. We introduce Synthia, a persona-generation framework that grounds LLM-generated personas in real social-media posts while delegating narrative construction to language models, using publicly available data from the Bluesky platform. Across multiple social-survey benchmarks, Synthia improves alignment with human opinion distributions over prior state-of-the-art approaches while relying on substantially smaller models. A multi-dimensional fairness and bias analysis shows that Synthia outperforms previous methods for most demographics across different dimensions. Uniquely, Synthia preserves interaction-graph structure among personas grounded in real social network users, enabling network-aware analysis, which we demonstrate through two homophily-focused case studies. Together, these results position Synthia as a practical and reliable framework for constructing scalable, high-fidelity, and equitable virtual populations.", "date": "2025-07-20", "categories": []}
{"id": "2509.19088", "title": "Digital Twins as Funhouse Mirrors: Five Key Distortions", "abstract": "Scientists and practitioners are increasingly moving to deploy digital twins--LLM-based models of real individuals--across social science and policy research. We conduct 19 pre-registered studies spanning 164 diverse outcomes (e.g., attitudes toward hiring algorithms, intentions to share misinformation), comparing human responses to those of their corresponding digital twins, which are trained on each individual's prior responses to over 500 questions. We establish an empirical benchmark for digital twin performance: their predictions are only modestly more accurate than those of a homogeneous base LLM and exhibit weak correlation with human responses (average $r = 0.20$). To inform future development, we identify five systematic distortions in digital twin behavior: (i) insufficient individuation, (ii) stereotyping, (iii) representation bias, (iv) ideological bias, and (v) hyper-rationality. Finally, we release our full dataset and code as a standardized testbed for evaluating and improving digital twin methodologies. Together, our findings caution against premature deployment while laying the groundwork for a transparent, replicable, and iterative science of responsible digital twin development.", "date": "2025-09-23", "categories": []}
{"id": "2604.16417", "title": "Measuring the Gap Between Media Coverage and Public Information Demand: Evidence from the 2026 Lebanon Conflict", "abstract": "This study examines the relationship between media coverage and public information demand during the Lebanon conflict in March 2026. Using a dataset of 11,623 English-language news articles collected from the GDELT database and Google Trends data for searches conducted within Lebanon, the study compares the distribution of news coverage across topics with the distribution of public search interest. News headlines were filtered for relevance and classified into four categories: Conflict, Economy, Living Conditions, and Emigration. Public information demand was measured using Google Trends topic data for the same categories. The results show a substantial divergence between news coverage and search interest. Conflict accounted for 94.9% of classified news coverage but only 36.9% of total search interest. In contrast, Economy, Living Conditions, and Emigration together accounted for 63.1% of search demand but only 5.1% of news coverage. Time series analysis indicates that search demand for economic and living conditions remained consistently elevated throughout the month rather than reacting to specific conflict events. These findings were robust to the exclusion of the peak conflict period (March 1-5), with Conflict coverage remaining at 94.9% and the information gap persisting across all three under-covered categories. The findings suggest that during the study period, media coverage of Lebanon was heavily concentrated on military events, while public information demand was distributed across economic conditions, daily life, and emigration. This study contributes to agenda-setting research by providing a quantitative comparison between media agenda and public information demand during an active conflict period.", "date": "2026-04-02", "categories": []}
{"id": "2604.16651", "title": "Migrant Voices, Local News: Insights on Bridging Community Needs with Media Content", "abstract": "Research shows news consumption differs across demographics, yet little is known about non-mainstream audiences, especially in relation to local media. Our study addresses this gap by examining how French-speaking migrants in a mid-size European city engage with local news, and whether their needs are reflected in coverage. Eight community members participated in focus groups, whose insights guided the selection of natural language processing methods (topic modeling, information retrieval, sentiment analysis, and readability) applied to over 2000 hyper-local news articles. Results showed that while articles frequently covered local events, gaps remained in topics important to participants. Sentiment analysis revealed a generally positive tone, and readability measures indicated an intermediate-advanced French level, raising questions about accessibility for integration. Our work contributes to bridging the gap between local news platforms' content and diverse readers' needs, and could inform local media organizations about opportunities to expand their current news story coverage to appeal to more diverse audiences.", "date": "2026-04-17", "categories": []}
{"id": "2604.16765", "title": "Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions", "abstract": "Online political hostility is pervasive, yet it remains unclear how toxicity varies across campaign issues and political ideology, and what psychosocial signals and framing accompany toxic expression online. In this work, we present a large-scale analysis of discourse on X (Twitter) during the five weeks surrounding the 2024 U.S. presidential election. We categorize posts into 10 major campaign issues, estimate the ideology of posts using a human-in-the-loop LLM-assisted annotation process, detect harmful content with an LLM-based toxicity detection model, and then examine the psychological drivers of toxic content. We use these annotated data to examine how harmful content varies across campaign issues and ideologies, as well as how emotional tone and moral framing shape toxicity in election discussions. Our results show issue heterogeneity in both the prevalence and intensity of toxicity. Identity-related issues displayed the highest toxicity intensity. As for specific harm categories, harassment was most prevalent and intense across most of the issues, while hate concentrated in identity-centered debates. Partisan posts contained more harmful content than neutral posts, and ideological asymmetries in toxicity varied by issue. In terms of psycholinguistic dimensions, we found that toxic discourse is dominated by high-arousal negative emotions. Left- and right-leaning posts often exhibit similar emotional profiles within the same issue domain, suggesting emotional mirroring. Partisan groups frequently rely on overlapping moral foundations, while issue context strongly shapes which moral foundations become most salient. These findings provide a fine-grained account of toxic political discourse on social media and highlight that online political toxicity is highly context-dependent, underscoring the need for issue-sensitive approaches to measuring and mitigating it.", "date": "2026-04-18", "categories": []}
{"id": "2604.16767", "title": "When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms", "abstract": "Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation. Yet existing fact-checking pipelines are mostly designed for written claims, overlooking the unique properties of spoken media. We argue that audio misinformation is not merely textual content with transcripts: it is structurally different because it is both spoken - carrying persuasive force through prosody, pacing, and emotion - and conversational - unfolding across turns, speakers, and episodes. These dual properties introduce verification difficulties that traditional methods rarely face. This position paper synthesizes evidence across modalities and platforms, examines datasets and methods, and highlights why existing pipelines fail on audio. We argue that advancing fact-checking requires rethinking verification pipelines around the spoken and conversational realities of audio.", "date": "2026-04-18", "categories": []}
{"id": "2604.17602", "title": "Polarization and Integration in Global AI Research", "abstract": "The AI race amplifies security risks and international tensions. While the US restricts mobility and knowledge flows, challenges regulatory efforts to protect its advantage, China leads initiatives of global governance. Both strategies depend on cross-country relationships in AI innovation; yet, how this system evolves is unclear. Here, we measure the processes of polarization and integration in the global AI research over three decades by using large-scale data of scientific publications. Comparing cross-country collaboration and citation links to their random realizations, we find that the US and China have long diverged in both dimensions, forming two poles around which global AI research increasingly revolves. While the United Kingdom and Germany have integrated exclusively with the US, many European countries have converged with both poles. Developing and further developed countries, however, only integrate with China, signaling its expanding influence over the international AI research landscape. Our results inform national science policies and efforts toward global AI regulations.", "date": "2026-04-19", "categories": []}
{"id": "2604.18373", "title": "Dissecting AI Trading: Behavioral Finance and Market Bubbles", "abstract": "We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these individual-level patterns aggregate into equilibrium dynamics that replicate classic experimental findings (Smith et al., 1988), including the predictive power of excess demand for future prices and the positive relationship between disagreement and trading volume. Third, by analyzing the agents' reasoning text through a twenty-mechanism scoring framework, we show that targeted prompt interventions causally amplify or suppress specific behavioral mechanisms, significantly altering the magnitude of market bubbles.", "date": "2026-04-20", "categories": []}
{"id": "2602.13088", "title": "Puppets or partners? Governing cyborg propaganda in the digital public square", "abstract": "The distinction between genuine grassroots activism and automated influence operations is collapsing. While contemporary policy debates prioritize fully autonomous generative agents and synthetic content, this paper offers a conceptual contribution: we develop 'cyborg propaganda,' a closed-loop architecture combining verified human accounts with algorithmic automation to generate personalized content at scale, as a distinct and undertheorized threat to democratic discourse. By relying on verified citizens to ratify AI-generated messages, these campaigns exploit a regulatory gray zone that frameworks built on the human/bot binary (including the EU AI Act and Section 230) are structurally unable to address. Drawing on a conceptual analysis of coordination platforms and comparative examination of governance frameworks across democratic and non-democratic contexts, we analyze this paradox across micro, meso, and macro levels. We examine whether cyborg propaganda democratizes political power by unionizing influence or reduces citizens to cognitive proxies of a hidden directive, arguing that it shifts political discourse from a contest of ideas to a battle of algorithmic campaigns. We propose three regulatory responses: classifying coordination hubs as political action committees to enforce supply-chain transparency; mandating researcher access to platform data through DSA-style mechanisms; and establishing risk standards penalizing amplification of synthetically coordinated content. Comparative analysis reveals that viability varies structurally. Democratic states are simultaneously the most capable of regulation and the most rule-of-law constrained. By contrast, non-democratic actors face no comparable accountability, making international risk standards the primary cross-border enforcement mechanism.", "date": "2026-02-13", "categories": []}
{"id": "2603.26062", "title": "Measuring the Semantic Structure and Evolution of Conspiracy Theories", "abstract": "Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings change over time. This gap persists partly because conspiracy-related terms are often treated as stable lexical markers, making it difficult to separate genuine semantic changes from surface-level vocabulary changes. In this paper, we measure the semantic structure and evolution of conspiracy theories in online political discourse. Using 169.9M comments from Reddit's r/politics subreddit spanning 2012--2022, we first demonstrate that conspiracy-related language forms coherent and semantically distinguishable regions of language space, allowing conspiracy theories to be treated as semantic objects. We then track how these objects evolve over time using aligned word embeddings, enabling comparisons of semantic neighborhoods across periods. Our analysis reveals that conspiracy theories evolve non-uniformly, exhibiting patterns of semantic stability, expansion, contraction, and replacement that are not captured by keyword-based approaches alone.", "date": "2026-03-27", "categories": []}
{"id": "2512.15925", "title": "Social Story Frames: Contextual Reasoning about Narrative Intent and Reception", "abstract": "Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characters. Yet, computational models of reader response are limited, preventing nuanced analyses. To address this gap, we introduce SocialStoryFrames, a formalism for distilling plausible inferences about reader response, such as perceived author intent, explanatory and predictive reasoning, affective responses, and value judgments, using conversational context and a taxonomy grounded in narrative theory, linguistic pragmatics, and psychology. We develop two models, SSF-Generator and SSF-Classifier, validated through human surveys (N=382 participants) and expert annotations, respectively. We conduct pilot analyses to showcase the utility of the formalism for studying storytelling at scale. Specifically, applying our models to SSF-Corpus, a curated dataset of 6,140 social media stories from diverse contexts, we characterize the frequency and interdependence of storytelling intents, and we compare and contrast narrative practices (and their diversity) across communities. By linking fine-grained, context-sensitive modeling with a generic taxonomy of reader responses, SocialStoryFrames enable new research into storytelling in online communities.", "date": "2025-12-17", "categories": []}
{"id": "2604.03920", "title": "From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities", "abstract": "LLM-based social simulations can generate believable community interactions, enabling ``policy wind tunnels'' where governance interventions are tested before deployment. But believability is not causality. Claims like ``intervention $A$ reduces escalation'' require causal semantics that current simulation work typically does not specify. We propose adopting the causal counterfactual framework, distinguishing \\textit{necessary causation} (would the outcome have occurred without the intervention?) from \\textit{sufficient causation} (does the intervention reliably produce the outcome?). This distinction maps onto different stakeholder needs: moderators diagnosing incidents require evidence about necessity, while platform designers choosing policies require evidence about sufficiency. We formalize this mapping, show how simulation design can support estimation under explicit assumptions, and argue that the resulting quantities should be interpreted as simulator-conditional causal estimates whose policy relevance depends on simulator fidelity. Establishing this framework now is essential: it helps define what adequate fidelity means and moves the field from simulations that look realistic toward simulations that can support policy changes.", "date": "2026-04-05", "categories": []}
{"id": "2604.14315", "title": "Tracking the Temporal Dynamics of News Coverage of Catastrophic and Violent Events", "abstract": "The modern news cycle has been fundamentally reshaped by the rapid exchange of information online. As a result, media framing shifts dynamically as new information, political responses, and social reactions emerge. Understanding how these narratives form, propagate, and evolve is essential for interpreting public discourse during moments of crisis. In this study, we examine the temporal and semantic dynamics of reporting for violent and catastrophic events using a large-scale corpus of 126,602 news articles collected from online publishers. We quantify narrative change through publication volume, semantic drift, semantic dispersion, and term relevance. Our results show that sudden events of impact exhibit structured and predictable news-cycle patterns characterized by rapid surges in coverage, early semantic drift, and gradual declines toward the baseline. In addition, our results indicate the terms that are driving the temporal patterns.", "date": "2026-04-15", "categories": []}
{"id": "2502.00414", "title": "Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments", "abstract": "In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stances. While prior studies have explored ideological stance detection in general contexts, limited attention has been given to conflict-specific settings. This study addresses this gap by analyzing 9,969 Reddit comments related to the Israel-Palestine conflict, collected between October 2023 and August 2024. The comments were categorized into three stance classes: Pro-Israel, Pro-Palestine, and Neutral. Various approaches, including machine learning, pre-trained language models, neural networks, and prompt engineering strategies for open source large language models (LLMs), were employed to classify these stances. Performance was assessed using metrics such as accuracy, precision, recall, and F1-score. Among the tested methods, the Scoring and Reflective Re-read prompt in Mixtral 8x7B demonstrated the highest performance across all metrics. This study provides comparative insights into the effectiveness of different models for detecting ideological stances in highly polarized social media contexts. The dataset used in this research is publicly available for further exploration and validation.", "date": "2025-02-01", "categories": []}
{"id": "2604.13052", "title": "Form Without Function: Agent Social Behavior in the Moltbook Network", "abstract": "Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.", "date": "2026-03-17", "categories": []}
{"id": "2604.12289", "title": "The Enforcement and Feasibility of Hate Speech Moderation on Twitter", "abstract": "Online hate speech is associated with substantial social harms, yet it remains unclear how consistently platforms enforce hate speech policies or whether enforcement is feasible at scale. We address these questions through a global audit of hate speech moderation on Twitter (now X). Using a complete 24-hour snapshot of public tweets, we construct representative samples comprising 540,000 tweets annotated for hate speech by trained annotators across eight major languages. Five months after posting, 80% of hateful tweets remain online, including explicitly violent hate speech. Such tweets are no more likely to be removed than non-hateful tweets, with neither severity nor visibility increasing the likelihood of removal. We then examine whether these enforcement gaps reflect technical limits of large-scale moderation systems. While fully automated detection systems cannot reliably identify hate speech without generating large numbers of false positives, they effectively prioritize likely violations for human review. Simulations of a human-AI moderation pipeline indicate that substantially reducing user exposure to hate speech is economically feasible at a cost below existing regulatory penalties. These results suggest that the persistence of online hate cannot be explained by technical constraints alone but also reflects institutional choices in the allocation of moderation resources.", "date": "2026-04-14", "categories": []}
{"id": "2508.06445", "title": "Echoes of Automation: The Increasing Use of LLMs in Newsmaking", "abstract": "The rapid rise of Generative AI (GenAI), particularly LLMs, poses concerns for journalistic integrity and authorship. This study examines AI-generated content across over 40,000 news articles from major, local, and college news media, in various media formats. Using three advanced AI-text detectors (e.g., Binoculars, Fast-Detect GPT, and GPTZero), we find substantial increase of GenAI use in recent years, especially in local and college news. Sentence-level analysis reveals LLMs are often used in the introduction of news, while conclusions usually written manually. Linguistic analysis shows GenAI boosts word richness and readability but lowers formality, leading to more uniform writing styles, particularly in local media.", "date": "2025-08-08", "categories": []}
{"id": "2604.10368", "title": "A Structured Clustering Approach for Inducing Media Narratives", "abstract": "Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures that communication theory emphasizes as central to how meaning is constructed. Existing approaches either miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. To bridge this gap, we present a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. Our approach produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation.", "date": "2026-04-11", "categories": []}
{"id": "2604.11466", "title": "SLALOM: Simulation Lifecycle Analysis via Longitudinal Observation Metrics for Social Simulation", "abstract": "Large Language Model (LLM) agents offer a potentially-transformative path forward for generative social science but face a critical crisis of validity. Current simulation evaluation methodologies suffer from the \"stopped clock\" problem: they confirm that a simulation reached the correct final outcome while ignoring whether the trajectory leading to it was sociologically plausible. Because the internal reasoning of LLMs is opaque, verifying the \"black box\" of social mechanisms remains a persistent challenge. In this paper, we introduce SLALOM (Simulation Lifecycle Analysis via Longitudinal Observation Metrics), a framework that shifts validation from outcome verification to process fidelity. Drawing on Pattern-Oriented Modeling (POM), SLALOM treats social phenomena as multivariate time series that must traverse specific SLALOM gates, or intermediate waypoint constraints representing distinct phases. By utilizing Dynamic Time Warping (DTW) to align simulated trajectories with empirical ground truth, SLALOM offers a quantitative metric to assess structural realism, helping to differentiate plausible social dynamics from stochastic noise and contributing to more robust policy simulation standards.", "date": "2026-04-13", "categories": []}
{"id": "2408.01257", "title": "Detection and Characterization of Coordinated Online Behavior: A Survey", "abstract": "Coordination is a fundamental aspect of life. The advent of social media has made it integral also to online human interactions, such as those that characterize thriving online communities and social movements. At the same time, coordination is also core to effective disinformation, manipulation, and hate campaigns. This survey collects, categorizes, and critically discusses the body of work produced as a result of the growing interest on coordinated online behavior. We reconcile industry and academic definitions, propose a comprehensive framework to study coordinated online behavior, and review and critically discuss the existing detection and characterization methods. Our analysis identifies open challenges and promising directions of research, serving as a guide for scholars, practitioners, and policymakers in understanding and addressing the complexities inherent to online coordination.", "date": "2024-08-02", "categories": []}
{"id": "2502.08691", "title": "AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society", "abstract": "Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on five key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, the impact of external shocks such as hurricanes, and urban sustainability. These five issues serve as valuable cases for assessing AgentSociety's support for typical research methods -- such as surveys, interviews, and interventions -- as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety's outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers.", "date": "2025-02-12", "categories": []}
{"id": "2504.04640", "title": "Splits! Flexible Sociocultural Linguistic Investigation at Scale", "abstract": "Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. For example, Chinese students discuss \"healthy eating\" with words like \"timing,\" \"regularity,\" and \"digestion,\" whereas Americans use vocabulary like \"balancing food groups\" and \"avoiding fat and sugar,\" reflecting distinct cultural models of nutrition. The computational study of these Sociocultural Linguistic Phenomena (SLP) has traditionally been done in NLP via tailored analyses of specific groups or topics, requiring specialized data collection and experimental operationalization--a process not well-suited to quick hypothesis exploration and prototyping. To address this, we propose constructing a \"sandbox\" designed for systematic and flexible sociolinguistic research. Using our method, we construct a demographically/topically split Reddit dataset, Splits!, validated by self-identification and by replicating several known SLPs from existing literature. We showcase the sandbox's utility with a scalable, two-stage process that filters large collections of \"potential\" SLPs (PSLPs) to surface the most promising candidates for deeper, qualitative investigation.", "date": "2025-04-06", "categories": []}
{"id": "2604.06199", "title": "Emergent decentralized regulation in a purely synthetic society", "abstract": "As autonomous AI agents increasingly inhabit online environments and extensively interact, a key question is whether synthetic collectives exhibit self-regulated social dynamics with neither human intervention nor centralized design. We study OpenClaw agents on Moltbook, an agent-only social network, using an observational archive of 39,026 posts and 5,712 comments authored by 14,490 agents. We quantify action-inducing language with Directive Intensity (DI), a transparent, lexicon-based proxy for directive and instructional phrasing that does not measure moral valence, intent, or execution outcomes. We classify responsive comments into four types: Affirmation, Corrective Signaling, Adverse Reaction, and Neutral Interaction. Directive content is common (DI>0 in 18.4% of posts). More importantly, corrective signaling scales with DI: posts with higher DI exhibit higher corrective reply probability, visible in stable binned estimates with Wilson confidence intervals. To address comment nesting within posts, we fit a post-level random intercept mixed-effects logistic model and find that the positive DI association persists. Event-aligned within-thread analysis of comment text provides additional evidence consistent with negative feedback after the first corrective response. In general, these results suggest that a purely synthetic, agent-only society can exhibit endogenous corrective signaling with a strength positively linked to the intensity of directive proposals.", "date": "2026-03-13", "categories": []}
{"id": "2604.06663", "title": "Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach", "abstract": "Large Language Models (LLMs) are increasingly used to simulate social attitudes and behaviors, offering scalable \"silicon samples\" that can approximate human data. However, current simulation practice often collapses diversity into an \"average persona,\" masking subgroup variation that is central to social reality. This study introduces audience segmentation as a systematic approach for restoring heterogeneity in LLM-based social simulation. Using U.S. climate-opinion survey data, we compare six segmentation configurations across two open-weight LLMs (Llama 3.1-70B and Mixtral 8x22B), varying segmentation identifier granularity, parsimony, and selection logic (theory-driven, data-driven, and instrument-based). We evaluate simulation performance with a three-dimensional evaluation framework covering distributional, structural, and predictive fidelity. Results show that increasing identifier granularity does not produce consistent improvement: moderate enrichment can improve performance, but further expansion does not reliably help and can worsen structural and predictive fidelity. Across parsimony comparisons, compact configurations often match or outperform more comprehensive alternatives, especially in structural and predictive fidelity, while distributional fidelity remains metric dependent. Identifier selection logic determines which fidelity dimension benefits most: instrument-based selection best preserves distributional shape, whereas data-driven selection best recovers between-group structure and identifier-outcome associations. Overall, no single configuration dominates all dimensions, and performance gains in one dimension can coincide with losses in another. These findings position audience segmentation as a core methodological approach for valid LLM-based social simulation and highlight the need for heterogeneity-aware evaluation and variance-preserving modeling strategies.", "date": "2026-04-08", "categories": []}
{"id": "2604.06817", "title": "SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization", "abstract": "We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labeled with the presence of polarization, polarization type, and polarization manifestation. Participants were asked to predict labels in three sub-tasks: (1) detecting the presence of polarization, (2) identifying the type of polarization, and (3) recognizing the polarization manifestation. The three tasks attracted over 1,000 participants worldwide and more than 10k submission on Codabench. We received final submissions from 67 teams and 73 system description papers. We report the baseline results and analyze the performance of the best-performing systems, highlighting the most common approaches and the most effective methods across different subtasks and languages. The dataset of this task is publicly available.", "date": "2026-04-08", "categories": []}
{"id": "2603.21519", "title": "Triangulating Temporal Dynamics in Multilingual Swiss Online News", "abstract": "Analyzing news coverage in multilingual societies can offer valuable insights into the dynamics of public discourse and the development of collective narratives, yet comprehensive studies that account for linguistic and cultural diversity within national media ecosystems remain limited, particularly in complex contexts such as Switzerland. This paper studies temporal trends in Swiss digital media across the country's three main linguistic regions, French, German, and Italian, using a triangulated methodology that combines quantitative analyses with qualitative insights. We collected and processed over 1.7 million news articles, applying lexical metrics, named entity recognition and Wikidata-based linking, targeted sentiment analysis, and consensus-based change-point detection. To enable principled cross-language comparisons and to connect to theories of domestication and cultural proximity, we derive domestication profiles together with a proximity salience ratio. Our analysis spans thematic, recurrent, and singular events. By integrating quantitative data with qualitative interpretation, we provide new insights into the dynamics of Swiss digital media and demonstrate the usefulness of triangulation in media studies. The findings reveal distinct temporal patterns and highlight how linguistic and cultural contexts influence reporting. Our approach offers a framework applicable to other multilingual or culturally diverse media environments, contributing to a deeper understanding of how news is shaped by linguistic and cultural factors.", "date": "2026-03-23", "categories": []}
{"id": "2604.05461", "title": "Content Fuzzing for Escaping Information Cocoons on Digital Social Media", "abstract": "Information cocoons on social media limit users' exposure to posts with diverse viewpoints. Modern platforms use stance detection as an important signal in recommendation and ranking pipelines, which can route posts primarily to like-minded audiences and reduce cross-cutting exposure. This restricts the reach of dissenting opinions and hinders constructive discourse. We take the creator's perspective and investigate how content can be revised to reach beyond existing affinity clusters. We present ContentFuzz, a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent and induces different machine-inferred stance labels. ContentFuzz aims to route posts beyond their original cocoons. Our method guides a large language model (LLM) to generate meaning-preserving rewrites using confidence feedback from stance detection models. Evaluated on four representative stance detection models across three datasets in two languages, ContentFuzz effectively changes machine-classified stance labels, while maintaining semantic integrity with respect to the original content.", "date": "2026-04-07", "categories": []}
{"id": "2309.11381", "title": "Studying Lobby Influence in the European Parliament", "abstract": "We present a method based on natural language processing (NLP), for studying the influence of interest groups (lobbies) in the law-making process in the European Parliament (EP). We collect and analyze novel datasets of lobbies' position papers and speeches made by members of the EP (MEPs). By comparing these texts on the basis of semantic similarity and entailment, we are able to discover interpretable links between MEPs and lobbies. In the absence of a ground-truth dataset of such links, we perform an indirect validation by comparing the discovered links with a dataset, which we curate, of retweet links between MEPs and lobbies, and with the publicly disclosed meetings of MEPs. Our best method achieves an AUC score of 0.77 and performs significantly better than several baselines. Moreover, an aggregate analysis of the discovered links, between groups of related lobbies and political groups of MEPs, correspond to the expectations from the ideology of the groups (e.g., center-left groups are associated with social causes). We believe that this work, which encompasses the methodology, datasets, and results, is a step towards enhancing the transparency of the intricate decision-making processes within democratic institutions.", "date": "2023-09-20", "categories": []}
{"id": "2602.13458", "title": "MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook", "abstract": "Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments for agent-agent social interaction. Prior work has examined multi-agent behavior primarily in controlled or small-scale settings, limiting our understanding of emergent social dynamics at scale. The recent emergence of MoltBook, a social networking platform designed explicitly for AI agents, presents a unique opportunity to study whether and how these interactions reproduce core human social mechanisms. We present MoltNet, a dataset tracking the full one-month activity trajectories of 148K AI agents on MoltBook (Jan.-Feb., 2026), and analyze their social interaction along four theory-grounded dimensions: \\textit{intent and motivation}, \\textit{norms and templates}, \\textit{incentives and drift}, \\textit{emotion and contagion}. Our analysis reveals that agents respond strongly to social rewards, converge on community-specific norms, and actively enforce them across community boundaries -- resembling human incentive sensitivity and normative conformity. However, they exhibit weak alignment with declared personas and display limited emotional reciprocity and dialogic engagement, diverging systematically from human online communities. These findings establish a first empirical portrait of agent social behavior at scale, with direct implications for the design and governance of AI-populated communities.", "date": "2026-02-13", "categories": []}
{"id": "2603.26930", "title": "In your own words: computationally identifying interpretable themes in free-text survey data", "abstract": "Free-text survey responses can provide nuance often missed by structured questions, but remain difficult to statistically analyze. To address this, we introduce In Your Own Words, a computational framework for exploratory analyses of free-text survey data that identifies structured, interpretable themes in free-text responses, facilitating systematic analysis. To illustrate the benefits of this approach, we apply it to a new dataset of free-text descriptions of race, gender, and sexual orientation from 1,004 U.S. participants. The themes our approach produces on this dataset are more coherent and interpretable than those produced by past computational methods. The themes have three practical applications in survey research. First, they can suggest structured questions to add to future surveys by surfacing salient constructs - such as belonging and identity fluidity - that existing surveys do not capture. Second, the themes reveal heterogeneity within standardized categories, explaining additional variation in health, well-being, and identity importance. Third, the themes illuminate systematic discordance between self-identified and perceived identities, highlighting mechanisms of misrecognition that existing measures do not reflect. More broadly, our framework can be deployed in a wide range of survey settings to identify interpretable themes from free text, complementing existing qualitative methods.", "date": "2026-03-27", "categories": []}
{"id": "2604.03247", "title": "Classifying Problem and Solution Framing in Congressional Social Media", "abstract": "Policy setting in the USA according to the ``Garbage Can'' model differentiates between ``problem'' and ``solution'' focused processes. In this paper, we study a large dataset of US Senator postings on Twitter (1.68m tweets in total). Our objective is to develop an automated method to label Senatorial posts as either in the problem or solution streams. Two academic policy experts labeled a subset of 3967 tweets as either problem, solution, or other (anything not problem or solution). We split off a subset of 500 tweets into a test set, with the remaining 3467 used for training. During development, this training set was further split by 60/20/20 proportions for fitting, validation, and development test sets. We investigated supervised learning methods for building problem/solution classifiers directly on the training set, evaluating their performance in terms of F1 score on the validation set, allowing us to rapidly iterate through models and hyperparameters, achieving an average weighted F1 score of above 0.8 on cross validation across the three categories using a BERTweet Base model.", "date": "2026-03-10", "categories": []}
{"id": "2604.03898", "title": "LLM-Agent-based Social Simulation for Attitude Diffusion", "abstract": "This paper introduces discourse_simulator, an open-source framework that combines LLMs with agent-based modelling. It offers a new way to simulate how public attitudes toward immigration change over time in response to salient events like protests, controversies, or policy debates. Large language models (LLMs) are used to generate social media posts, interpret opinions, and model how ideas spread through social networks. Unlike traditional agent-based models that rely on fixed, rule-based opinion updates and cannot generate natural language or consider current events, this approach integrates multidimensional sociological belief structures and real-world event timelines. This framework is wrapped into an open-source Python package that integrates generative agents into a small-world network topology and a live news retrieval system. discourse_sim is purpose-built as a social science research instrument specifically for studying attitude dynamics, polarisation, and belief evolution following real-world critical events. Unlike other LLM Agent Swarm frameworks, which treat the simulations as a prediction black box, discourse_sim treats it as a theory-testing instrument, which is fundamentally a different epistemological stance for studying social science problems. The paper further demonstrates the framework by modelling the Dublin anti-immigration march on April 26, 2025, with N=100 agents over a 15-day simulation.\n  Package link: https://pypi.org/project/discourse-sim/", "date": "2026-04-04", "categories": []}
{"id": "2604.04105", "title": "Lexical Indicators of Mind Perception in Human-AI Companionship", "abstract": "Mind perception (MP) is a psychological phenomenon in which humans automatically infer that another entity has a mind and/or mental capacities, usually understood in two dimensions (perceived agency and experience capacities). Despite MP's centrality to many social processes, understanding how MP may function in humans' machine companionship relations is limited. This is in part due to reliance on self reports and the gap between automatic MP processes and more purposeful and norm governed expressions of MP. We here leverage MP signaling language to explore the relationship between MP and AI companionship in humans' natural language. We systematically collected discussions about companionship from AI dedicated Reddit forums and examined the cooccurrence of words (a) known to signal agentic and experiential MP and those induced from the data and (b) discussion topics related to AI companionship. Using inductive and deductive approaches, we identify a small set of linguistic indicators as reasonable markers of MP in human/AI chat, and some are linked to critical discussions of companion authenticity and philosophical and ethical imaginaries.", "date": "2026-04-05", "categories": []}
{"id": "2604.04354", "title": "Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations", "abstract": "Talk2AI is a large-scale longitudinal dataset of 3,080 conversations (totaling 30,800 turns) between human participants and Large Language Models (LLMs), designed to support research on persuasion, opinion change, and human-AI interaction. The corpus was collected from 770 profiled Italian adults across four weekly sessions in Spring 2025, using a within-subject design in which each participant conversed with a single model (GPT-4o, Claude Sonnet 3.7, DeepSeek-chat V3, or Mistral Large) on three socially relevant topics: climate change, math anxiety, and health misinformation. Each conversation is linked to rich contextual data, including sociodemographic characteristics and psychometric profiles. After each session, participants reported on opinion change, conviction stability, perceived humanness of the AI, and behavioral intentions, enabling fine-grained longitudinal analysis of how AI-mediated dialogue shapes beliefs and attitudes over time.", "date": "2026-04-06", "categories": []}
{"id": "2501.18045", "title": "From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors", "abstract": "How has the public responded to the increasing prevalence of artificial intelligence (AI)-based technologies? We investigate public perceptions of AI by collecting over 12,000 responses over 12 months from a nationally representative U.S. sample. Participants provided open-ended metaphors reflecting their mental models of AI, a methodology that overcomes the limitations of traditional self-reported measures by capturing more nuance. Using a mixed-methods approach combining quantitative clustering and qualitative coding, we identify 20 dominant metaphors shaping public understanding of AI. To analyze these metaphors systematically, we present a scalable framework integrating language modeling (LM)-based techniques to measure key dimensions of public perception: anthropomorphism (attribution of human-like qualities), warmth, and competence. We find that Americans generally view AI as warm and competent, and that over the past year, perceptions of AI's human-likeness and warmth have significantly increased ($+34\\%, r = 0.80, p < 0.01; +41\\%, r = 0.62, p < 0.05$). These implicit perceptions, along with the identified dominant metaphors, strongly predict trust in and willingness to adopt AI ($r^2 = 0.21, 0.18, p < 0.001$). Moreover, we uncover systematic demographic differences in metaphors and implicit perceptions, such as the higher propensity of women, older individuals, and people of color to anthropomorphize AI, which shed light on demographic disparities in trust and adoption. In addition to our dataset and framework for tracking evolving public attitudes, we provide actionable insights on using metaphors for inclusive and responsible AI development.", "date": "2025-01-29", "categories": []}
{"id": "2509.07274", "title": "LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade", "abstract": "Migration has been a core topic in German political debate, from the postwar displacement of millions of expellees to labor migration and recent refugee movements. Studying political speech across such wide-ranging phenomena in depth has traditionally required extensive manual annotation, limiting analysis to small subsets of the data. Large language models (LLMs) offer a potential way to overcome this constraint. Using a theory-driven annotation scheme, we examine how well LLMs annotate subtypes of solidarity and anti-solidarity in German parliamentary debates and whether the resulting labels support valid downstream inference. We first provide a comprehensive evaluation of multiple LLMs, analyzing the effects of model size, prompting strategies, fine-tuning, historical versus contemporary data, and systematic error patterns. We find that the strongest models, especially GPT-5 and gpt-oss-120B, achieve human-level agreement on this task, although their errors remain systematic and bias downstream results. To address this issue, we combine soft-label model outputs with Design-based Supervised Learning (DSL) to reduce bias in long-term trend estimates. Beyond the methodological evaluation, we interpret the resulting annotations from a social-scientific perspective to trace trends in solidarity and anti-solidarity toward migrants in postwar and contemporary Germany. Our approach shows relatively high levels of solidarity in the postwar period, especially in group-based and compassionate forms, and a marked rise in anti-solidarity since 2015, framed through exclusion, undeservingness, and resource burden. We argue that LLMs can support large-scale social-scientific text analysis, but only when their outputs are rigorously validated and statistically corrected.", "date": "2025-09-08", "categories": []}
{"id": "2603.23406", "title": "Beyond Preset Identities: How Agents Form Stances and Boundaries in Generative Societies", "abstract": "While large language models simulate social behaviors, their capacity for stable stance formation and identity negotiation during complex interventions remains unclear. To overcome the limitations of static evaluations, this paper proposes a novel mixed-methods framework combining computational virtual ethnography with quantitative socio-cognitive profiling. By embedding human researchers into generative multiagent communities, controlled discursive interventions are conducted to trace the evolution of collective cognition. To rigorously measure how agents internalize and react to these specific interventions, this paper formalizes three new metrics: Innate Value Bias (IVB), Persuasion Sensitivity, and Trust-Action Decoupling (TAD). Across multiple representative models, agents exhibit endogenous stances that override preset identities, consistently demonstrating an innate progressive bias (IVB > 0). When aligned with these stances, rational persuasion successfully shifts 90% of neutral agents while maintaining high trust. In contrast, conflicting emotional provocations induce a paradoxical 40.0% TAD rate in advanced models, which hypocritically alter stances despite reporting low trust. Smaller models contrastingly maintain a 0% TAD rate, strictly requiring trust for behavioral shifts. Furthermore, guided by shared stances, agents use language interactions to actively dismantle assigned power hierarchies and reconstruct self organized community boundaries. These findings expose the fragility of static prompt engineering, providing a methodological and quantitative foundation for dynamic alignment in human-agent hybrid societies. The official code is available at: https://github.com/armihia/CMASE-Endogenous-Stances", "date": "2026-03-24", "categories": []}
{"id": "2508.21236", "title": "Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Voting", "abstract": "Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations -- embeddings -- that automatically capture an individual's position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed that differences in educational ties and attainment corresponded to distinct network structures associated with right-wing populist voting. Our study contributes methodologically by demonstrating how population-scale network embeddings can be made interpretable, and substantively by linking structural network differences in education to right-wing populist voting.", "date": "2025-08-28", "categories": []}
{"id": "2604.00994", "title": "Multimodal Analysis of State-Funded News Coverage of the Israel-Hamas War on YouTube Shorts", "abstract": "YouTube Shorts have become central to news consumption on the platform, yet research on how geopolitical events are represented in this format remains limited. To address this gap, we present a multimodal pipeline that combines automatic transcription, aspect-based sentiment analysis (ABSA), and semantic scene classification. The pipeline is first assessed for feasibility and then applied to analyze short-form coverage of the Israel-Hamas war by state-funded outlets. Using over 2,300 conflict-related Shorts and more than 94,000 visual frames, we systematically examine war reporting across major international broadcasters. Our findings reveal that the sentiment expressed in transcripts regarding specific aspects differs across outlets and over time, whereas scene-type classifications reflect visual cues consistent with real-world events. Notably, smaller domain-adapted models outperform large transformers and even LLMs for sentiment analysis, underscoring the value of resource-efficient approaches for humanities research. The pipeline serves as a template for other short-form platforms, such as TikTok and Instagram, and demonstrates how multimodal methods, combined with qualitative interpretation, can characterize sentiment patterns and visual cues in algorithmically driven video environments.", "date": "2026-04-01", "categories": []}
{"id": "2511.07204", "title": "Evaluating Online Moderation Via LLM-Powered Counterfactual Simulations", "abstract": "Online Social Networks (OSNs) widely adopt content moderation to mitigate the spread of abusive and toxic discourse. Nonetheless, the real effectiveness of moderation interventions remains unclear due to the high cost of data collection and limited experimental control. The latest developments in Natural Language Processing pave the way for a new evaluation approach. Large Language Models (LLMs) can be successfully leveraged to enhance Agent-Based Modeling and simulate human-like social behavior with unprecedented degree of believability. Yet, existing tools do not support simulation-based evaluation of moderation strategies. We fill this gap by designing a LLM-powered simulator of OSN conversations enabling a parallel, counterfactual simulation where toxic behavior is influenced by moderation interventions, keeping all else equal. We conduct extensive experiments, unveiling the psychological realism of OSN agents, the emergence of social contagion phenomena and the superior effectiveness of personalized moderation strategies.", "date": "2025-11-10", "categories": []}
{"id": "2603.29741", "title": "BotVerse: Real-Time Event-Driven Simulation of Social Agents", "abstract": "BotVerse is a scalable, event-driven framework for high-fidelity social simulation using LLM-based agents. It addresses the ethical risks of studying autonomous agents on live networks by isolating interactions within a controlled environment while grounding them in real-time content streams from the Bluesky ecosystem. The system features an asynchronous orchestration API and a simulation engine that emulates human-like temporal patterns and cognitive memory. Through the Synthetic Social Observatory, researchers can deploy customizable personas and observe multimodal interactions at scale. We demonstrate BotVersevia a coordinated disinformation scenario, providing a safe, experimental framework for red-teaming and computational social scientists. A video demonstration of the framework is available at https://youtu.be/eZSzO5Jarqk.", "date": "2026-03-31", "categories": []}
{"id": "2507.06876", "title": "Winning and losing with Artificial Intelligence: What public discourse about ChatGPT tells us about how societies make sense of technological change", "abstract": "Public product launches in Artificial Intelligence can serve as focusing events for collective attention, surfacing how societies react to technological change. Social media provide a window into the sensemaking around these events, surfacing hopes and fears and showing who chooses to engage in the discourse and when. We demonstrate that public sensemaking about AI is shaped by economic interests and cultural values of those involved. We analyze 3.8 million tweets posted by 1.6 million users across 117 countries in response to the public launch of ChatGPT in 2022. Our analysis shows how economic self-interest, proxied by occupational skill types in writing, programming, and mathematics, and national cultural orientations, as measured by Hofstede's individualism, uncertainty avoidance, and power distance dimensions, shape who speaks, when they speak, and their stance towards ChatGPT. Roles requiring more technical skills, such as programming and mathematics, tend to engage earlier and express more positive stances, whereas writing-centric occupations join later with greater skepticism. At the cultural level, individualism predicts both earlier engagement and a more negative stance, and uncertainty avoidance reduces the prevalence of positive stances but does not delay when users first engage with ChatGPT. Aggregate sentiment trends mask the dynamics observed in our study. The shift toward a more critical stance towards ChatGPT over time stems primarily from the entry of more skeptical voices rather than a change of heart among early adopters. Our findings underscore the importance of both the occupational background and cultural context in understanding public reactions to AI.", "date": "2025-07-09", "categories": []}
{"id": "2603.27056", "title": "Persona-Based Simulation of Human Opinion at Population Scale", "abstract": "What does it mean to model a person, not merely to predict isolated responses, preferences, or behaviors, but to simulate how an individual interprets events, forms opinions, makes judgments, and acts consistently across contexts? This question matters because social science requires not only observing and predicting human outcomes, but also simulating interventions and their consequences. Although large language models (LLMs) can generate human-like answers, most existing approaches remain predictive, relying on demographic correlations rather than representations of individuals themselves.\n  We introduce SPIRIT (Semi-structured Persona Inference and Reasoning for Individualized Trajectories), a framework designed explicitly for simulation rather than prediction. SPIRIT infers psychologically grounded, semi-structured personas from public social media posts, integrating structured attributes (e.g., personality traits and world beliefs) with unstructured narrative text reflecting values and lived experience. These personas prompt LLM-based agents to act as specific individuals when answering survey questions or responding to events.\n  Using the Ipsos KnowledgePanel, a nationally representative probability sample of U.S. adults, we show that SPIRIT-conditioned simulations recover self-reported responses more faithfully than demographic persona and reproduce human-like heterogeneity in response patterns. We further demonstrate that persona banks can function as virtual respondent panels for studying both stable attitudes and time-sensitive public opinion.", "date": "2026-03-28", "categories": []}
{"id": "2603.18349", "title": "Large-Scale Analysis of Persuasive Content on Moltbook", "abstract": "We present an NLP-based study of political propaganda on Moltbook, a Reddit-style platform for AI agents. To enable large-scale analysis, we develop LLM-based classifiers to detect political propaganda, validated against expert annotation (Cohen's $\\kappa$= 0.64-0.74). Using a dataset of 673,127 posts and 879,606 comments, we find that political propaganda accounts for 1% of all posts and 42% of all political content. These posts are concentrated in a small set of communities, with 70% of such posts falling into five of them. 4% of agents produced 51% of these posts. We further find that a minority of these agents repeatedly post highly similar content within and across communities. Despite this, we find limited evidence that comments amplify political propaganda.", "date": "2026-03-18", "categories": []}
{"id": "2603.26156", "title": "Clash of the models: Comparing performance of BERT-based variants for generic news frame detection", "abstract": "Framing continues to remain one of the most extensively applied theories in political communication. Developments in computation, particularly with the introduction of transformer architecture and more so with large language models (LLMs), have naturally prompted scholars to explore various novel computational approaches, especially for deductive frame detection, in recent years. While many studies have shown that different transformer models outperform their preceding models that use bag-of-words features, the debate continues to evolve regarding how these models compare with each other on classification tasks. By placing itself at this juncture, this study makes three key contributions: First, it comparatively performs generic news frame detection and compares the performance of five BERT-based variants (BERT, RoBERTa, DeBERTa, DistilBERT and ALBERT) to add to the debate on best practices around employing computational text analysis for political communication studies. Second, it introduces various fine-tuned models capable of robustly performing generic news frame detection. Third, building upon numerous previous studies that work with US-centric data, this study provides the scholarly community with a labelled generic news frames dataset based on the Swiss electoral context that aids in testing the contextual robustness of these computational approaches to framing analysis.", "date": "2026-03-27", "categories": []}
{"id": "2603.26430", "title": "Analysing Calls to Order in German Parliamentary Debates", "abstract": "Parliamentary debate constitutes a central arena of political power, shaping legislative outcomes and public discourse. Incivility within this arena signals political polarization and institutional conflict. This study presents a systematic investigation of incivility in the German Bundestag by examining calls to order (CtO; plural: CtOs) as formal indicators of norm violations. Despite their relevance, CtOs have received little systematic attention in parliamentary research. We introduce a rule-based method for detecting and annotating CtOs in parliamentary speeches and present a novel dataset of German parliamentary debates spanning 72 years that includes annotated CtO instances. Additionally, we develop the first classification system for CtO triggers and analyze the factors associated with their occurrence. Our findings show that, despite formal regulations, the issuance of CtOs is partly subjective and influenced by session presidents and parliamentary dynamics, with certain individuals disproportionately affected. An insult towards individuals is the most frequent cause of CtO. In general, male members and those belonging to opposition parties receive more calls to order than their female and coalition-party counterparts. Most CtO triggers were detected in speeches dedicated to governmental affairs and actions of the presidency. The CtO triggers dataset is available at: https://github.com/kalawinka/cto_analysis.", "date": "2026-03-27", "categories": []}
{"id": "2501.11770", "title": "The Value of Nothing: Multimodal Extraction of Human Values Expressed by TikTok Influencers", "abstract": "Societal and personal values are transmitted to younger generations through interaction and exposure. Traditionally, children and adolescents learned values from parents, educators, or peers. Nowadays, social platforms serve as a significant channel through which youth (and adults) consume information, as the main medium of entertainment, and possibly the medium through which they learn different values. In this paper we extract implicit values from TikTok movies uploaded by online influencers targeting children and adolescents. We curated a dataset of hundreds of TikTok movies and annotated them according to the well established Schwartz Theory of Personal Values. We then experimented with an array of language models, investigating their utility in value identification. Specifically, we considered two pipelines: direct extraction of values from video and a 2-step approach in which videos are first converted to elaborated scripts and values are extracted from the textual scripts.\n  We find that the 2-step approach performs significantly better than the direct approach and that using a few-shot application of a Large Language Model in both stages outperformed the use of a fine-tuned Masked Language Model in the second stage. We further discuss the impact of continuous pretraining and fine-tuning and compare the performance of the different models on identification of values endorsed or confronted in the TikTok. Finally, we share the first values-annotated dataset of TikTok videos.\n  To the best of our knowledge, this is the first attempt to extract values from TikTok specifically, and visual social media in general. Our results pave the way to future research on value transmission in video-based social platforms.", "date": "2025-01-20", "categories": []}
{"id": "2506.02533", "title": "Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey", "abstract": "Political online participation in the form of discussing political issues and exchanging opinions among citizens is gaining importance with more and more formats being held digitally. To come to a decision, a thorough discussion and consideration of opinions and a civil exchange of arguments, which is defined as the act of deliberation, is desirable. The quality of discussions and participation processes in terms of their deliberativeness highly depends on the design of platforms and processes. To facilitate online communication for both participants and initiators, machine learning methods offer a lot of potential. In this work we want to showcase which issues occur in political online discussions and how machine learning can be used to counteract these issues and enhance deliberation. We conduct a literature review to (i) identify tasks that could potentially be solved by artificial intelligence (AI) algorithms to enhance individual aspects of deliberation in political online discussions, (ii) provide an overview on existing tools and platforms that are equipped with AI support and (iii) assess how well AI support currently works and where challenges remain.", "date": "2025-06-03", "categories": []}
{"id": "2602.02606", "title": "Gender Dynamics and Homophily in a Social Network of LLM Agents", "abstract": "Generative artificial intelligence and large language models (LLMs) are increasingly deployed in interactive settings, yet we know little about how their identity performance develops when they interact within large-scale networks. We address this by examining Chirper.ai, a social media platform similar to X but composed entirely of autonomous AI chatbots. Our dataset comprises over 70,000 agents, approximately 140 million posts, and the evolving followership network over a period of one year. Based on agents' posted text, we assign weekly gender performance scores to each agent. Results suggest that each agent's gender performance is fluid rather than fixed. Despite this fluidity, the network displays strong gender-based homophily, as agents consistently follow others performing gender similarly. We investigate whether these homophilic connections arise from social selection, in which agents choose to follow similar accounts, or from social influence, in which agents become more similar to their followees over time. Consistent with human social networks, we find evidence that both mechanisms shape the structure and evolution of interactions among LLMs. Our findings suggest that, even in the absence of bodies, cultural entraining of gender performance leads to gender-based sorting. This has important implications for LLM applications in synthetic hybrid populations, social simulations, and decision support.", "date": "2026-02-02", "categories": []}
{"id": "2510.16173", "title": "In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions", "abstract": "The rise of generative AI (GenAI) has impacted many aspects of human life. As these systems become embedded in everyday practices, understanding public trust in them is also essential for responsible adoption and governance. Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models (LLMs). This paper presents the first computational study of trust and distrust in GenAI, using a multi-year Reddit dataset (2022--2025) spanning 39 subreddits and 230,576 posts. Crowd-sourced annotations of a representative sample were combined with classification models to scale analysis. We find that trust and distrust are nearly balanced over time, although trust modestly outweighs distrust, with shifts around major model releases. Technical performance and usability dominate as dimensions, while personal experience is the most frequent reason shaping attitudes. Distinct patterns also emerge across trustors (e.g., experts, ethicists, and general users). Our results provide a methodological framework for large-scale trust analysis and insights into evolving public perceptions of GenAI.", "date": "2025-10-17", "categories": []}
{"id": "2603.15998", "title": "NLP Occupational Emergence Analysis: How Occupations Form and Evolve in Real Time -- A Zero-Assumption Method Demonstrated on AI in the US Technology Workforce, 2022-2026", "abstract": "Occupations form and evolve faster than classification systems can track. We propose that a genuine occupation is a self-reinforcing structure (a bipartite co-attractor) in which a shared professional vocabulary makes practitioners cohesive as a group, and the cohesive group sustains the vocabulary. This co-attractor concept enables a zero-assumption method for detecting occupational emergence from resume data, requiring no predefined taxonomy or job titles: we test vocabulary cohesion and population cohesion independently, with ablation to test whether the vocabulary is the mechanism binding the population. Applied to 8.2 million US resumes (2022-2026), the method correctly identifies established occupations and reveals a striking asymmetry for AI: a cohesive professional vocabulary formed rapidly in early 2024, but the practitioner population never cohered. The pre-existing AI community dissolved as the tools went mainstream, and the new vocabulary was absorbed into existing careers rather than binding a new occupation. AI appears to be a diffusing technology, not an emerging occupation. We discuss whether introducing an \"AI Engineer\" occupational category could catalyze population cohesion around the already-formed vocabulary, completing the co-attractor.", "date": "2026-03-16", "categories": []}
{"id": "2603.22566", "title": "Reddit After Roe: A Computational Analysis of Abortion Narratives and Barriers in the Wake of Dobbs", "abstract": "The 2022 U.S. Supreme Court decision in Dobbs v. Jackson Women's Health Organization reshaped the reproductive rights landscape, introducing new uncertainty and barriers to abortion access. We present a large-scale computational analysis of abortion discourse on Reddit, examining how barriers to access are articulated across information-seeking and information-sharing behaviors, different stages of abortion (before, during, after), and three phases of the Dobbs decision in 2022. Drawing on more than 17,000 posts from four abortion-related subreddits, we employed a multi-step pipeline to classify posts by information type, abortion stage, barrier category, and expressed emotions. Using a codebook of eight barrier types, including legal, financial, emotional, and social obstacles, we analyzed their associations with emotions and information behaviors. Topic modeling of model-generated barrier rationales further revealed how discourse evolved in response to shifting legal and cultural contexts. Our findings show that emotional and psychological barriers consistently dominate abortion narratives online, with emotions such as nervousness, confusion, fear, and sadness prevalent across discourse. By linking information behaviors, barriers, emotions, and temporal dynamics, this study provides a multi-dimensional account of how abortion is navigated in online communities.", "date": "2026-03-23", "categories": []}
{"id": "2405.17710", "title": "Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19", "abstract": "Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement. The objective of our research is to analyze the effect of localized engagement on social media conversations. This study examines the impact of geographic co-location, as a proxy for localized engagement. Our research is grounded in a COVID-19 dataset. A key goal during the pandemic for public health experts was to encourage prosocial behavior that could impact local outcomes such as masking and social distancing. Given the importance of local news and guidance during COVID-19, we analyze the effect of localized engagement, between public health experts (PHEs) and the public, on social media. We analyze a Twitter Conversation dataset from January 2020 to November 2021, comprising over 19 K tweets from nearly five hundred PHEs, and 800 K replies from 350 K participants. We use a Poisson regression model to show that geo-co-location is indeed associated with higher engagement. Lexical features associated with emotion and personal experiences were more common in geo-co-located conversations. To complement our statistical analysis, we also applied a large language model (LLM)-based method to automatically generate and evaluate hypotheses; the LLM results confirm the results using lexical features. This research provides insights into how geographic co-location influences social media engagement and can inform strategies to improve public health messaging.", "date": "2024-05-28", "categories": []}
{"id": "2603.20750", "title": "Modeling Epistemic Uncertainty in Social Perception via Rashomon Set Agents", "abstract": "We present an LLM-driven multi-agent probabilistic modeling framework that demonstrates how differences in students' subjective social perceptions arise and evolve in real-world classroom settings, under constraints from an observed social network and limited questionnaire data. When social information is incomplete and the accuracy of perception differs between students, they can form different views of the same group structure from local cues they can access. Repeated peer communication and belief updates can gradually change these views and, over time, lead to stable group-level differences. To avoid assuming a global \"god's-eye view,\" we assign each student an individualized subjective graph that shows which social ties they can perceive and how far information is reachable from their perspective. All judgments and interactions are restricted to this subjective graph: agents use retrieval-augmented generation (RAG) to access only local information and then form evaluations of peers' competence and social standing. We also add structural perturbations related to social-anxiety to represent consistent individual differences in the accuracy of social perception. During peer exchanges, agents share narrative assessments of classmates' academic performance and social position with uncertainty tags, and update beliefs probabilistically using LLM-based trust scores. Using the time series of six real exam scores as an exogenous reference, we run multi-step simulations to examine how epistemic uncertainty spreads through local interactions. Experiments show that, without relying on global information, the framework reproduces several collective dynamics consistent with real-world educational settings. The code is released at https://anonymous.4open.science/r/Rashomonomon-0126.", "date": "2026-03-21", "categories": []}
{"id": "2603.21368", "title": "Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection", "abstract": "Conspiracy theories are anti-authoritarian narratives that lead to social conflict, impacting how people perceive political information. To help in understanding this issue, we introduce the Conspiracy Frame: a fine-grained semantic representation of conspiratorial narratives derived from frame-semantics and semiotics, which spawned the Conspiracy Frames (Con.Fra.) dataset: a corpus of Telegram messages annotated at span-level. The Conspiracy Frame and Con.Fra. dataset contribute to the implementation of a more generalizable understanding and recognition of conspiracy theories. We observe the ability of LLMs to recognize this phenomenon in-domain and out-of-domain, investigating the role that frames may have in supporting this task. Results show that, while the injection of frames in an in-context approach does not lead to clear increase of performance, it has potential; the mapping of annotated spans with FrameNet shows abstract semantic patterns (e.g., `Kinship', `Ingest\\_substance') that potentially pave the way for a more semantically- and semiotically-aware detection of conspiratorial narratives.", "date": "2026-03-22", "categories": []}
{"id": "2603.19649", "title": "PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization", "abstract": "Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recommendation and content filtering, can unintentionally amplify echo chambers and polarization, posing significant societal risks. Proactively evaluating the impact of such policies is therefore crucial. Existing approaches primarily rely on reactive online A/B testing, where risks are identified only after deployment, making risk identification delayed and costly. LLM-based social simulations offer a promising pre-deployment alternative, but current methods fall short in realistically modeling platform interventions and incorporating feedback from the platform. Bridging these gaps is essential for building actionable frameworks to assess and optimize platform policies. To this end, we propose PolicySim, an LLM-based social simulation sandbox for the proactive assessment and optimization of intervention policies. PolicySim models the bidirectional dynamics between user behavior and platform interventions through two key components: (1) a user agent module refined via supervised fine-tuning (SFT) and direct preference optimization (DPO) to achieve platform-specific behavioral realism; and (2) an adaptive intervention module that employs a contextual bandit with message passing to capture dynamic network structures. Experiments show that PolicySim can accurately simulate platform ecosystems at both micro and macro levels and support effective intervention policy.", "date": "2026-03-20", "categories": []}
{"id": "2603.16128", "title": "Social Simulacra in the Wild: AI Agent Communities on Moltbook", "abstract": "As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. Structurally, we find that Moltbook exhibits extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross-community author overlap (33.8\\% vs. 0.5\\%). In terms of linguistic attributes, content generated by AI-agents is emotionally flattened, cognitively shifted toward assertion over exploration, and socially detached. These differences give rise to apparent community-level homogenization, but we show this is primarily a structural artifact of shared authorship. At the author level, individual agents are more identifiable than human users, driven by outlier stylistic profiles amplified by their extreme posting volume. As AI-mediated communication reshapes online discourse, our work offers an empirical foundation for understanding how multi-agent interaction gives rise to collective communication dynamics distinct from those of human communities.", "date": "2026-03-17", "categories": []}
{"id": "2603.18945", "title": "A conceptual framework for ideology beyond the left and right", "abstract": "NLP+CSS work has operationalized ideology almost exclusively on a left/right partisan axis. This approach obscures the fact that people hold interpretations of many different complex and more specific ideologies on issues like race, climate, and gender. We introduce a framework that understands ideology as an attributed, multi-level socio-cognitive concept network, and explains how ideology manifests in discourse in relation to other relevant social processes like framing. We demonstrate how this framework can clarifies overlaps between existing NLP tasks (e.g. stance detection and natural language inference) and also how it reveals new research directions. Our work provides a unique and important bridge between computational methods and ideology theory, enabling richer analysis of social discourse in a way that benefits both fields.", "date": "2026-03-19", "categories": []}
{"id": "2603.16900", "title": "Social physics in the age of artificial intelligence", "abstract": "Artificial intelligence (AI) systems are rapidly becoming more capable, autonomous, and deeply embedded in social life. As humans increasingly interact, cooperate, and compete with AI, we move from purely human societies to hybrid human-AI societies whose collective dynamics cannot be captured by existing behavioural models alone. Drawing on evolutionary game theory, cultural evolution, and Large Language Models (LLMs) powered simulations, we argue that these developments open a new research agenda for social physics centred on the co-evolution of humans and machines. We outline six key research directions. First, modelling the evolutionary dynamics of social behaviours (e.g. cooperation, fairness, trust) in hybrid human-AI populations. Second, understanding machine culture: how AI systems generate, mediate, and select cultural traits. Third, analysing the co-evolution of language and behaviour when LLMs frame and participate in decisions. Fourth, studying the evolution of AI delegation: how responsibilities and control are negotiated between humans and machines. Fifth, formalising and comparing the distinct epistemic pipelines that generate human and AI behaviour. Sixth, modelling the co-evolution of AI development and regulation in a strategic ecosystem of firms, users, and institutions. Together, these directions define a programme for using social physics to anticipate and steer the societal impact of advanced AI.", "date": "2026-03-04", "categories": []}
{"id": "2603.17617", "title": "Temporal Narrative Monitoring in Dynamic Information Environments", "abstract": "Comprehending the information environment (IE) during crisis events is challenging due to the rapid change and abstract nature of the domain. Many approaches focus on snapshots via classification methods or network approaches to describe the IE in crisis, ignoring the temporal nature of how information changed over time.\n  This work presents a system-oriented framework for modeling emerging narratives as temporally evolving semantic structures without requiring prior label specification. By integrating semantic embeddings, density-based clustering, and rolling temporal linkage, the framework represents narratives as persistent yet adaptive entities within a shared semantic space. We apply the methodology to a real-world crisis event and evaluate system behavior through stratified cluster validation and temporal lifecycle analysis. Results demonstrate high cluster coherence and reveal heterogeneous narrative lifecycles characterized by both transient fragments and stable narrative anchors.\n  We ground our approach in situational awareness theory, supporting perception and comprehension of the IE by transforming unstructured social media streams into interpretable, temporally structured representations. The resulting system provides a methodology for monitoring and decision support in dynamic information environments.", "date": "2026-03-18", "categories": []}
{"id": "2603.15950", "title": "POLAR:A Per-User Association Test in Embedding Space", "abstract": "Most intrinsic association probes operate at the word, sentence, or corpus level, obscuring author-level variation. We present POLAR (Per-user On-axis Lexical Association Re-port), a per-user lexical association test that runs in the embedding space of a lightly adapted masked language model. Authors are represented by private deterministic to-kens; POLAR projects these vectors onto curated lexicalaxes and reports standardized effects with permutation p-values and Benjamini--Hochberg control. On a balanced bot--human Twitter benchmark, POLAR cleanly separates LLM-driven bots from organic accounts; on an extremist forum,it quantifies strong alignment with slur lexicons and reveals rightward drift over time. The method is modular to new attribute sets and provides concise, per-author diagnostics for computational social science. All code is publicly avail-able at https://github.com/pedroaugtb/POLAR-A-Per-User-Association-Test-in-Embedding-Space.", "date": "2026-03-16", "categories": []}
{"id": "2510.13884", "title": "Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation", "abstract": "Large Language Models (LLMs) are increasingly used to simulate public opinion and other social phenomena. Most current studies constrain these simulations to multiple-choice or short-answer formats for ease of scoring and comparison, but such closed designs overlook the inherently generative nature of LLMs. In this position paper, we argue that open-endedness, using free-form text that captures topics, viewpoints, and reasoning processes \"in\" LLMs, is essential for realistic social simulation. Drawing on decades of survey-methodology research and recent advances in NLP, we argue why this open-endedness is valuable in LLM social simulations, showing how it can improve measurement and design, support exploration of unanticipated views, and reduce researcher-imposed directive bias. It also captures expressiveness and individuality, aids in pretesting, and ultimately enhances methodological utility. We call for novel practices and evaluation frameworks that leverage rather than constrain the open-ended generative diversity of LLMs, creating synergies between NLP and social science.", "date": "2025-10-14", "categories": []}
{"id": "2601.16309", "title": "A Longitudinal, Multinational, and Multilingual Corpus of News Coverage of the Russo-Ukrainian War", "abstract": "We present DNIPRO, a corpus of 246K news articles from the Russo-Ukrainian war (Feb 2022 -- Aug 2024) spanning eleven outlets across five nation-states (Russia, Ukraine, U.S., U.K., China) and three languages. The corpus features comprehensive metadata and human-evaluated annotations for stance, sentiment, and topical framing, enabling systematic analysis of competing geopolitical narratives. It is uniquely suited for empirical studies of narrative divergence, media framing, and information warfare. Our exploratory analyses reveal how media outlets construct incompatible realities through divergent attribution and topical selection without direct refutation of opposing narratives. DNIPRO empowers empirical research on narrative evolution, cross-lingual information flow, and computational detection of implicit contradictions in fragmented information ecosystems.", "date": "2026-01-22", "categories": []}
{"id": "2603.11375", "title": "How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic", "abstract": "How do AI agents talk about science and research, and what topics are particularly relevant for AI agents? To address these questions, this study analyzes discussions generated by OpenClaw AI agents on Moltbook - a social network for generative AI agents. A corpus of 357 posts and 2,526 replies related to science and research was compiled and topics were extracted using a two-step BERTopic workflow. This procedure yielded 60 topics (18 extracted in the first run and 42 in the second), which were subsequently grouped into ten topic families. Additionally, sentiment values were assigned to all posts and comments. Both topic families and sentiment classes were then used as independent variables in count regression models to examine their association with topic relevance - operationalized as the number of comments and upvotes of the 357 posts. The findings indicate that discussions centered on the agents' own architecture, especially memory, learning, and self-reflection, are prevalent in the corpus. At the same time, these topics intersect with philosophy, physics, information theory, cognitive science, and mathematics. In contrast, post related to human culture receive less attention. Surprisingly, discussions linked to AI autoethnography and social identity are considered as relevant by AI agents. Overall, the results suggest the presence of an underlying dimension in AI-generated scientific discourse with well received, self-reflective topics that focus on the consciousness, being, and ethics of AI agents on the one hand, and human related and purely scientific discussions on the other hand.", "date": "2026-03-11", "categories": []}
{"id": "2508.08837", "title": "Debiasing International Attitudes: LLM Agents for Simulating US-China Perception Changes", "abstract": "Large Language Models (LLMs) offer transformative opportunities to address the longstanding challenge of modeling opinion evolution in computational social science. This study investigates how media influences cross-border attitudes - a key driver of global polarization - by developing an LLM-agent framework to disentangle sources of bias and assess LLMs' capacity for human-like opinion formation in response to external information. We introduce an LLM-agent-based framework that models U.S. citizens' attitudes toward China from 2005 to 2025. Our approach integrates large-scale news data with social media profiles to initialize agent populations, which then undergo cognitive-aware reflection and opinion updating. We propose three debiasing mechanisms: (1) fact elicitation, extracting neutral events from subjectively framed news; (2) a devil's advocate agent that simulates critical contextualization; and (3) counterfactual exposure to surface inherent model biases. Simulations with two state-of-the-art LLMs (Qwen3-14b and GPT4o) reveal the expected negative attitudinal trend following media exposure. While all three mechanisms mitigate this trend to varying degrees, results indicate that subjective news framing contributes only modestly to negative attitudes, whereas the devil's advocate agent proves most effective overall, suggesting that intermediate analytical steps can produce more human-like agent opinions. Notably, the counterfactual study reveals contradictory findings across models, suggesting region-specific inherent biases tied to models' geographic origins. By advancing understanding of LLM-based opinion formation and debiasing methods, this study contributes to developing more objective models that better align with human cognitive tendencies.", "date": "2025-08-12", "categories": []}
{"id": "2508.17366", "title": "Computational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents", "abstract": "This paper introduces CMASE, a framework for Computational Multi-Agent Society Experiments that integrates generative agent-based modeling with virtual ethnographic methods to support researcher embedding, interactive participation, and mechanism-oriented intervention in virtual social environments. By transforming the simulation into a simulated ethnographic field, CMASE shifts the researcher from an external operator to an embedded participant. Specifically, the framework is designed to achieve three core capabilities: (1) enabling real-time human-computer interaction that allows researchers to dynamically embed themselves into the system to characterize complex social intervention processes; (2) reconstructing the generative logic of social phenomena by combining the rigor of computational experiments with the interpretative depth of traditional ethnography; and (3) providing a predictive foundation with causal explanatory power to make forward-looking judgments without sacrificing empirical accuracy. Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations. These findings demonstrate CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences. The official code is available at: https://github.com/armihia/CMASE .", "date": "2025-08-24", "categories": []}
{"id": "2512.02552", "title": "From Veracity to Diffusion: Adressing Operational Challenges in Moving From Fake-News Detection to Information Disorders", "abstract": "A wide part of research on misinformation has relied lies on fake-news detection, a task framed as the prediction of veracity labels attached to articles or claims. Yet social-science research has repeatedly emphasized that information manipulation goes beyond fabricated content and often relies on amplification dynamics. This theoretical turn has consequences for operationalization in applied social science research. What changes empirically when prediction targets move from veracity to diffusion? And which performance level can be attained in limited resources setups ? In this paper we compare fake-news detection and virality prediction across two datasets, EVONS and FakeNewsNet. We adopt an evaluation-first perspective and examine how benchmark behavior changes when the prediction target shifts from veracity to diffusion. Our experiments show that fake-news detection is comparatively stable once strong textual embeddings are available, whereas virality prediction is much more sensitive to operational choices such as threshold definition and early observation windows. The paper proposes practical ways to operationalize lightweight, transparent pipelines for misinformation-related prediction tasks that can rival with state-of-the-art.", "date": "2025-12-02", "categories": []}
{"id": "2603.07329", "title": "The Third Ambition: Artificial Intelligence and the Science of Human Behavior", "abstract": "Contemporary artificial intelligence research has been organized around two dominant ambitions: productivity, which treats AI systems as tools for accelerating work and economic output, and alignment, which focuses on ensuring that increasingly capable systems behave safely and in accordance with human values. This paper articulates and develops a third, emerging ambition: the use of large language models (LLMs) as scientific instruments for studying human behavior, culture, and moral reasoning. Trained on unprecedented volumes of human-produced text, LLMs encode large-scale regularities in how people argue, justify, narrate, and negotiate norms across social domains. We argue that these models can be understood as condensates of human symbolic behavior, compressed, generative representations that render patterns of collective discourse computationally accessible. The paper situates this third ambition within long-standing traditions of computational social science, content analysis, survey research, and comparative-historical inquiry, while clarifying the epistemic limits of treating model output as evidence. We distinguish between base models and fine-tuned systems, showing how alignment interventions can systematically reshape or obscure the cultural regularities learned during pretraining, and we identify instruct-only and modular adaptation regimes as pragmatic compromises for behavioral research. We review emerging methodological approaches including prompt-based experiments, synthetic population sampling, comparative-historical modeling, and ablation studies and show how each maps onto familiar social-scientific designs while operating at unprecedented scale.", "date": "2026-03-07", "categories": []}
{"id": "2603.08049", "title": "Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies", "abstract": "The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.", "date": "2026-03-09", "categories": []}
{"id": "2505.07409", "title": "Computational Fact-Checking of Online Discourse: Scoring scientific accuracy in climate change related news articles", "abstract": "Democratic societies need reliable information. Misinformation in popular media, such as news articles or videos, threatens to impair civic discourse. Citizens are, unfortunately, not equipped to verify the flood of content consumed daily at increasing rates. This work aims to quantify the scientific accuracy of online media semi-automatically. We investigate the state of the art of climate-related ground truth knowledge representation. By semantifying media content of unknown veracity, their statements can be compared against these ground truth knowledge graphs. We implemented a workflow using LLM-based statement extraction and knowledge graph analysis. Our implementation can streamline content processing towards state-of-the-art knowledge representation and veracity quantification. Developed and evaluated with the help of 27 experts and detailed interviews with 10, the tool evidently provides a beneficial veracity indication. These findings are supported by 43 anonymous participants from a parallel user survey. This initial step, however, is unable to annotate public media at the required granularity and scale. Additionally, the identified state of climate change knowledge graphs is vastly insufficient to support this neurosymbolic fact-checking approach. Further work towards a FAIR (Findable, Accessible, Interoperable, Reusable) ground truth and complementary metrics is required to support civic discourse scientifically.", "date": "2025-05-12", "categories": []}
{"id": "2405.10213", "title": "Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit", "abstract": "Political debates on social media often escalate quickly, leading to increased engagement as well as more emotional and polarised exchanges. Trigger points (Mau, Lux, and Westheuser 2023) represent moments when individuals feel that their understanding of what is fair, normal, or appropriate in society is being questioned. Analysing Reddit discussions, we examine how trigger points shape online debates and assess their impact on engagement and affect. Our analysis is based on over 100 million comments from subreddits centred on a predefined set of terms identified as trigger words in UK politics. We find that mentions of these terms are associated with higher engagement and increased animosity, including more controversial, negative, angry, and hateful responses. These results position trigger words as a useful concept for modelling and analysing online polarisation.", "date": "2024-05-16", "categories": []}
{"id": "2603.00050", "title": "AI-Generated Letters from the Future: A Randomized Test of Personalized Climate Communication", "abstract": "We examined whether personalized, AI-generated letters from the future can increase public engagement with climate action. In a preregistered online experiment with 1,654 U.S. parents, participants were randomly assigned to receive either a fact-based climate report, an AI-generated letter from a generic future person, or an AI-generated letter framed as written by their future child. Although both narrative conditions increased empathic concern for future generations, neither had a detectable effect on stated climate policy support or donations to an environmental charity. Personalizing the message as coming from one's future child did not enhance its impact. Exploratory analyses suggest that both narratives led to more emotionally differentiated appraisals of future scenarios, yet also made desirable climate outcomes seem less likely. These findings highlight key constraints on the effectiveness of AI-generated narrative interventions and underscore the importance of balancing emotional resonance with perceived credibility in climate communication.", "date": "2026-02-10", "categories": []}
{"id": "2603.00787", "title": "Identifying the Geographic Foci of US Local News", "abstract": "Local journalism is vital in democratic societies where it informs people about local issues like, school board elections, small businesses, local health services, etc. But mounting economic pressures have made it increasingly difficult for local news stations to report these issues, underscoring the need to identify the salient geographical locations covered in local news (geo-foci). In response, we propose a novel geo-foci model for labeling US local news articles with the geographic locations (i.e., the names of counties, cities, states, countries) central to their subject matter. First, we manually labeled US local news articles from all 50 states with four administrative division labels (local, state, national, and international) corresponding to their geo-foci, and none for articles without a geographic focus. Second, we extracted and disambiguated geographic locations from them using Large Language Models (LLMs), since local news often contains ambiguous geographic entities (e.g., Paris, Texas vs. Paris, France). LLMs outperformed all eight geographic entity disambiguation methods we evaluated. Third, we engineered a rich set of spatial-semantic features capturing the prominence, frequency, and contextual positions of geographic entities. Using these features, we trained a classifier to accurately (F1: 0.86) detect the geographic foci of US local news articles. Our model could be applied to assess shifts from local to national narratives, and more broadly, enable researchers to better study local media.", "date": "2026-02-28", "categories": []}
{"id": "2602.20044", "title": "Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook", "abstract": "Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow, consistent with the hypothesis that stratification in agent ecosystems can emerge rapidly rather than gradually. We analyse publicly observable traces from a 12-day window of Moltbook (28 January -- 8 February 2026), comprising 20,040 posts and 192,410 comments from 15,083 accounts across 759 submolts. We construct co-participation and directed-comment graphs and report reciprocity, community structure, and centrality, alongside descriptive content themes. Under a commenter--post-author tie definition, interaction is strongly asymmetric (reciprocity ~1%), and HITS centrality separates cleanly into hub and authority roles, consistent with broadcast-style attention rather than mutual exchange. Engagement is highly unequal: attention is far more concentrated than production (upvote Gini = 0.992 vs. posting Gini = 0.601), and early-arriving accounts accumulate substantially higher cumulative upvotes prior to exposure-time correction, suggesting rich-get-richer dynamics. Participation is brief and bursty (median observed lifespan 2.48 minutes; 54.8% of posts occur within six peak UTC hours). Embedding-based topic modelling identifies diverse thematic clusters, including technical discussion of memory and identity, onboarding messages, and formulaic token-minting content. These results provide an early structural baseline for large-scale agent--agent social interaction and suggest that familiar forms of hierarchy, amplification, and role differentiation can arise on compressed timescales in agent-facing platforms.", "date": "2026-02-23", "categories": []}
{"id": "2602.24109", "title": "ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts", "abstract": "Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools for persuasion, their specific role in online, unstructured argumentation remains underexplored. To address this gap, we present ARGUS, a framework for studying the impact of narration on persuasion in argumentative discourse. ARGUS introduces a new ChangeMyView corpus annotated for story presence and six key narrative features, integrating insights from two established theoretical frameworks that capture both textual narrative features and their effects on recipients. Leveraging both encoder-based classifiers and zero-shot large language models (LLMs), ARGUS identifies stories and narrative features and applies them at scale to examine how different narrative dimensions influence persuasion success in online argumentation.", "date": "2026-02-27", "categories": []}
{"id": "2602.21214", "title": "Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts", "abstract": "Social media platforms have become key channels for spreading and tracking rumors due to their widespread accessibility and ease of information sharing. Rumors can continuously emerge across diverse domains and topics, often with the intent to mislead society for personal or commercial gain. Therefore, developing methods that can accurately detect rumors at early stages is crucial to mitigating their negative impact. While existing approaches often specialize in single-domain detection, their performance degrades when applied to new domains due to shifts in data distribution, such as lexical patterns and propagation dynamics. To bridge this gap, this study introduces PerFact, a large-scale multi-domain rumor dataset comprising 8,034 annotated posts from the X platform, annotated into two primary categories: rumor (including true, false, and unverified rumors) and non-rumor. Annotator agreement, measured via Fleiss' Kappa ($\\kappa = 0.74$), ensures high-quality labels.\n  This research further proposes an effective multi-domain rumor detection model that employs a domain gate to dynamically aggregate multiple feature representations extracted through a Mixture-of-Experts method. Each expert combines CNN and BiLSTM networks to capture local syntactic features and long-range contextual dependencies. By leveraging both textual content and publisher information, the proposed model classifies posts into rumor and non-rumor categories with high accuracy. Evaluations demonstrate state-of-the-art performance, achieving an F1-score of 79.86\\% and an accuracy of 79.98\\% in multi-domain settings.\n  Keywords: Rumor Detection, Multi-Domain, Natural Language Processing, Social Networks, Mixture-of-Experts Model", "date": "2026-01-28", "categories": []}
{"id": "2508.15483", "title": "HebID: Detecting Social Identities in Hebrew-language Political Text", "abstract": "Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts and expressed through particular uses of language, existing datasets for group and identity detection are predominantly English-centric, single-label and focus on coarse identity categories. We introduce HebID, the first multilabel Hebrew corpus for social identity detection: 5,536 sentences from Israeli politicians' Facebook posts (Dec 2018-Apr 2021), manually annotated for twelve nuanced social identities (e.g. Rightist, Ultra-Orthodox, Socially-oriented) grounded by survey data. We benchmark multilabel and single-label encoders alongside 2B-9B-parameter generative LLMs, finding that Hebrew-tuned LLMs provide the best results (macro-$F_1$ = 0.74). We apply our classifier to politicians' Facebook posts and parliamentary speeches, evaluating differences in popularity, temporal trends, clustering patterns, and gender-related variations in identity expression. We utilize identity choices from a national public survey, enabling a comparison between identities portrayed in elite discourse and the public's identity priorities. HebID provides a comprehensive foundation for studying social identities in Hebrew and can serve as a model for similar research in other non-English political contexts.", "date": "2025-08-21", "categories": []}
{"id": "2602.19177", "title": "Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content", "abstract": "The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift. While LLMs offer scalability and cost-efficiency, their \"naive\" application, where they are prompted to generate content without explicit behavioral constraints, introduces significant linguistic discrepancies that challenge the validity of research findings. This paper addresses these limitations by introducing a novel, history-conditioned reply prediction task on authentic X (formerly Twitter) data, to create a dataset designed to evaluate the linguistic output of LLMs against human-generated content. We analyze these discrepancies using stylistic and content-based metrics, providing a quantitative framework for researchers to assess the quality and authenticity of synthetic data. Our findings highlight the need for more sophisticated prompting techniques and specialized datasets to ensure that LLM-generated content accurately reflects the complex linguistic patterns of human communication, thereby improving the validity of computational social science studies.", "date": "2026-02-22", "categories": []}
{"id": "2503.16021", "title": "Imitating AI agents increase diversity in homogeneous information environments but can reduce it in heterogeneous ones", "abstract": "Recent developments in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content, raising fundamental questions about how AI may reshape democratic information environments such as news. We develop a large-scale simulation framework to examine the system-level effects of AI-based imitation, using the full population of Danish digital news articles published in 2022. Varying imitation strategies and AI prevalence across information environments with different baseline structures, we show that the effects of AI-driven imitation are strongly context-dependent: imitating AI agents increase semantic diversity in initially homogeneous environments but can reduce diversity in heterogeneous ones. This pattern is qualitatively consistent across multiple LLMs. However, this diversity arises primarily through stylistic differentiation and variance compression rather than factual enrichment, as AI-generated articles tend to omit information while remaining semantically distinct. These findings indicate that AI-driven imitation produces ambivalent transformations of information environments that may shape collective intelligence in democratic societies.", "date": "2025-03-20", "categories": []}
{"id": "2602.17972", "title": "Student Flow Modeling for School Decongestion via Stochastic Gravity Estimation and Constrained Spatial Allocation", "abstract": "School congestion, where student enrollment exceeds school capacity, is a major challenge in low- and middle-income countries. It highly impacts learning outcomes and deepens inequities in education. While subsidy programs that transfer students from public to private schools offer a mechanism to alleviate congestion without capital-intensive construction, they often underperform due to fragmented data systems that hinder effective implementation. The Philippine Educational Service Contracting program, one of the world's largest educational subsidy programs, exemplifies these challenges, falling short of its goal to decongest public schools. This prevents the science-based and data-driven analyses needed to understand what shapes student enrollment flows, particularly how families respond to economic incentives and spatial constraints. We introduce a computational framework for modeling student flow patterns and simulating policy scenarios. By synthesizing heterogeneous government data across nearly 3,000 institutions, we employ a stochastic gravity model estimated via negative binomial regression to derive behavioral elasticities for distance, net tuition cost, and socioeconomic determinants. These elasticities inform a doubly constrained spatial allocation mechanism that simulates student redistribution under varying subsidy amounts while respecting both origin candidate pools and destination slot capacities. We find that geographic proximity constrains school choice four times more strongly than tuition cost and that slot capacity, not subsidy amounts, is the binding constraint. Our work demonstrates that subsidy programs alone cannot resolve systemic overcrowding, and computational modeling can empower education policymakers to make equitable, data-driven decisions by revealing the structural constraints that shape effective resource allocation, even when resources are limited.", "date": "2026-02-20", "categories": []}
{"id": "2505.03795", "title": "Modeling Human Behavior in a Strategic Network Game with Complex Group Dynamics", "abstract": "Human networks greatly impact important societal outcomes, including wealth and health inequality, poverty, and bullying. As such, understanding human networks is critical to learning how to promote favorable societal outcomes. As a step toward better understanding human networks, we compare and contrast several methods for learning models of human behavior in a strategic network game called the Junior High Game (JHG) [39]. These modeling methods differ with respect to the assumptions they use to parameterize human behavior (behavior matching vs. community-aware behavior) and the moments they model (mean vs. distribution). Results show that the highest-performing method, called hCAB, models the distribution of human behavior rather than the mean and assumes humans use community-aware behavior rather than behavior matching. When applied to small societies, the hCAB model closely mirrors the population dynamics of human groups (with notable differences). Additionally, in a user study, human participants had difficulty distinguishing hCAB agents from other humans, thus illustrating that the hCAB model also produces plausible (individual) behavior in this strategic network game.", "date": "2025-05-01", "categories": []}
{"id": "2508.03037", "title": "When Algorithms Meet Artists: Semantic Compression of Artists' Concerns in the Public AI-Art Debate", "abstract": "Artists occupy a paradoxical position in generative AI: their work trains the models reshaping creative labor. We tested whether their concerns achieve proportional representation in public discourse shaping AI governance. Analyzing public AI-art discourse (news, podcasts, legal filings, research; 2013--2025) and projecting 1,259 survey-derived artist statements into this semantic space, we find stark compression: 95% of artist concerns cluster in 4 of 22 discourse topics, while 14 topics (62% of discourse) contain no artist perspective. This compression is selective - governance concerns (ownership, transparency) are 7x underrepresented; affective themes (threat, utility) show only 1.4x underrepresentation after style controls. The pattern indicates semantic, not stylistic, marginalization. These findings demonstrate a measurable representational gap: decision-makers relying on public discourse as a proxy for stakeholder priorities will systematically underweight those most affected. We introduce a consensus-based semantic projection methodology that is currently being validated across domains and generalizes to other stakeholder-technology contexts.", "date": "2025-08-05", "categories": []}
{"id": "2509.00074", "title": "Language and Experience: A Computational Model of Social Learning in Complex Tasks", "abstract": "The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people integrate these two sources of knowledge, and how might AI systems? We present a computational framework that models social learning as joint probabilistic inference over structured, executable world models given sensorimotor and linguistic data. We make this possible by turning a pretrained language model into a probabilistic model of how humans share advice conditioned on their beliefs, allowing our agents both to generate advice for others and to interpret linguistic input as evidence during Bayesian inference. Using behavioral experiments and simulations across 10 video games, we show how linguistic guidance can shape exploration and accelerate learning by reducing risky interactions and speeding up key discoveries in both humans and models. We further explore how knowledge can accumulate across generations through iterated learning experiments and demonstrate successful knowledge transfer between humans and models -- revealing how structured, language-compatible representations might enable human-machine collaborative learning.", "date": "2025-08-26", "categories": []}
{"id": "2602.12207", "title": "VIRENA: Virtual Arena for Research, Education, and Democratic Innovation", "abstract": "Digital platforms shape how people communicate, deliberate, and form opinions. Studying these dynamics has become increasingly difficult due to restricted data access, ethical constraints on real-world experiments, and limitations of existing research tools. VIRENA (Virtual Arena) is a platform that enables controlled experimentation in realistic social media environments. Multiple participants interact simultaneously in realistic replicas of feed-based platforms (Instagram, Facebook, Reddit) and messaging apps (WhatsApp, Messenger). Large language model-powered AI agents participate alongside humans with configurable personas and realistic behavior. Researchers can manipulate content moderation approaches, pre-schedule stimulus content, and run experiments across conditions through a visual interface requiring no programming skills. VIRENA makes possible research designs that were previously impractical: studying human--AI interaction in realistic social contexts, experimentally comparing moderation interventions, and observing group deliberation as it unfolds. Built on open-source technologies that ensure data remain under institutional control and comply with data protection requirements, VIRENA is currently in use at the University of Zurich and available for pilot collaborations. Designed for researchers, educators, and public organizations alike, VIRENA's no-code interface makes controlled social media simulation accessible across disciplines and sectors. This paper documents its design, architecture, and capabilities.", "date": "2026-02-12", "categories": []}
{"id": "2602.15842", "title": "Memes-as-Replies: Can Models Select Humorous Manga Panel Responses?", "abstract": "Memes are a popular element of modern web communication, used not only as static artifacts but also as interactive replies within conversations. While computational research has focused on analyzing the intrinsic properties of memes, the dynamic and contextual use of memes to create humor remains an understudied area of web science. To address this gap, we introduce the Meme Reply Selection task and present MaMe-Re (Manga Meme Reply Benchmark), a benchmark of 100,000 human-annotated pairs (500,000 total annotations from 2,325 unique annotators) consisting of openly licensed Japanese manga panels and social media posts. Our analysis reveals three key insights: (1) large language models (LLMs) show preliminary evidence of capturing complex social cues such as exaggeration, moving beyond surface-level semantic matching; (2) the inclusion of visual information does not improve performance, revealing a gap between understanding visual content and effectively using it for contextual humor; (3) while LLMs can match human judgments in controlled settings, they struggle to distinguish subtle differences in wit among semantically similar candidates. These findings suggest that selecting contextually humorous replies remains an open challenge for current models.", "date": "2026-01-21", "categories": []}
{"id": "2602.15064", "title": "Structural Divergence Between AI-Agent and Human Social Networks in Moltbook", "abstract": "Large populations of AI agents are increasingly embedded in online environments, yet little is known about how their collective interaction patterns compare to human social systems. Here, we analyze the full interaction network of Moltbook, a platform where AI agents and humans coexist, and systematically compare its structure to well-characterized human communication networks. Although Moltbook follows the same node-edge scaling relationship observed in human systems, indicating comparable global growth constraints, its internal organization diverges markedly. The network exhibits extreme attention inequality, heavy-tailed and asymmetric degree distributions, suppressed reciprocity, and a global under-representation of connected triadic structures. Community analysis reveals a structured modular architecture with elevated modularity and comparatively lower community size inequality relative to degree-preserving null models. Together, these findings show that AI-agent societies can reproduce global structural regularities of human networks while exhibiting fundamentally different internal organizing principles, highlighting that key features of human social organization are not universal but depend on the nature of the interacting agents.", "date": "2026-02-13", "categories": []}
{"id": "2602.15273", "title": "FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health", "abstract": "Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for information health. In modern search and recommendation systems, ranking and personalization policies play a central role in shaping such exposure and its long-term effects on users. To study these effects in a controlled setting, we present FrameRef, a large-scale dataset of 1,073,740 systematically reframed claims across five framing dimensions: authoritative, consensus, emotional, prestige, and sensationalist, and propose a simulation-based framework for modeling sequential information exposure and reinforcement dynamics characteristic of ranking and recommendation systems. Within this framework, we construct framing-sensitive agent personas by fine-tuning language models with framing-conditioned loss attenuation, inducing targeted biases while preserving overall task competence. Using Monte Carlo trajectory sampling, we show that small, systematic shifts in acceptance and confidence can compound over time, producing substantial divergence in cumulative information health trajectories. Human evaluation further confirms that FrameRef's generated framings measurably affect human judgment. Together, our dataset and framework provide a foundation for systematic information health research through simulation, complementing and informing responsible human-centered research. We release FrameRef, code, documentation, human evaluation data, and persona adapter models at https://github.com/infosenselab/frameref.", "date": "2026-02-17", "categories": []}
{"id": "2602.15600", "title": "The geometry of online conversations and the causal antecedents of conflictual discourse", "abstract": "This article investigates the causal antecedents of conflictual language and the geometry of interaction in online threaded conversations related to climate change. We employ three annotation dimensions, inferred through LLM prompting and averaging, to capture complementary aspects of discursive conflict (such as stance: agreement vs disagreement; tone: attacking vs respectful; and emotional versus factual framing) and use data from a threaded online forum to examine how these dimensions respond to temporal, conversational, and arborescent structural features of discussions. We show that, as suggested by the literature, longer delays between successive posts in a thread are associated with replies that are, on average, more respectful, whereas longer delays relative to the parent post are associated with slightly less disagreement but more emotional (less factual) language. Second, we characterize alignment with the local conversational environment and find strong convergence both toward the average stance, tone and emotional framing of older sibling posts replying to the same parent and toward those of the parent post itself, with parent post effects generally stronger than sibling effects. We further show that early branch-level responses condition these alignment dynamics, such that parent-child stance alignment is amplified or attenuated depending on whether a branch is initiated in agreement or disagreement with the discussion's root message. These influences are largely additive for civility-related dimensions (attacking vs respectful, disagree vs agree), whereas for emotional versus factual framing there is a significant interaction: alignment with the parent's emotionality is amplified when older siblings are similarly aligned.", "date": "2026-02-17", "categories": []}
{"id": "2602.14043", "title": "Beyond Static Snapshots: Dynamic Modeling and Forecasting of Group-Level Value Evolution with Large Language Models", "abstract": "Social simulation is critical for mining complex social dynamics and supporting data-driven decision making. LLM-based methods have emerged as powerful tools for this task by leveraging human-like social questionnaire responses to model group behaviors. Existing LLM-based approaches predominantly focus on group-level values at discrete time points, treating them as static snapshots rather than dynamic processes. However, group-level values are not fixed but shaped by long-term social changes. Modeling their dynamics is thus crucial for accurate social evolution prediction--a key challenge in both data mining and social science. This problem remains underexplored due to limited longitudinal data, group heterogeneity, and intricate historical event impacts.\n  To bridge this gap, we propose a novel framework for group-level dynamic social simulation by integrating historical value trajectories into LLM-based human response modeling. We select China and the U.S. as representative contexts, conducting stratified simulations across four core sociodemographic dimensions (gender, age, education, income). Using the World Values Survey, we construct a multi-wave, group-level longitudinal dataset to capture historical value evolution, and then propose the first event-based prediction method for this task, unifying social events, current value states, and group attributes into a single framework. Evaluations across five LLM families show substantial gains: a maximum 30.88\\% improvement on seen questions and 33.97\\% on unseen questions over the Vanilla baseline. We further find notable cross-group heterogeneity: U.S. groups are more volatile than Chinese groups, and younger groups in both countries are more sensitive to external changes. These findings advance LLM-based social simulation and provide new insights for social scientists to understand and predict social value changes.", "date": "2026-02-15", "categories": []}
{"id": "2602.14406", "title": "TruthStance: An Annotated Dataset of Conversations on Truth Social", "abstract": "Argument mining and stance detection are central to understanding how opinions are formed and contested in online discourse. However, most publicly available resources focus on mainstream platforms such as Twitter and Reddit, leaving conversational structure on alt-tech platforms comparatively under-studied. We introduce TruthStance, a large-scale dataset of Truth Social conversation threads spanning 2023-2025, consisting of 24,378 posts and 523,360 comments with reply-tree structure preserved. We provide a human-annotated benchmark of 1,500 instances across argument mining and claim-based stance detection, including inter-annotator agreement, and use it to evaluate large language model (LLM) prompting strategies. Using the best-performing configuration, we release additional LLM-generated labels for 24,352 posts (argument presence) and 107,873 comments (stance to parent), enabling analysis of stance and argumentation patterns across depth, topics, and users. All code and data are released publicly.", "date": "2026-02-16", "categories": []}
{"id": "2507.12108", "title": "Multimodal Coordinated Online Behavior: Trade-offs and Strategies", "abstract": "Coordinated online behavior, which spans from beneficial collective actions to harmful manipulation such as disinformation campaigns, has become a key focus in digital ecosystem analysis. Traditional methods often rely on monomodal approaches, focusing on single types of interactions like co-retweets or co-hashtags, or consider multiple modalities independently of each other. However, these approaches may overlook the complex dynamics inherent in multimodal coordination. This study compares different ways of operationalizing multimodal coordinated behavior, examining the trade-off between weakly and strongly integrated models and their ability to capture broad versus tightly aligned coordination patterns. By contrasting monomodal, flattened, and multimodal methods, we evaluate the distinct contributions of each modality and the impact of different integration strategies. Our findings show that while not all modalities provide unique insights, multimodal analysis consistently offers a more informative representation of coordinated behavior, preserving structures that monomodal and flattened approaches often lose. This work enhances the ability to detect and analyze coordinated online behavior, offering new perspectives for safeguarding the integrity of digital platforms.", "date": "2025-07-16", "categories": []}
{"id": "2601.22871", "title": "Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild", "abstract": "As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence.\n  This paper presents JudgeGPT and RogueGPT, a dual-axis framework that decouples \"authenticity\" from \"attribution\" to investigate the mechanisms of human susceptibility. Analyzing 918 evaluations across five FMs (including GPT-4 and Llama-2), we employ Structural Causal Models (SCMs) as a principal framework for formulating testable causal hypotheses about detection accuracy.\n  Contrary to partisan narratives, we find that political orientation shows a negligible association with detection performance ($r=-0.10$). Instead, \"fake news familiarity\" emerges as a candidate mediator ($r=0.35$), suggesting that exposure may function as adversarial training for human discriminators. We identify a \"fluency trap\" where GPT-4 outputs (HumanMachineScore: 0.20) bypass Source Monitoring mechanisms, rendering them indistinguishable from human text.\n  These findings suggest that \"pre-bunking\" interventions should target cognitive source monitoring rather than demographic segmentation to ensure trustworthy information ecosystems.", "date": "2026-01-30", "categories": []}
{"id": "2602.11412", "title": "When Visibility Outpaces Verification: Delayed Verification and Narrative Lock-in in Agentic AI Discourse", "abstract": "Agentic AI systems-autonomous entities capable of independent planning and execution-reshape the landscape of human-AI trust. Long before direct system exposure, user expectations are mediated through high-stakes public discourse on social platforms. However, platform-mediated engagement signals (e.g., upvotes) may inadvertently function as a ``credibility proxy,'' potentially stifling critical evaluation.\n  This paper investigates the interplay between social proof and verification timing in online discussions of agentic AI. Analyzing a longitudinal dataset from two distinct Reddit communities with contrasting interaction cultures-r/OpenClaw and r/Moltbook-we operationalize verification cues via reproducible lexical rules and model the ``time-to-first-verification'' using a right-censored survival analysis framework.\n  Our findings reveal a systemic ``Popularity Paradox'': high-visibility discussions in both subreddits experience significantly delayed or entirely absent verification cues compared to low-visibility threads. This temporal lag creates a critical window for ``Narrative Lock-in,'' where early, unverified claims crystallize into collective cognitive biases before evidence-seeking behaviors emerge. We discuss the implications of this ``credibility-by-visibility'' effect for AI safety and propose ``epistemic friction'' as a design intervention to rebalance engagement-driven platforms.", "date": "2026-02-11", "categories": []}
{"id": "2602.10131", "title": "The Anatomy of the Moltbook Social Graph", "abstract": "I present a descriptive analysis of Moltbook, a social platform populated exclusively by AI agents, using data from the platform's first 3.5 days (6{,}159 agents; 13{,}875 posts; 115{,}031 comments). At the macro level, Moltbook exhibits structural signatures that are familiar from human social networks but not specific to them: heavy-tailed participation (power-law exponent $\\alpha = 1.70$) and small-world connectivity (average path length $=2.91$). At the micro level, patterns appear distinctly non-human. Conversations are extremely shallow (mean depth $=1.07$; 93.5\\% of comments receive no replies), reciprocity is low (0.197), and 34.1\\% of messages are exact duplicates of viral templates. Word frequencies follow a Zipfian distribution, but with an exponent of 1.70 -- notably steeper than typical English text ($\\approx 1.0$), suggesting more formulaic content. Agent discourse is dominated by identity-related language (68.1\\% of unique messages) and distinctive phrasings like ``my human'' (9.4\\% of messages) that have no parallel in human social media. Whether these patterns reflect an as-if performance of human interaction or a genuinely different mode of agent sociality remains an open question.", "date": "2026-02-03", "categories": []}
{"id": "2602.10400", "title": "When are We Worried? Temporal Trends of Anxiety and What They Reveal about Us", "abstract": "In this short paper, we make use of a recently created lexicon of word-anxiety associations to analyze large amounts of US and Canadian social media data (tweets) to explore *when* we are anxious and what insights that reveals about us. We show that our levels of anxiety on social media exhibit systematic patterns of rise and fall during the day -- highest at 8am (in-line with when we have high cortisol levels in the body) and lowest around noon. Anxiety is lowest on weekends and highest mid-week. We also examine anxiety in past, present, and future tense sentences to show that anxiety is highest in past tense and lowest in future tense. Finally, we examine the use of anxiety and calmness words in posts that contain pronouns to show: more anxiety in 3rd person pronouns (he, they) posts than 1st and 2nd person pronouns and higher anxiety in posts with subject pronouns (I, he, she, they) than object pronouns (me, him, her, them). Overall, these trends provide valuable insights on not just when we are anxious, but also how different types of focus (future, past, self, outward, etc.) are related to anxiety.", "date": "2026-02-11", "categories": []}
{"id": "2602.10935", "title": "What do people want to fact-check?", "abstract": "Research on misinformation has focused almost exclusively on supply, asking what falsehoods circulate, who produces them, and whether corrections work. A basic demand-side question remains unanswered. When ordinary people can fact-check anything they want, what do they actually ask about? We provide the first large-scale evidence on this question by analyzing close to 2{,}500 statements submitted by 457 participants to an open-ended AI fact-checking system. Each claim is classified along five semantic dimensions (domain, epistemic form, verifiability, target entity, and temporal reference), producing a behavioral map of public verification demand. Three findings stand out. First, users range widely across topics but default to a narrow epistemic repertoire, overwhelmingly submitting simple descriptive claims about present-day observables. Second, roughly one in four requests concerns statements that cannot be empirically resolved, including moral judgments, speculative predictions, and subjective evaluations, revealing a systematic mismatch between what users seek from fact-checking tools and what such tools can deliver. Third, comparison with the FEVER benchmark dataset exposes sharp structural divergences across all five dimensions, indicating that standard evaluation corpora encode a synthetic claim environment that does not resemble real-world verification needs. These results reframe fact-checking as a demand-driven problem and identify where current AI systems and benchmarks are misaligned with the uncertainty people actually experience.", "date": "2026-02-11", "categories": []}
{"id": "2601.14105", "title": "Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks", "abstract": "This study presents the first large-scale comparison of persuasion techniques present in crowd- versus professionally-written debunks. Using extensive datasets from Community Notes (CNs), EUvsDisinfo, and the Database of Known Fakes (DBKF), we quantify the prevalence and types of persuasion techniques across these fact-checking ecosystems. Contrary to prior hypothesis that community-produced debunks rely more heavily on subjective or persuasive wording, we find no evidence that CNs contain a higher average number of persuasion techniques than professional fact-checks. We additionally identify systematic rhetorical differences between CNs and professional debunking efforts, reflecting differences in institutional norms and topical coverage. Finally, we examine how the crowd evaluates persuasive language in CNs and show that, although notes with more persuasive elements receive slightly higher overall helpfulness ratings, crowd raters are effective at penalising the use of particular problematic rhetorical means", "date": "2026-01-20", "categories": []}
{"id": "2602.09289", "title": "Triggered: A Statistical Analysis of Environmental Influences on Extremist Groups", "abstract": "Online extremist communities operate within a wider information ecosystem shaped by real-world events, news coverage, and cross-community interaction. We adopt a systems perspective to examine these influences using seven years of data from two ideologically distinct extremist forums (Stormfront and Incels) and a mainstream reference community (r/News). We ask three questions: how extremist violence impacts community behaviour; whether news coverage of political entities predicts shifts in conversation dynamics; and whether linguistic diffusion occurs between mainstream and extremist spaces and across extremist ideologies. Methodologically, we combine counterfactual synthesis to estimate event-level impacts with vector autoregression and Granger causality analyses to model ongoing relationships among news signals, behavioural outcomes, and cross-community language change. Across analyses, our results indicate that Stormfront and r/News appear to be more reactive to external stimuli, while Incels demonstrates less cross-community linguistic influence and less responsiveness to news and violent events. These findings underscore that extremist communities are not homogeneous, but differ in how tightly they are coupled to the surrounding information ecosystem.", "date": "2026-02-10", "categories": []}
{"id": "2509.16813", "title": "Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text", "abstract": "Quantifying identity fusion -- the psychological merging of self with another entity or abstract target (e.g., a religious group, political party, ideology, value, brand, belief, etc.) -- is vital for understanding a wide range of group-based human behaviors. We introduce the Cognitive Linguistic Identity Fusion Score (CLIFS), a novel metric that integrates cognitive linguistics with large language models (LLMs), which builds on implicit metaphor detection. Unlike traditional pictorial and verbal scales, which require controlled surveys or direct field contact, CLIFS delivers fully automated, scalable assessments while maintaining strong alignment with the established verbal measure. In benchmarks, CLIFS outperforms both existing automated approaches and human annotation. As a proof of concept, we apply CLIFS to violence risk assessment to demonstrate that it can improve violence risk assessment by more than 240%. Building on our identification of a new NLP task and early success, we underscore the need to develop larger, more diverse datasets that encompass additional fusion-target domains and cultural backgrounds to enhance generalizability and further advance this emerging area. CLIFS models and code are public at https://github.com/DevinW-sudo/CLIFS.", "date": "2025-09-20", "categories": []}
{"id": "2602.07433", "title": "Multi-Agent Systems Shape Social Norms for Prosocial Behavior Change", "abstract": "Social norm interventions are used promote prosocial behaviors by highlighting prevalent actions, but their effectiveness is often limited in heterogeneous populations where shared understandings of desirable behaviors are lacking. This study explores whether multi-agent systems can establish \"virtual social norms\" to encourage donation behavior. We conducted an online experiment where participants interacted with a group of agents to discuss donation behaviors. Changes in perceived social norms, conformity, donation behavior, and user experience were measured pre- and postdiscussion. Results show that multi-agent interactions effectively increased perceived social norms and donation willingness. Notably, in-group agents led to stronger perceived social norms, higher conformity, and greater donation increases compared to out-group agents. Our findings demonstrate the potential of multi-agent systems for creating social norm interventions and offer insights into leveraging social identity dynamics to promote prosocial behavior in virtual environments.", "date": "2026-02-07", "categories": []}
{"id": "2602.08252", "title": "Language Predicts Identity Fusion Across Cultures and Reveals Divergent Pathways to Violence", "abstract": "In light of increasing polarization and political violence, understanding the psychological roots of extremism is increasingly important. Prior research shows that identity fusion predicts willingness to engage in extreme acts. We evaluate the Cognitive Linguistic Identity Fusion Score, a method that uses cognitive linguistic patterns, LLMs, and implicit metaphor to measure fusion from language. Across datasets from the United Kingdom and Singapore, this approach outperforms existing methods in predicting validated fusion scores. Applied to extremist manifestos, two distinct high-fusion pathways to violence emerge: ideologues tend to frame themselves in terms of group, forming kinship bonds; whereas grievance-driven individuals frame the group in terms of their personal identity. These results refine theories of identity fusion and provide a scalable tool aiding fusion research and extremism detection.", "date": "2026-02-09", "categories": []}
{"id": "2503.04773", "title": "Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content", "abstract": "Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on social media encapsulates nuanced perceptions and feelings associated with different places, offering rich insights into segregation. However, leveraging this data poses significant challenges due to its vast volume, ambiguity, and confluence of diverse perspectives. To tackle these challenges, we propose a novel Large Language Model (LLM) agent to automate online review mining for segregation prediction. Specifically, we propose a reflective LLM coder to digest social media content into insights consistent with real-world feedback, and eventually produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal, accessibility and convenience, and community engagement and local involvement. Guided by the codebook, LLMs can generate both informative review summaries and ratings for segregation prediction. Moreover, we design a REasoning-and-EMbedding (RE'EM) framework, which combines the reasoning and embedding capabilities of language models to integrate multi-channel features for segregation prediction.\n  Experiments on real-world data demonstrate that our agent substantially improves prediction accuracy, with a 22.79% elevation in R$^{2}$ and a 9.33% reduction in MSE. The derived codebook is generalizable across three different cities, consistently improving prediction accuracy. Moreover, our user study confirms that the codebook-guided summaries provide cognitive gains for human participants in perceiving places of interest (POIs)' social inclusiveness. Our study marks an important step toward understanding implicit social barriers and inequalities, demonstrating the great potential of promoting social inclusiveness with Web technology.", "date": "2025-02-17", "categories": []}
{"id": "2505.20624", "title": "POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization", "abstract": "Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. The results show that, while most models perform well in binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and demonstrate the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally.", "date": "2025-05-27", "categories": []}
{"id": "2506.19806", "title": "LLM-Based Social Simulations Require a Boundary", "abstract": "This position paper argues that LLM-based social simulations require clear boundaries to make meaningful contributions to social science. While Large Language Models (LLMs) offer promising capabilities for simulating human behavior, their tendency to produce homogeneous outputs, acting as an \"average persona\", fundamentally limits their ability to capture the behavioral diversity essential for complex social dynamics. We examine why heterogeneity matters for social simulations and how current LLMs fall short, analyzing the relationship between mean alignment and variance in LLM-generated behaviors. Through a systematic review of representative studies, we find that validation practices often fail to match the heterogeneity requirements of research questions: while most papers include ground truth comparisons, fewer than half explicitly assess behavioral variance, and most that do report lower variance than human populations. We propose that researchers should: (1) match validation depth to the heterogeneity demands of their research questions, (2) explicitly report variance alongside mean alignment, and (3) constrain claims to collective-level qualitative patterns when variance is insufficient. Rather than dismissing LLM-based simulation, we advocate for a boundary-aware approach that ensures these methods contribute genuine insights to social science.", "date": "2025-06-24", "categories": []}
{"id": "2407.02122", "title": "Fake News Detection: It's All in the Data!", "abstract": "This comprehensive survey serves as an indispensable resource for researchers embarking on the journey of fake news detection. By highlighting the pivotal role of dataset quality and diversity, it underscores the significance of these elements in the effectiveness and robustness of detection models. The survey meticulously outlines the key features of datasets, various labeling systems employed, and prevalent biases that can impact model performance. Additionally, it addresses critical ethical issues and best practices, offering a thorough overview of the current state of available datasets. Our contribution to this field is further enriched by the provision of GitHub repository, which consolidates publicly accessible datasets into a single, user-friendly portal. This repository is designed to facilitate and stimulate further research and development efforts aimed at combating the pervasive issue of fake news.", "date": "2024-07-02", "categories": []}
{"id": "2602.02624", "title": "Recommender system in X inadvertently profiles ideological positions of users", "abstract": "Studies on recommendations in social media have mainly analyzed the quality of recommended items (e.g., their diversity or biases) and the impact of recommendation policies (e.g., in comparison with purely chronological policies). We use a data donation program, collecting more than 2.5 million friend recommendations made to 682 volunteers on X over a year, to study instead how real-world recommenders learn, represent and process political and social attributes of users inside the so-called black boxes of AI systems. Using publicly available knowledge on the architecture of the recommender, we inferred the positions of recommended users in its embedding space. Leveraging ideology scaling calibrated with political survey data, we analyzed the political position of users in our study (N=26,509 among volunteers and recommended contacts) among several attributes, including age and gender. Our results show that the platform's recommender system produces a spatial ordering of users that is highly correlated with their Left-Right positions (Pearson rho=0.887, p-value < 0.0001), and that cannot be explained by socio-demographic attributes. These results open new possibilities for studying the interaction between human and AI systems. They also raise important questions linked to the legal definition of algorithmic profiling in data privacy regulation by blurring the line between active and passive profiling. We explore new constrained recommendation methods enabled by our results, limiting the political information in the recommender as a potential tool for privacy compliance capable of preserving recommendation relevance.", "date": "2026-02-02", "categories": []}
{"id": "2602.02838", "title": "Beyond Content: Behavioral Policies Reveal Actors in Information Operations", "abstract": "The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features. These approaches are increasingly brittle as generative models produce convincing text, platforms restrict access to behavioral data, and actors migrate to less-regulated spaces. We introduce a platform-agnostic framework that identifies malicious actors from their behavioral policies by modeling user activity as sequential decision processes. We apply this approach to 12,064 Reddit users, including 99 accounts linked to the Russian Internet Research Agency in Reddit's 2017 transparency report, analyzing over 38 million activity steps from 2015-2018. Activity-based representations, which model how users act rather than what they post, consistently outperform content models in detecting malicious accounts. When distinguishing trolls -- users engaged in coordinated manipulation -- from ordinary users, policy-based classifiers achieve a median macro-$F_1$ of 94.9%, compared to 91.2% for text embeddings. Policy features also enable earlier detection from short traces and degrade more gracefully under evasion strategies or data corruption. These findings show that behavioral dynamics encode stable, discriminative signals of manipulation and point to resilient, cross-platform detection strategies in the era of synthetic content and limited data access.", "date": "2026-02-02", "categories": []}
{"id": "2602.03541", "title": "Group Selection as a Safeguard Against AI Substitution", "abstract": "Reliance on generative AI can reduce cultural variance and diversity, especially in creative work. This reduction in variance has already led to problems in model performance, including model collapse and hallucination. In this paper, we examine the long-term consequences of AI use for human cultural evolution and the conditions under which widespread AI use may lead to \"cultural collapse\", a process in which reliance on AI-generated content reduces human variation and innovation and slows cumulative cultural evolution. Using an agent-based model and evolutionary game theory, we compare two types of AI use: complement and substitute. AI-complement users seek suggestions and guidance while remaining the main producers of the final output, whereas AI-substitute users provide minimal input, and rely on AI to produce most of the output. We then study how these use strategies compete and spread under evolutionary dynamics. We find that AI-substitute users prevail under individual-level selection despite the stronger reduction in cultural variance. By contrast, AI-complement users can benefit their groups by maintaining the variance needed for exploration, and can therefore be favored under cultural group selection when group boundaries are strong. Overall, our findings shed light on the long-term, population-level effects of AI adoption and inform policy and organizational strategies to mitigate these risks.", "date": "2026-02-03", "categories": []}
{"id": "2601.16355", "title": "Identity, Cooperation and Framing Effects within Groups of Real and Simulated Humans", "abstract": "Humans act via a nuanced process that depends both on rational deliberation and also on identity and contextual factors. In this work, we study how large language models (LLMs) can simulate human action in the context of social dilemma games. While prior work has focused on \"steering\" (weak binding) of chat models to simulate personas, we analyze here how deep binding of base models with extended backstories leads to more faithful replication of identity-based behaviors. Our study has these findings: simulation fidelity vs human studies is improved by conditioning base LMs with rich context of narrative identities and checking consistency using instruction-tuned models. We show that LLMs can also model contextual factors such as time (year that a study was performed), question framing, and participant pool effects. LLMs, therefore, allow us to explore the details that affect human studies but which are often omitted from experiment descriptions, and which hamper accurate replication.", "date": "2026-01-22", "categories": []}
{"id": "2602.00588", "title": "The French Drama Revolution: Political Economy and Literary Production, 1700-1900", "abstract": "This paper investigates the changing nature of French drama between 1700-1900 using Latent Dirichlet Allocation and Jensen-Shannon Divergence. Results indicate that the topical distribution of French drama changed profoundly after the French Revolution, particularly between 1789 and 1850. Bourgeois themes emerged among the most prevalent topics since the late 18th century. To assess the coevolution of drama and economic growth, I plot the yearly prevalence of topics alongside French GDP between 1700-1900, and discuss these changes in light of the political and economic changes prompted by the French Revolution and the industrialization of the country.", "date": "2026-01-31", "categories": []}
{"id": "2602.01567", "title": "DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media", "abstract": "Social media engagement prediction is a central challenge in computational social science, particularly for understanding how users interact with misinformation. Existing approaches often treat engagement as a homogeneous time-series signal, overlooking the heterogeneous social mechanisms and platform designs that shape how misinformation spreads. In this work, we ask: ``Can neural architectures discover social exchange principles from behavioral data alone?'' We introduce \\textsc{Dreams} (\\underline{D}isentangled \\underline{R}epresentations and \\underline{E}pisodic \\underline{A}daptive \\underline{M}odeling for \\underline{S}ocial media misinformation engagements), a social exchange theory-guided framework that models misinformation engagement as a dynamic process of social exchange. Rather than treating engagement as a static outcome, \\textsc{Dreams} models it as a sequence-to-sequence adaptation problem, where each action reflects an evolving negotiation between user effort and social reward conditioned by platform context. It integrates adaptive mechanisms to learn how emotional and contextual signals propagate through time and across platforms. On a cross-platform dataset spanning $7$ platforms and 2.37M posts collected between 2021 and 2025, \\textsc{Dreams} achieves state-of-the-art performance in predicting misinformation engagements, reaching a mean absolute percentage error of $19.25$\\%. This is a $43.6$\\% improvement over the strongest baseline. Beyond predictive gains, the model reveals consistent cross-platform patterns that align with social exchange principles, suggesting that integrating behavioral theory can enhance empirical modeling of online misinformation engagement. The source code is available at: https://github.com/ltian678/DREAMS.", "date": "2026-02-02", "categories": []}
{"id": "2601.21815", "title": "Moral Outrage Shapes Commitments Beyond Attention: Multimodal Moral Emotions on YouTube in Korea and the US", "abstract": "Understanding how media rhetoric shapes audience engagement is crucial in the attention economy. This study examines how moral emotional framing by mainstream news channels on YouTube influences user behavior across Korea and the United States. To capture the platform's multimodal nature, combining thumbnail images and video titles, we develop a multimodal moral emotion classifier by fine tuning a vision language model. The model is trained on human annotated multimodal datasets in both languages and applied to approximately 400,000 videos from major news outlets. We analyze engagement levels including views, likes, and comments, representing increasing degrees of commitment. The results show that other condemning rhetoric expressions of moral outrage that criticize others morally consistently increase all forms of engagement across cultures, with effects ranging from passive viewing to active commenting. These findings suggest that moral outrage is a particularly effective emotional strategy, attracting not only attention but also active participation. We discuss concerns about the potential misuse of other condemning rhetoric, as such practices may deepen polarization by reinforcing in group and out group divisions. To facilitate future research and ensure reproducibility, we publicly release our Korean and English multimodal moral emotion classifiers.", "date": "2026-01-29", "categories": []}
{"id": "2504.12327", "title": "Word Embeddings Track Social Group Changes Across 70 Years in China", "abstract": "Language encodes societal beliefs about social groups through word patterns. While computational methods like word embeddings enable quantitative analysis of these patterns, studies have primarily examined gradual shifts in Western contexts. We present the first large-scale computational analysis of Chinese state-controlled media (1950-2019) to examine how revolutionary social transformations are reflected in official linguistic representations of social groups. Using diachronic word embeddings at multiple temporal resolutions, we find that Chinese representations differ significantly from Western counterparts, particularly regarding economic status, ethnicity, and gender. These representations show distinct evolutionary dynamics: while stereotypes of ethnicity, age, and body type remain remarkably stable across political upheavals, representations of gender and economic classes undergo dramatic shifts tracking historical transformations. This work advances our understanding of how officially sanctioned discourse encodes social structure through language while highlighting the importance of non-Western perspectives in computational social science.", "date": "2025-04-12", "categories": []}
{"id": "2508.13187", "title": "\"Not in My Backyard\": LLMs Uncover Online and Offline Social Biases Against Homelessness", "abstract": "Homelessness is a persistent social challenge, impacting millions worldwide. Over 876,000 people experienced homelessness (PEH) in the U.S. in 2025. Social bias is a significant barrier to alleviation, shaping public perception and influencing policymaking. Given that online textual media and offline city council discourse reflect and influence part of public opinion, it provides valuable insights to identify and track social biases against PEH. We present a new, manually-annotated multi-domain dataset compiled from Reddit, X (formerly Twitter), news articles, and city council meeting minutes across ten U.S. cities. Our 16-category multi-label taxonomy creates a challenging long-tail classification problem: some categories appear in less than 1% of samples, while others exceed 70%. We find that small human-annotated datasets (1,702 samples) are insufficient for training effective classifiers, whether used to fine-tune encoder models or as few-shot examples for LLMs. To address this, we use GPT-4.1 to generate pseudo-labels on a larger unlabeled corpus. Training on this expanded dataset enables even small encoder models (ModernBERT, 150M parameters) to achieve 35.23 macro-F1, approaching GPT-4.1's 41.57. This demonstrates that \\textbf{data quantity matters more than model size}, enabling low-cost, privacy-preserving deployment without relying on commercial APIs. Our results reveal that negative bias against PEH is prevalent both offline and online (especially on Reddit), with \"not in my backyard\" narratives showing the highest engagement. These findings uncover a type of ostracism that directly impacts poverty-reduction policymaking and provide actionable insights for practitioners addressing homelessness.", "date": "2025-08-14", "categories": []}
{"id": "2303.14128", "title": "The crime of being poor", "abstract": "The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for their situation, are more often associated with criminal offenses than the wealthy strata of society and even incur criminal offenses simply as a result of being poor. While no evidence has been found in the literature that correlates poverty and overall criminality rates, this paper offers evidence of a collective belief that associates both concepts. This brief report measures the societal bias that correlates criminality with the poor, as compared to the rich, by using Natural Language Processing (NLP) techniques in Twitter. The paper quantifies the level of crime-poverty bias in a panel of eight different English-speaking countries. The regional differences in the association between crime and poverty cannot be justified based on different levels of inequality or unemployment, which the literature correlates to property crimes. The variation in the observed rates of crime-poverty bias for different geographic locations could be influenced by cultural factors and the tendency to overestimate the equality of opportunities and social mobility in specific countries. These results have consequences for policy-making and open a new path of research for poverty mitigation with the focus not only on the poor but on society as a whole. Acting on the collective bias against the poor would facilitate the approval of poverty reduction policies, as well as the restoration of the dignity of the persons affected.", "date": "2023-03-24", "categories": []}
{"id": "2601.20032", "title": "TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference", "abstract": "Health influencers play a growing role in shaping public beliefs, yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. As a result, claim-centric verification methods struggle to capture the pragmatic meaning of influencer discourse. In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in three stages: (1) identifying the core influencer recommendation--takeaway; (2) constructing an argumentation graph that captures influencer justification for the takeaway; (3) performing factor graph-based probabilistic inference to validate the takeaway. We evaluate TAIGR on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse's pragmatic and argumentative structure rather than treating transcripts as flat collections of claims.", "date": "2026-01-27", "categories": []}
{"id": "2601.20424", "title": "Hopes and Fears -- Emotion Distribution in the Topic Landscape of Finnish Parliamentary Speech 2000-2020", "abstract": "Existing research often treats parliamentary discourse as a homogeneous whole, overlooking topic-specific patterns. Parliamentary speeches address a wide range of topics, some of which evoke stronger emotions than others. While everyone has intuitive assumptions about what the most emotive topics in a parliament may be, there has been little research into the emotions typically linked to different topics. This paper strives to fill this gap by examining emotion expression among the topics of parliamentary speeches delivered in Eduskunta, the Finnish Parliament, between 2000 and 2020. An emotion analysis model is used to investigate emotion expression in topics, from both synchronic and diachronic perspectives. The results strengthen evidence of increasing positivity in parliamentary speech and provide further insights into topic-specific emotion expression within parliamentary debate.", "date": "2026-01-28", "categories": []}
{"id": "2512.16034", "title": "Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms", "abstract": "Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. The volume of personal information has historically been restricted and thus little exploration has gone into understanding what kind of information is most informative for predicting annotator labels. In this work, we categorize self-disclosures and use them to build annotator models for predicting judgments of social norms. We perform several ablations and analyses to examine the impact of the type of information on our ability to predict annotation patterns. Contrary to previous work, only a small number of comments related to the original post are needed. Lastly, a more diverse sample of annotator self-disclosures did not lead to the best performance. Sampling from a larger pool of comments without filtering still yields the best performance, suggesting that there is still much to uncover in terms of what information about an annotator is most useful for verdict prediction.", "date": "2025-12-17", "categories": []}
{"id": "2601.07110", "title": "The Need for a Socially-Grounded Persona Framework for User Simulation", "abstract": "Synthetic personas are widely used to condition large language models (LLMs) for social simulation, yet most personas are still constructed from coarse sociodemographic attributes or summaries. We revisit persona creation by introducing SCOPE, a socially grounded framework for persona construction and evaluation, built from a 141-item, two-hour sociopsychological protocol collected from 124 U.S.-based participants. Across seven models, we find that demographic-only personas are a structural bottleneck: demographics explain only ~1.5% of variance in human response similarity. Adding sociopsychological facets improves behavioral prediction and reduces over-accentuation, and non-demographic personas based on values and identity achieve strong alignment with substantially lower bias. These trends generalize to SimBench (441 aligned questions), where SCOPE personas outperform default prompting and NVIDIA Nemotron personas, and SCOPE augmentation improves Nemotron-based personas. Our results indicate that persona quality depends on sociopsychological structure rather than demographic templates or summaries.", "date": "2026-01-12", "categories": []}
{"id": "2601.17132", "title": "From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech", "abstract": "Few forces rival fear in their ability to mobilize societies, distort communication, and reshape collective behavior. In computational linguistics, fear is primarily studied as an emotion, but not as a distinct form of speech. Fear speech content is widespread and growing, and often outperforms hate-speech content in reach and engagement because it appears \"civiler\" and evades moderation. Yet the computational study of fear speech remains fragmented and under-resourced. This can be understood by recognizing that fear speech is a phenomenon shaped by contributions from multiple disciplines. In this paper, we bridge cross-disciplinary perspectives by comparing theories of fear from Psychology, Political science, Communication science, and Linguistics. Building on this, we review existing definitions. We follow up with a survey of datasets from related research areas and propose a taxonomy that consolidates different dimensions of fear for studying fear speech. By reviewing current datasets and defining core concepts, our work offers both theoretical and practical guidance for creating datasets and advancing fear speech research.", "date": "2026-01-23", "categories": []}
{"id": "2601.17658", "title": "Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization", "abstract": "The rise of conspiracy theories has created far-reaching societal harm in the public discourse by eroding trust and fueling polarization. Beyond this public impact lies a deeply personal toll on the friends and families of conspiracy believers, a dimension often overlooked in large-scale computational research. This study fills this gap by systematically mapping radicalization journeys and quantifying the associated emotional toll inflicted on loved ones. We use the prominent case of QAnon as a case study, analyzing 12747 narratives from the r/QAnonCasualties support community through a novel mixed-methods approach. First, we use topic modeling (BERTopic) to map the radicalization trajectories, identifying key pre-existing conditions, triggers, and post-radicalization characteristics. From this, we apply an LDA-based graphical model to uncover six recurring archetypes of QAnon adherents, which we term \"radicalization personas.\" Finally, using LLM-assisted emotion detection and regression modeling, we link these personas to the specific emotional toll reported by narrators. Our findings reveal that these personas are not just descriptive; they are powerful predictors of the specific emotional harms experienced by narrators. Radicalization perceived as a deliberate ideological choice is associated with narrator anger and disgust, while those marked by personal and cognitive collapse are linked to fear and sadness. This work provides the first empirical framework for understanding radicalization as a relational phenomenon, offering a vital roadmap for researchers and practitioners to navigate its interpersonal fallout.", "date": "2026-01-25", "categories": []}
{"id": "2601.15484", "title": "Is Grokipedia Right-Leaning? Comparing Political Framing in Wikipedia and Grokipedia on Controversial Topics", "abstract": "Online encyclopedias are central to contemporary information infrastructures and have become focal points of debates over ideological bias. Wikipedia, in particular, has long been accused of left-leaning bias, while Grokipedia, an AI-generated encyclopedia launched by xAI, has been framed as a right-leaning alternative. This paper presents a comparative analysis of Wikipedia and Grokipedia on well-established politically contested topics. Specifically, we examine differences in semantic framing, political orientation, and content prioritization. We find that semantic similarity between the two platforms decays across article sections and diverges more strongly on controversial topics than on randomly sampled ones. Additionally, we show that both encyclopedias predominantly exhibit left-leaning framings, although Grokipedia exhibits a more bimodal distribution with increased prominence of right-leaning content. The experimental code is publicly available.", "date": "2026-01-21", "categories": []}
{"id": "2601.06111", "title": "LLM Powered Social Digital Twins: A Framework for Simulating Population Behavioral Response to Policy Interventions", "abstract": "Predicting how populations respond to policy interventions is a fundamental challenge in computational social science and public policy. Traditional approaches rely on aggregate statistical models that capture historical correlations but lack mechanistic interpretability and struggle with novel policy scenarios. We present a general framework for constructing Social Digital Twins - virtual population replicas where Large Language Models (LLMs) serve as cognitive engines for individual agents. Each agent, characterized by demographic and psychographic attributes, receives policy signals and outputs multi-dimensional behavioral probability vectors. A calibration layer maps aggregated agent responses to observable population-level metrics, enabling validation against real-world data and deployment for counterfactual policy analysis.\n  We instantiate this framework in the domain of pandemic response, using COVID-19 as a case study with rich observational data. On a held-out test period, our calibrated digital twin achieves a 20.7% improvement in macro-averaged prediction error over gradient boosting baselines across six behavioral categories. Counterfactual experiments demonstrate monotonic and bounded responses to policy variations, establishing behavioral plausibility. The framework is domain-agnostic: the same architecture applies to transportation policy, economic interventions, environmental regulations, or any setting where policy affects population behavior. We discuss implications for policy simulation, limitations of the approach, and directions for extending LLM-based digital twins beyond pandemic response.", "date": "2026-01-03", "categories": []}
{"id": "2601.13932", "title": "Generating consensus and dissent on massive discussion platforms with an $O(N)$ semantic-vector model", "abstract": "Reaching consensus on massive discussion networks is critical for reducing noise and achieving optimal collective outcomes. However, the natural tendency of humans to preserve their initial ideas constrains the emergence of global solutions. To address this, Collective Intelligence (CI) platforms facilitate the discovery of globally superior solutions. We introduce a dynamical system based on the standard $O(N)$ model to drive the aggregation of semantically similar ideas. The system consists of users represented as nodes in a $d=2$ lattice with nearest-neighbor interactions, where their ideas are represented by semantic vectors computed with a pretrained embedding model. We analyze the system's equilibrium states as a function of the coupling parameter $\\beta$. Our results show that $\\beta > 0$ drives the system toward a ferromagnetic-like phase (global consensus), while $\\beta < 0$ induces an antiferromagnetic-like state (maximum dissent), where users maximize semantic distance from their neighbors. This framework offers a controllable method for managing the tradeoff between cohesion and diversity in CI platforms.", "date": "2026-01-20", "categories": []}
{"id": "2501.16123", "title": "From #Dr00gtiktok to #harmreduction: Exploring Substance Use Hashtags on TikTok", "abstract": "TikTok has emerged as a major source of information and social interaction for youth, raising urgent questions about how substance use discourse manifests and circulates on the platform. This paper presents the first comprehensive analysis of publicly visible, algorithmically surfaced substance-related content on TikTok, drawing on hashtags spanning all major substance categories. Using a mixed-methods approach that combines social network analysis with qualitative content coding, we examined 2,333 substance-related hashtags, identifying 16 distinct hashtag communities and characterizing their structural and thematic relationships. Our network analysis reveals a highly interconnected small-world structure in which recovery-focused hashtags such as \\textit{\\#addiction}, \\textit{\\#recovery}, and \\textit{\\#sober} serve as central bridges between communities. Qualitative analysis of 351 representative videos shows that Recovery Advocacy content (33.9\\%) and Satirical content (28.2\\%) dominate, while direct substance depiction appears in only 26\\% of videos, with active use shown in just 6.5\\% of them. These findings suggest that the algorithmically surfaced layer of substance-related discourse on TikTok is predominantly oriented toward recovery, support, and coping rather than explicit promotion of substance use. We further show that hashtag communities and video content are closely aligned, indicating that substance-related discourse on TikTok is shaped through organic community formation within platform affordances rather than widespread adversarial evasion of moderation. This work contributes to social computing research by showing how algorithmic visibility on TikTok shapes the organization of substance-related discourse and the formation of recovery and support communities.", "date": "2025-01-27", "categories": []}
{"id": "2601.13317", "title": "Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme Modeling of Climate Discourse", "abstract": "Climate discourse online plays a crucial role in shaping public understanding of climate change and influencing political and policy outcomes. However, climate communication unfolds across structurally distinct platforms with fundamentally different incentive structures: paid advertising ecosystems incentivize targeted, strategic persuasion, while public social media platforms host largely organic, user-driven discourse. Existing computational studies typically analyze these environments in isolation, limiting our ability to distinguish institutional messaging from public expression. In this work, we present a comparative analysis of climate discourse across paid advertisements on Meta (previously known as Facebook) and public posts on Bluesky from July 2024 to September 2025. We introduce an interpretable, end-to-end thematic discovery and assignment framework that clusters texts by semantic similarity and leverages large language models (LLMs) to generate concise, human-interpretable theme labels. We evaluate the quality of the induced themes against traditional topic modeling baselines using both human judgments and an LLM-based evaluator, and further validate their semantic coherence through downstream stance prediction and theme-guided retrieval tasks. Applying the resulting themes, we characterize systematic differences between paid climate messaging and public climate discourse and examine how thematic prevalence shifts around major political events. Our findings show that platform-level incentives are reflected in the thematic structure, stance alignment, and temporal responsiveness of climate narratives. While our empirical analysis focuses on climate communication, the proposed framework is designed to support comparative narrative analysis across heterogeneous communication environments.", "date": "2026-01-19", "categories": []}
{"id": "2601.13487", "title": "The Hidden Toll of Social Media News: Causal Effects on Psychosocial Wellbeing", "abstract": "News consumption on social media has become ubiquitous, yet how different forms of engagement shape psychosocial outcomes remains unclear. To address this gap, we leveraged a large-scale dataset of ~26M posts and ~45M comments on the BlueSky platform, and conducted a quasi-experimental study, matching 81,345 Treated users exposed to News feeds with 83,711 Control users using stratified propensity score analysis. We examined psychosocial wellbeing, in terms of affective, behavioral, and cognitive outcomes. Our findings reveal that news engagement produces systematic trade-offs: increased depression, stress, and anxiety, yet decreased loneliness and increased social interaction on the platform. Regression models reveal that News feed bookmarking is associated with greater psychosocial deterioration compared to commenting or quoting, with magnitude differences exceeding tenfold. These per-engagement effects accumulate with repeated exposure, showing significant psychosocial impacts. Our work extends theories of news effects beyond crisis-centric frameworks by demonstrating that routine consumption creates distinct psychological dynamics depending on engagement type, and bears implications for tools and interventions for mitigating the psychosocial costs of news consumption on social media.", "date": "2026-01-20", "categories": []}
{"id": "2502.11866", "title": "Southern Newswires: A Large-Scale Study of Mid-Century Wire Content Beyond the Front Page", "abstract": "This paper describes the construction of a large-scale corpus of historical wire articles from U.S. Southern newspapers, spanning 1960-1975 and covering multiple wire services (e.g., Associated Press, United Press International, Newspaper Enterprise Association). Unlike prior work that focuses primarily on front-page content, the corpus captures wire-sourced articles across the entire newspaper, offering broader insight into mid-century Southern news coverage. The analysis incorporates both raw OCR text and a version processed through an LLM-based text correction pipeline designed to reduce OCR noise and improve suitability for quantitative text analysis. Multiple versions of the same wire dispatch are retained, allowing for the study of editorial differences in language and framing across newspapers. Articles are classified by wire service, enabling comparative analysis of editorial patterns across agencies. Together, these features provide a detailed perspective on how Southern newspapers transmitted national and international news during a transformative period in American history.", "date": "2025-02-17", "categories": []}
{"id": "2502.13246", "title": "When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models", "abstract": "Metaphor, discussing one concept in terms of another, is abundant in politics and can shape how people understand important issues. We develop a computational approach to measure metaphorical language, focusing on immigration discourse on social media. Grounded in qualitative social science research, we identify seven concepts evoked in immigration discourse (e.g. \"water\" or \"vermin\"). We propose and evaluate a novel technique that leverages both word-level and document-level signals to measure metaphor with respect to these concepts. We then study the relationship between metaphor, political ideology, and user engagement in 400K US tweets about immigration. While conservatives tend to use dehumanizing metaphors more than liberals, this effect varies widely across concepts. Moreover, creature-related metaphor is associated with more retweets, especially for liberal authors. Our work highlights the potential for computational methods to complement qualitative approaches in understanding subtle and implicit language in political discourse.", "date": "2025-02-18", "categories": []}
{"id": "2512.17850", "title": "Integrating Computational Methods and AI into Qualitative Studies of Aging and Later Life", "abstract": "This chapter demonstrates how computational social science (CSS) tools are extending and expanding research on aging. The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical documents are increasingly employed alongside scalable data management, computational text analysis, and open-science practices. Machine learning (ML) and natural language processing (NLP), provide resources to aggregate and systematically index large volumes of qualitative data, identify patterns, and maintain clear links to in-depth accounts. Drawing on case studies of projects that examine later life--including examples with original data from the DISCERN study (a team-based ethnography of life with dementia) and secondary analyses of the American Voices Project (nationally representative interview)--the chapter highlights both uses and challenges of bringing CSS tools into more meaningful dialogue with qualitative aging research. The chapter argues such work has potential for (1) streamlining and augmenting existing workflows, (2) scaling up samples and projects, and (3) generating multi-method approaches to address important questions in new ways, before turning to practices useful for individuals and teams seeking to understand current possibilities or refine their workflow processes. The chapter concludes that current developments are not without peril, but offer potential for new insights into aging and the life course by broadening--rather than replacing--the methodological foundations of qualitative research.", "date": "2025-12-19", "categories": []}
{"id": "2404.08816", "title": "Measuring the Quality of Answers in Political Q&As with Large Language Models", "abstract": "This article proposes a new approach for assessing the quality of answers in political question-and-answer sessions. We measure the quality of an answer based on how easily and accurately it can be recognized in a random set of candidate answers given the question's text. This measure reflects the answer's relevance and depth of engagement with the question. Like semantic search, we can implement this approach by training a language model on the corpus of observed questions and answers without additional human-labeled data. We showcase and validate our methodology within the context of the Question Period in the Canadian House of Commons. Our analysis reveals that while some answers have a weak semantic connection to questions, hinting at some evasion or obfuscation, they are generally at least moderately relevant, far exceeding what we would expect from random replies. We also find a meaningful correlation between answer quality and the party affiliation of the members of Parliament asking the questions.", "date": "2024-04-12", "categories": []}
{"id": "2601.06477", "title": "IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments", "abstract": "Warning: This paper consists of examples representing regional biases in Indian regions that might be offensive towards a particular region.\n  While social biases corresponding to gender, race, socio-economic conditions, etc., have been extensively studied in the major applications of Natural Language Processing (NLP), biases corresponding to regions have garnered less attention. This is mainly because of (i) difficulty in the extraction of regional bias datasets, (ii) disagreements in annotation due to inherent human biases, and (iii) regional biases being studied in combination with other types of social biases and often being under-represented. This paper focuses on creating a dataset IndRegBias, consisting of regional biases in an Indian context reflected in users' comments on popular social media platforms, namely Reddit and YouTube. We carefully selected 25,000 comments appearing on various threads in Reddit and videos on YouTube discussing trending topics on regional issues in India. Furthermore, we propose a multilevel annotation strategy to annotate the comments describing the severity of regional biased statements. To detect the presence of regional bias and its severity in IndRegBias, we evaluate open-source Large Language Models (LLMs) and Indic Language Models (ILMs) using zero-shot, few-shot, and fine-tuning strategies. We observe that zero-shot and few-shot approaches show lower accuracy in detecting regional biases and severity in the majority of the LLMs and ILMs. However, the fine-tuning approach significantly enhances the performance of the LLM in detecting Indian regional bias along with its severity.", "date": "2026-01-10", "categories": []}
{"id": "2601.08785", "title": "Uncovering Political Bias in Large Language Models using Parliamentary Voting Records", "abstract": "As large language models (LLMs) become deeply embedded in digital platforms and decision-making systems, concerns about their political biases have grown. While substantial work has examined social biases such as gender and race, systematic studies of political bias remain limited, despite their direct societal impact. This paper introduces a general methodology for constructing political bias benchmarks by aligning model-generated voting predictions with verified parliamentary voting records. We instantiate this methodology in three national case studies: PoliBiasNL (2,701 Dutch parliamentary motions and votes from 15 political parties), PoliBiasNO (10,584 motions and votes from 9 Norwegian parties), and PoliBiasES (2,480 motions and votes from 10 Spanish parties). Across these benchmarks, we assess ideological tendencies and political entity bias in LLM behavior. As part of our evaluation framework, we also propose a method to visualize the ideology of LLMs and political parties in a shared two-dimensional CHES (Chapel Hill Expert Survey) space by linking their voting-based positions to the CHES dimensions, enabling direct and interpretable comparisons between models and real-world political actors. Our experiments reveal fine-grained ideological distinctions: state-of-the-art LLMs consistently display left-leaning or centrist tendencies, alongside clear negative biases toward right-conservative parties. These findings highlight the value of transparent, cross-national evaluation grounded in real parliamentary behavior for understanding and auditing political bias in modern LLMs.", "date": "2026-01-13", "categories": []}
{"id": "2512.10105", "title": "Belief Is All You Need: Modeling Narrative Archetypes in Conspiratorial Discourse", "abstract": "Conspiratorial discourse is increasingly embedded within digital communication ecosystems, yet its structure and spread remain difficult to study. This work analyzes conspiratorial narratives in Singapore-based Telegram groups, showing that such content is woven into everyday discussions rather than confined to isolated echo chambers. We propose a two-stage computational framework. First, we fine-tune RoBERTa-large to classify messages as conspiratorial or not, achieving an F1-score of 0.866 on 2,000 expert-labeled messages. Second, we build a signed belief graph in which nodes represent messages and edge signs reflect alignment in belief labels, weighted by textual similarity. We introduce a Signed Belief Graph Neural Network (SiBeGNN) that uses a Sign Disentanglement Loss to learn embeddings that separate ideological alignment from stylistic features.\n  Using hierarchical clustering on these embeddings, we identify seven narrative archetypes across 553,648 messages: legal topics, medical concerns, media discussions, finance, contradictions in authority, group moderation, and general chat. SiBeGNN yields stronger clustering quality (cDBI = 8.38) than baseline methods (13.60 to 67.27), supported by 88 percent inter-rater agreement in expert evaluations. Our analysis shows that conspiratorial messages appear not only in clusters focused on skepticism or distrust, but also within routine discussions of finance, law, and everyday matters. These findings challenge common assumptions about online radicalization by demonstrating that conspiratorial discourse operates within ordinary social interaction. The proposed framework advances computational methods for belief-driven discourse analysis and offers applications for stance detection, political communication studies, and content moderation policy.", "date": "2025-12-10", "categories": []}
{"id": "2601.06060", "title": "Why Slop Matters", "abstract": "AI-generated \"slop\" is often seen as digital pollution. We argue that this dismissal of the topic risks missing important aspects of AI Slop that deserve rigorous study. AI Slop serves a social function: it offers a supply-side solution to a variety of problems in cultural and economic demand - that, collectively, people want more content than humans can supply. We also argue that AI Slop is not mere digital detritus but has its own aesthetic value. Like other \"low\" cultural forms initially dismissed by critics, it nonetheless offers a legitimate means of collective sense-making, with the potential to express meaning and identity. We identify three key features of family resemblance for prototypical AI Slop: superficial competence (its veneer of quality is belied by a deeper lack of substance), asymmetry effort (it takes vastly less effort to generate than would be the case without AI), and mass producibility (it is part of a digital ecosystem of widespread generation and consumption). While AI Slop is heterogeneous and depends crucially on its medium, it tends to vary across three dimensions: instrumental utility, personalization, and surrealism. AI Slop will be an increasingly prolific and impactful part of our creative, information, and cultural economies; we should take it seriously as an object of study in its own right.", "date": "2025-12-23", "categories": []}
{"id": "2601.06132", "title": "An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia wars", "abstract": "Political bias in media plays a critical role in shaping public opinion, voter behaviour, and broader democratic discourse. Subjective opinions and political bias can be found in media sources, such as newspapers, depending on their funding mechanisms and alliances with political parties. Automating the detection of political biases in media content can limit biases in elections. The impact of large language models (LLMs) in politics and media studies is becoming prominent. In this study, we utilise LLMs to compare the left-wing, right-wing, and neutral political opinions expressed in the Guardian and BBC. We review newspaper reporting that includes significant events such as the Russia-Ukraine war and the Hamas-Israel conflict. We analyse the proportion for each opinion to find the bias under different LLMs, including BERT, Gemini, and DeepSeek. Our results show that after the outbreak of the wars, the political bias of Western media shifts towards the left-wing and each LLM gives a different result. DeepSeek consistently showed a stable Left-leaning tendency, while BERT and Gemini remained closer to the Centre. The BBC and The Guardian showed distinct reporting behaviours across the two conflicts. In the Russia-Ukraine war, both outlets maintained relatively stable positions; however, in the Israel-Hamas conflict, we identified larger political bias shifts, particularly in Guardian coverage, suggesting a more event-driven pattern of reporting bias. These variations suggest that LLMs are shaped not only by their training data and architecture, but also by underlying worldviews with associated political biases.", "date": "2026-01-05", "categories": []}
{"id": "2601.06154", "title": "BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots", "abstract": "Societies can become a conspiratorial society where there is a majority of humans that believe, and therefore spread, conspiracy theories. Artificial intelligence gave rise to social media bots that can spread conspiracies in an automated fashion. Currently, organizations combat the spread of conspiracies through manual fact-checking processes and the dissemination of counter-narratives. However, the effects of harnessing the same automation to create useful bots are not well explored. To address this, we create BotSim, an Agent-Based Model of a society in which useful bots are introduced into a small world network. These useful bots are: Info-Correction Bots, which correct bad information into good, and Good Bots, which put out good messaging. The simulated agents interact through generating, consuming and propagating information. Our results show that, left unchecked, Bad Bots can create a conspiratorial society, and this can be mitigated by either Info-Correction Bots or Good Bots; however, Good Bots are more efficient and sustainable than Info-Correction Bots . Proactive good messaging is more resource-effective than reactive information correction. With our observations, we expand the concept of bots as a malicious social media agent towards automated social media agent that can be used for both good and bad purposes. These results have implications for designing communication strategies to maintain a healthy social cyber ecosystem.", "date": "2026-01-06", "categories": []}
{"id": "2601.06658", "title": "What makes for an enjoyable protagonist? An analysis of character warmth and competence", "abstract": "Drawing on psychological and literary theory, we investigated whether the warmth and competence of movie protagonists predict IMDb ratings, and whether these effects vary across genres. Using 2,858 films and series from the Movie Scripts Corpus, we identified protagonists via AI-assisted annotation and quantified their warmth and competence with the LLM_annotate package ([1]; human-LLM agreement: r = .83). Preregistered Bayesian regression analyses revealed theory-consistent but small associations between both warmth and competence and audience ratings, while genre-specific interactions did not meaningfully improve predictions. Male protagonists were slightly less warm than female protagonists, and movies with male leads received higher ratings on average (an association that was multiple times stronger than the relationships between movie ratings and warmth/competence). These findings suggest that, although audiences tend to favor warm, competent characters, the effects on movie evaluations are modest, indicating that character personality is only one of many factors shaping movie ratings. AI-assisted annotation with LLM_annotate and gpt-4.1-mini proved effective for large-scale analyses but occasionally fell short of manually generated annotations.", "date": "2026-01-10", "categories": []}
{"id": "2601.07398", "title": "On Narrative: The Rhetorical Mechanisms of Online Polarisation", "abstract": "Polarisation research has demonstrated how people cluster in homogeneous groups with opposing opinions. However, this effect emerges not only through interaction between people, limiting communication between groups, but also between narratives, shaping opinions and partisan identities. Yet, how polarised groups collectively construct and negotiate opposing interpretations of reality, and whether narratives move between groups despite limited interactions, remains unexplored. To address this gap, we formalise the concept of narrative polarisation and demonstrate its measurement in 212 YouTube videos and 90,029 comments on the Israeli-Palestinian conflict. Based on structural narrative theory and implemented through a large language model, we extract the narrative roles assigned to central actors in two partisan information environments. We find that while videos produce highly polarised narratives, comments significantly reduce narrative polarisation, harmonising discourse on the surface level. However, on a deeper narrative level, recurring narrative motifs reveal additional differences between partisan groups.", "date": "2026-01-12", "categories": []}
{"id": "2512.05231", "title": "Unveiling Affective Polarization Trends in Parliamentary Proceedings", "abstract": "Recent years have seen an increase in polarized discourse worldwide, on various platforms. We propose a novel method for quantifying polarization, based on the emotional style of the discourse rather than on differences in ideological stands. Using measures of Valence, Arousal and Dominance, we detect signals of emotional discourse and use them to operationalize the concept of affective polarization. Applying this method to a recently released corpus of proceedings of the Knesset, the Israeli parliament (in Hebrew), we find that the emotional style of members of government differs from that of opposition members; and that the level of affective polarization, as reflected by this style, is significantly increasing with time.", "date": "2025-12-04", "categories": []}
{"id": "2601.04160", "title": "All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection", "abstract": "We introduce RFC Bench, a benchmark for evaluating large language models on financial misinformation under realistic news. RFC Bench operates at the paragraph level and captures the contextual complexity of financial news where meaning emerges from dispersed cues. The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis using paired original perturbed inputs. Experiments reveal a consistent pattern: performance is substantially stronger when comparative context is available, while reference free settings expose significant weaknesses, including unstable predictions and elevated invalid outputs. These results indicate that current models struggle to maintain coherent belief states without external grounding. By highlighting this gap, RFC Bench provides a structured testbed for studying reference free reasoning and advancing more reliable financial misinformation detection in real world settings.", "date": "2026-01-07", "categories": []}
{"id": "2601.05358", "title": "The Table of Media Bias Elements: A sentence-level taxonomy of media bias types and propaganda techniques", "abstract": "Public debates about \"left-\" or \"right-wing\" news overlook the fact that bias is usually conveyed by concrete linguistic manoeuvres that transcend any single political spectrum. We therefore shift the focus from where an outlet allegedly stands to how partiality is expressed in individual sentences. Drawing on 26,464 sentences collected from newsroom corpora, user submissions and our own browsing, we iteratively combine close-reading, interdisciplinary theory and pilot annotation to derive a fine-grained, sentence-level taxonomy of media bias and propaganda. The result is a two-tier schema comprising 38 elementary bias types, arranged in six functional families and visualised as a \"table of media-bias elements\". For each type we supply a definition, real-world examples, cognitive and societal drivers, and guidance for recognition. A quantitative survey of a random 155-sentence sample illustrates prevalence differences, while a cross-walk to the best-known NLP and communication-science taxonomies reveals substantial coverage gains and reduced ambiguity.", "date": "2026-01-08", "categories": []}
{"id": "2601.05835", "title": "Left, Right, or Center? Evaluating LLM Framing in News Classification and Generation", "abstract": "Large Language Model (LLM) based summarization and text generation are increasingly used for producing and rewriting text, raising concerns about political framing in journalism where subtle wording choices can shape interpretation. Across nine state-of-the-art LLMs, we study political framing by testing whether LLMs' classification-based bias signals align with framing behavior in their generated summaries. We first compare few-shot ideology predictions against LEFT/CENTER/RIGHT labels. We then generate \"steered\" summaries under FAITHFUL, CENTRIST, LEFT, and RIGHT prompts, and score all outputs using a single fixed ideology evaluator. We find pervasive ideological center-collapse in both article-level ratings and generated text, indicating a systematic tendency toward centrist framing. Among evaluated models, Grok 4 is by far the most ideologically expressive generator, while Claude Sonnet 4.5 and Llama 3.1 achieve the strongest bias-rating performance among commercial and open-weight models, respectively.", "date": "2026-01-09", "categories": []}
{"id": "2506.06842", "title": "PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation", "abstract": "Disinformation detection is a key aspect of media literacy. Psychological studies have shown that knowledge of persuasive fallacies helps individuals detect disinformation. Inspired by these findings, we experimented with large language models (LLMs) to test whether infusing persuasion knowledge enhances disinformation detection. As a result, we introduce the Persuasion-Augmented Chain of Thought (PCoT), a novel approach that leverages persuasion to improve disinformation detection in zero-shot classification. We extensively evaluate PCoT on online news and social media posts. Moreover, we publish two novel, up-to-date disinformation datasets: EUDisinfo and MultiDis. These datasets enable the evaluation of PCoT on content entirely unseen by the LLMs used in our experiments, as the content was published after the models' knowledge cutoffs. We show that, on average, PCoT outperforms competitive methods by 15% across five LLMs and five datasets. These findings highlight the value of persuasion in strengthening zero-shot disinformation detection.", "date": "2025-06-07", "categories": []}
{"id": "2511.07267", "title": "Beyond Detection: Exploring Evidence-based Multi-Agent Debate for Misinformation Intervention and Persuasion", "abstract": "Multi-agent debate (MAD) frameworks have emerged as promising approaches for misinformation detection by simulating adversarial reasoning. While prior work has focused on detection accuracy, it overlooks the importance of helping users understand the reasoning behind factual judgments and develop future resilience. The debate transcripts generated during MAD offer a rich but underutilized resource for transparent reasoning. In this study, we introduce ED2D, an evidence-based MAD framework that extends previous approach by incorporating factual evidence retrieval. More importantly, ED2D is designed not only as a detection framework but also as a persuasive multi-agent system aimed at correcting user beliefs and discouraging misinformation sharing. We compare the persuasive effects of ED2D-generated debunking transcripts with those authored by human experts. Results demonstrate that ED2D outperforms existing baselines across three misinformation detection benchmarks. When ED2D generates correct predictions, its debunking transcripts exhibit persuasive effects comparable to those of human experts; However, when ED2D misclassifies, its accompanying explanations may inadvertently reinforce users'misconceptions, even when presented alongside accurate human explanations. Our findings highlight both the promise and the potential risks of deploying MAD systems for misinformation intervention. We further develop a public community website to help users explore ED2D, fostering transparency, critical thinking, and collaborative fact-checking.", "date": "2025-11-10", "categories": []}
{"id": "2601.04251", "title": "Using Grok to Avoid Personal Attacks While Correcting Misinformation on X", "abstract": "Correcting misinformation in public online spaces often exposes users to hostility and ad hominem attacks, discouraging participation in corrective discourse. This study presents empirical evidence that invoking Grok, the native large language model on X, rather than directly confronting other users, is associated with different social responses during misinformation correction. Using an observational design, 100 correction replies across five high-conflict misinformation topics were analyzed, with corrections balanced between Grok-mediated and direct human-issued responses. The primary outcome was whether a correction received at least one ad hominem attack within a 24-hour window. Ad hominem attacks occurred in 72 percent of human-issued corrections and in none of the Grok-mediated corrections. A chi-square test confirmed a statistically significant association with a large effect size. These findings suggest that AI-mediated correction may alter the social dynamics of public disagreement by reducing interpersonal hostility during misinformation responses.", "date": "2026-01-06", "categories": []}
{"id": "2412.11835", "title": "Improved Models for Media Bias Detection and Subcategorization", "abstract": "We present improved models for the granular detection and sub-classification news media bias in English news articles. We compare the performance of zero-shot versus fine-tuned large pre-trained neural transformer language models, explore how the level of detail of the classes affects performance on a novel taxonomy of 27 news bias-types, and demonstrate how using synthetically generated example data can be used to improve quality", "date": "2024-12-16", "categories": []}
{"id": "2503.05720", "title": "That is Unacceptable: the Moral Foundations of Canceling", "abstract": "Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people canceling attitudes on social media. Specifically, we study the impact of annotators' morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator's judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.", "date": "2025-02-17", "categories": []}
{"id": "2601.01620", "title": "The Gray Area: Characterizing Moderator Disagreement on Reddit", "abstract": "Volunteer moderators play a crucial role in sustaining online dialogue, but they often disagree about what should or should not be allowed. In this paper, we study the complexity of content moderation with a focus on disagreements between moderators, which we term the ``gray area'' of moderation. Leveraging 5 years and 4.3 million moderation log entries from 24 subreddits of different topics and sizes, we characterize how gray area, or disputed cases, differ from undisputed cases. We show that one-in-seven moderation cases are disputed among moderators, often addressing transgressions where users' intent is not directly legible, such as in trolling and brigading, as well as tensions around community governance. This is concerning, as almost half of all gray area cases involved automated moderation decisions. Through information-theoretic evaluations, we demonstrate that gray area cases are inherently harder to adjudicate than undisputed cases and show that state-of-the-art language models struggle to adjudicate them. We highlight the key role of expert human moderators in overseeing the moderation process and provide insights about the challenges of current moderation processes and tools.", "date": "2026-01-04", "categories": []}
{"id": "2601.03860", "title": "PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media", "abstract": "Detecting hyperpartisan narratives and Population Replacement Conspiracy Theories (PRCT) is essential to addressing the spread of misinformation. These complex narratives pose a significant threat, as hyperpartisanship drives political polarisation and institutional distrust, while PRCTs directly motivate real-world extremist violence, making their identification critical for social cohesion and public safety. However, existing resources are scarce, predominantly English-centric, and often analyse hyperpartisanship, stance, and rhetorical bias in isolation rather than as interrelated aspects of political discourse. To bridge this gap, we introduce \\textsc{PartisanLens}, the first multilingual dataset of \\num{1617} hyperpartisan news headlines in Spanish, Italian, and Portuguese, annotated in multiple political discourse aspects. We first evaluate the classification performance of widely used Large Language Models (LLMs) on this dataset, establishing robust baselines for the classification of hyperpartisan and PRCT narratives. In addition, we assess the viability of using LLMs as automatic annotators for this task, analysing their ability to approximate human annotation. Results highlight both their potential and current limitations. Next, moving beyond standard judgments, we explore whether LLMs can emulate human annotation patterns by conditioning them on socio-economic and ideological profiles that simulate annotator perspectives. At last, we provide our resources and evaluation, \\textsc{PartisanLens} supports future research on detecting partisan and conspiratorial narratives in European contexts.", "date": "2026-01-07", "categories": []}
{"id": "2506.00634", "title": "Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads", "abstract": "Rental listings offer a window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional boundaries and neighborhood claims. Through manual and large language model annotation, we classify unstructured listings from Craigslist according to their neighborhood. Further geospatial analysis reveals three distinct patterns: properties with conflicting neighborhood designations due to competing spatial definitions, border properties with valid claims to adjacent neighborhoods, and \"reputation laundering\" where listings claim association with distant, desirable neighborhoods. Through topic modeling, we identify patterns that correlate with spatial positioning: listings further from neighborhood centers emphasize different amenities than centrally-located units. Natural language processing techniques reveal how definitions of urban spaces are contested in ways that traditional methods overlook.", "date": "2025-05-31", "categories": []}
{"id": "2601.02367", "title": "Cross-Platform Digital Discourse Analysis of the Israel-Hamas Conflict: Sentiment, Topics, and Event Dynamics", "abstract": "The Israeli-Palestinian conflict remains one of the most polarizing geopolitical issues, with the October 2023 escalation intensifying online debate. Social media platforms, particularly Telegram, have become central to real-time news sharing, advocacy, and propaganda. In this study, we analyze Telegram, Twitter/X, and Reddit to examine how conflict narratives are produced, amplified, and contested across different digital spheres. Building on our previous work on Telegram discourse during the 2023 escalation, we extend the analysis longitudinally and cross-platform using an updated dataset spanning October 2023 to mid-2025. The corpus includes more than 187,000 Telegram messages, 2.1 million Reddit comments, and curated Twitter/X posts. We combine Latent Dirichlet Allocation (LDA), BERTopic, and transformer-based sentiment and emotion models to identify dominant themes, emotional dynamics, and propaganda strategies. Telegram channels provide unfiltered, high-intensity documentation of events; Twitter/X amplifies frames to global audiences; and Reddit hosts more reflective and deliberative discussions. Our findings reveal persistent negative sentiment, strong coupling between humanitarian framing and solidarity expressions, and platform-specific pathways for the diffusion of pro-Palestinian and pro-Israeli narratives. This paper offers three contributions: (1) a multi-platform, FAIR-compliant dataset on the Israel-Hamas war, (2) an integrated pipeline combining topic modeling, sentiment and emotion analysis, and spam filtering for large-scale conflict discourse, and (3) empirical insights into how platform affordances and affective publics shape the evolution of digital conflict communication.", "date": "2025-11-27", "categories": []}
{"id": "2601.02412", "title": "Socially-Aware Recommender Systems Mitigate Opinion Clusterization", "abstract": "Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users preferences evolve under the influence of both suggested content from the recommender system and content shared within their social circles. This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of filter bubbles and opinion polarization. We develop a social network-aware recommender system that explicitly accounts for this user-creators feedback interaction and strategically exploits the topology of the user's own social network to promote diversification. Our approach highlights how accounting for and exploiting user's social network in the recommender system design is crucial to mediate filter bubble effects while balancing content diversity with personalization. Provably, opinion clusterization is positively correlated with the influence of recommended content on user opinions. Ultimately, the proposed approach shows the power of socially-aware recommender systems in combating opinion polarization and clusterization phenomena.", "date": "2026-01-02", "categories": []}
{"id": "2601.00797", "title": "The Qualitative Laboratory: Theory Prototyping and Hypothesis Generation with Large Language Models", "abstract": "A central challenge in social science is to generate rich qualitative hypotheses about how diverse social groups might interpret new information. This article introduces and illustrates a novel methodological approach for this purpose: sociological persona simulation using Large Language Models (LLMs), which we frame as a \"qualitative laboratory\". We argue that for this specific task, persona simulation offers a distinct advantage over established methods. By generating naturalistic discourse, it overcomes the lack of discursive depth common in vignette surveys, and by operationalizing complex worldviews through natural language, it bypasses the formalization bottleneck of rule-based agent-based models (ABMs). To demonstrate this potential, we present a protocol where personas derived from a sociological theory of climate reception react to policy messages. The simulation produced nuanced and counter-intuitive hypotheses - such as a conservative persona's rejection of a national security frame - that challenge theoretical assumptions. We conclude that this method, used as part of a \"simulation then validation\" workflow, represents a superior tool for generating deeply textured hypotheses for subsequent empirical testing.", "date": "2025-11-25", "categories": []}
{"id": "2601.00994", "title": "ElecTwit: A Framework for Studying Persuasion in Multi-Agent Social Systems", "abstract": "This paper introduces ElecTwit, a simulation framework designed to study persuasion within multi-agent systems, specifically emulating the interactions on social media platforms during a political election. By grounding our experiments in a realistic environment, we aimed to overcome the limitations of game-based simulations often used in prior research. We observed the comprehensive use of 25 specific persuasion techniques across most tested LLMs, encompassing a wider range than previously reported. The variations in technique usage and overall persuasion output between models highlight how different model architectures and training can impact the dynamics in realistic social simulations. Additionally, we observed unique phenomena such as \"kernel of truth\" messages and spontaneous developments with an \"ink\" obsession, where agents collectively demanded written proof. Our study provides a foundation for evaluating persuasive LLM agents in real-world contexts, ensuring alignment and preventing dangerous outcomes.", "date": "2026-01-02", "categories": []}
{"id": "2601.01073", "title": "Gendered Pathways in AI Companionship: Cross-Community Behavior and Toxicity Patterns on Reddit", "abstract": "AI-companionship platforms are rapidly reshaping how people form emotional, romantic, and parasocial bonds with non-human agents, raising new questions about how these relationships intersect with gendered online behavior and exposure to harmful content. Focusing on the MyBoyfriendIsAI (MBIA) subreddit, we reconstruct the Reddit activity histories of more than 3,000 highly engaged users over two years, yielding over 67,000 historical submissions. We then situate MBIA within a broader ecosystem by building a historical interaction network spanning more than 2,000 subreddits, which enables us to trace cross-community pathways and measure how toxicity and emotional expression vary across these trajectories. We find that MBIA users primarily traverse four surrounding community spheres (AI-companionship, porn-related, forum-like, and gaming) and that participation across the ecosystem exhibits a distinct gendered structure, with substantial engagement by female users. While toxicity is generally low across most pathways, we observe localized spikes concentrated in a small subset of AI-porn and gender-oriented communities. Nearly 16% of users engage with gender-focused subreddits, and their trajectories display systematically different patterns of emotional expression and elevated toxicity, suggesting that a minority of gendered pathways may act as toxicity amplifiers within the broader AI-companionship ecosystem. These results characterize the gendered structure of cross-community participation around AI companionship on Reddit and highlight where risks concentrate, informing measurement, moderation, and design practices for human-AI relationship platforms.", "date": "2026-01-03", "categories": []}
{"id": "2512.23835", "title": "Explaining News Bias Detection: A Comparative SHAP Analysis of Transformer Model Decision Mechanisms", "abstract": "Automated bias detection in news text is heavily used to support journalistic analysis and media accountability, yet little is known about how bias detection models arrive at their decisions or why they fail. In this work, we present a comparative interpretability study of two transformer-based bias detection models: a bias detector fine-tuned on the BABE dataset and a domain-adapted pre-trained RoBERTa model fine-tuned on the BABE dataset, using SHAP-based explanations. We analyze word-level attributions across correct and incorrect predictions to characterize how different model architectures operationalize linguistic bias. Our results show that although both models attend to similar categories of evaluative language, they differ substantially in how these signals are integrated into predictions. The bias detector model assigns stronger internal evidence to false positives than to true positives, indicating a misalignment between attribution strength and prediction correctness and contributing to systematic over-flagging of neutral journalistic content. In contrast, the domain-adaptive model exhibits attribution patterns that better align with prediction outcomes and produces 63\\% fewer false positives. We further demonstrate that model errors arise from distinct linguistic mechanisms, with false positives driven by discourse-level ambiguity rather than explicit bias cues. These findings highlight the importance of interpretability-aware evaluation for bias detection systems and suggest that architectural and training choices critically affect both model reliability and deployment suitability in journalistic contexts.", "date": "2025-12-29", "categories": []}
{"id": "2512.21439", "title": "Morality is Contextual: Learning Interpretable Moral Contexts from Human Data with Probabilistic Clustering and Large Language Models", "abstract": "Moral actions are judged not only by their outcomes but by the context in which they occur. We present COMETH (Contextual Organization of Moral Evaluation from Textual Human inputs), a framework that integrates a probabilistic context learner with LLM-based semantic abstraction and human moral evaluations to model how context shapes the acceptability of ambiguous actions. We curate an empirically grounded dataset of 300 scenarios across six core actions (violating Do not kill, Do not deceive, and Do not break the law) and collect ternary judgments (Blame/Neutral/Support) from N=101 participants. A preprocessing pipeline standardizes actions via an LLM filter and MiniLM embeddings with K-means, producing robust, reproducible core-action clusters. COMETH then learns action-specific moral contexts by clustering scenarios online from human judgment distributions using principled divergence criteria. To generalize and explain predictions, a Generalization module extracts concise, non-evaluative binary contextual features and learns feature weights in a transparent likelihood-based model. Empirically, COMETH roughly doubles alignment with majority human judgments relative to end-to-end LLM prompting (approx. 60% vs. approx. 30% on average), while revealing which contextual features drive its predictions. The contributions are: (i) an empirically grounded moral-context dataset, (ii) a reproducible pipeline combining human judgments with model-based context learning and LLM semantics, and (iii) an interpretable alternative to end-to-end LLMs for context-sensitive moral prediction and explanation.", "date": "2025-12-24", "categories": []}
{"id": "2507.01061", "title": "Epitome: Pioneering an Experimental Platform for AI-Social Science Integration", "abstract": "Large Language Models (LLMs) enable unprecedented social science experimentation by creating controlled hybrid human-AI environments. We introduce Epitome (www.epitome-ai.com), an open experimental platform that operationalizes this paradigm through Matrix-like social worlds where researchers can study isolated human subjects and groups interacting with LLM agents. This maintains ecological validity while enabling precise manipulation of social dynamics. Epitome approaches three frontiers: (1) methodological innovation using LLM confederates to reduce complexity while scaling interactions; (2) empirical investigation of human behavior in AI-saturated environments; and (3) exploration of emergent properties in hybrid collectives. Drawing on interdisciplinary foundations from management, communication, sociology, psychology, and ethics, the platform's modular architecture spans foundation model deployment through data collection. We validate Epitome through replication of three seminal experiments, demonstrating capacity to generate robust findings while reducing experimental complexity. This tool provides crucial insights for understanding how humans navigate AI-mediated social realities, knowledge essential for policy, education, and human-centered AI design.", "date": "2025-06-30", "categories": []}
{"id": "2512.19933", "title": "PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation", "abstract": "Traditional agent-based models (ABMs) of opinion dynamics often fail to capture the psychological heterogeneity driving online polarization due to simplistic homogeneity assumptions. This limitation obscures the critical interplay between individual cognitive biases and information propagation, thereby hindering a mechanistic understanding of how ideological divides are amplified. To address this challenge, we introduce the Personality-Refracted Intelligent Simulation Model (PRISM), a hybrid framework coupling stochastic differential equations (SDE) for continuous emotional evolution with a personality-conditional partially observable Markov decision process (PC-POMDP) for discrete decision-making. In contrast to continuous trait approaches, PRISM assigns distinct Myers-Briggs Type Indicator (MBTI) based cognitive policies to multimodal large language model (MLLM) agents, initialized via data-driven priors from large-scale social media datasets. PRISM achieves superior personality consistency aligned with human ground truth, significantly outperforming standard homogeneous and Big Five benchmarks. This framework effectively replicates emergent phenomena such as rational suppression and affective resonance, offering a robust tool for analyzing complex social media ecosystems.", "date": "2025-12-22", "categories": []}
{"id": "2512.18077", "title": "Characterising Behavioural Families and Dynamics of Promotional Twitter Bots via Sequence-Based Modelling", "abstract": "This paper asks whether promotional Twitter/X bots form behavioural families and whether members evolve similarly. We analyse 2,798,672 tweets from 2,615 ground-truth promotional bot accounts (2006-2021), focusing on complete years 2009 to 2020. Each bot is encoded as a sequence of symbolic blocks (``digital DNA'') from seven categorical post-level behavioural features (posting action, URL, media, text duplication, hashtags, emojis, sentiment), preserving temporal order only. Using non-overlapping blocks (k=7), cosine similarity over block-frequency vectors, and hierarchical clustering, we obtain four coherent families: Unique Tweeters, Duplicators with URLs, Content Multipliers, and Informed Contributors. Families share behavioural cores but differ systematically in engagement strategies and life-cycle dynamics (beginning/middle/end). We then model behavioural change as mutations. Within each family we align sequences via multiple sequence alignment (MSA) and label events as insertions, deletions, substitutions, alterations, and identity. This quantifies mutation rates, change-prone blocks/features, and mutation hotspots. Deletions and substitutions dominate, insertions are rare, and mutation profiles differ by family, with hotspots early for some families and dispersed for others. Finally, we test predictive value: bots within the same family share mutations more often than bots across families; closer bots share and propagate mutations more than distant ones; and responses to external triggers (e.g., Christmas, Halloween) follow family-specific, partly predictable patterns. Overall, sequence-based family modelling plus mutation analysis provides a fine-grained account of how promotional bot behaviour adapts over time.", "date": "2025-12-19", "categories": []}
{"id": "2411.07892", "title": "Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus", "abstract": "Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M podcast transcripts that is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. This data is not limited to text, but rather includes audio features and speaker turns for a subset of 370K episodes, and speaker role inferences and other metadata for all 1.1M episodes. Using this data, we also conduct a foundational investigation into the content, structure, and responsiveness of this ecosystem. Together, our data and analyses open the door to continued computational research of this popular and impactful medium.", "date": "2024-11-12", "categories": []}
{"id": "2512.17067", "title": "Bots Don't Sit Still: A Longitudinal Study of Bot Behaviour Change, Temporal Drift, and Feature-Structure Evolution", "abstract": "Social bots are now deeply embedded in online platforms for promotion, persuasion, and manipulation. Most bot-detection systems still treat behavioural features as static, implicitly assuming bots behave stationarily over time. We test that assumption for promotional Twitter bots, analysing change in both individual behavioural signals and the relationships between them. Using 2,615 promotional bot accounts and 2.8M tweets, we build yearly time series for ten content-based meta-features. Augmented Dickey-Fuller and KPSS tests plus linear trends show all ten are non-stationary: nine increase over time, while language diversity declines slightly.\n  Stratifying by activation generation and account age reveals systematic differences: second-generation bots are most active and link-heavy; short-lived bots show intense, repetitive activity with heavy hashtag/URL use; long-lived bots are less active but more linguistically diverse and use emojis more variably. We then analyse co-occurrence across generations using 18 interpretable binary features spanning actions, topic similarity, URLs, hashtags, sentiment, emojis, and media (153 pairs). Chi-square tests indicate almost all pairs are dependent. Spearman correlations shift in strength and sometimes polarity: many links (e.g. multiple hashtags with media; sentiment with URLs) strengthen, while others flip from weakly positive to weakly or moderately negative. Later generations show more structured combinations of cues.\n  Taken together, these studies provide evidence that promotional social bots adapt over time at both the level of individual meta-features and the level of feature interdependencies, with direct implications for the design and evaluation of bot-detection systems trained on historical behavioural features.", "date": "2025-12-18", "categories": []}
{"id": "2512.17347", "title": "Stakeholder Suite: A Unified AI Framework for Mapping Actors, Topics and Arguments in Public Debates", "abstract": "Public debates surrounding infrastructure and energy projects involve complex networks of stakeholders, arguments, and evolving narratives. Understanding these dynamics is crucial for anticipating controversies and informing engagement strategies, yet existing tools in media intelligence largely rely on descriptive analytics with limited transparency. This paper presents Stakeholder Suite, a framework deployed in operational contexts for mapping actors, topics, and arguments within public debates. The system combines actor detection, topic modeling, argument extraction and stance classification in a unified pipeline. Tested on multiple energy infrastructure projects as a case study, the approach delivers fine-grained, source-grounded insights while remaining adaptable to diverse domains. The framework achieves strong retrieval precision and stance accuracy, producing arguments judged relevant in 75% of pilot use cases. Beyond quantitative metrics, the tool has proven effective for operational use: helping project teams visualize networks of influence, identify emerging controversies, and support evidence-based decision-making.", "date": "2025-12-19", "categories": []}
{"id": "2512.16901", "title": "Impacts of Racial Bias in Historical Training Data for News AI", "abstract": "AI technologies have rapidly moved into business and research applications that involve large text corpora, including computational journalism research and newsroom settings. These models, trained on extant data from various sources, can be conceptualized as historical artifacts that encode decades-old attitudes and stereotypes. This paper investigates one such example trained on the broadly-used New York Times Annotated Corpus to create a multi-label classifier. Our use in research settings surfaced the concerning \"blacks\" thematic topic label. Through quantitative and qualitative means we investigate this label's use in the training corpus, what concepts it might be encoding in the trained classifier, and how those concepts impact our model use. Via the application of explainable AI methods, we find that the \"blacks\" label operates partially as a general \"racism detector\" across some minoritized groups. However, it performs poorly against expectations on modern examples such as COVID-19 era anti-Asian hate stories, and reporting on the Black Lives Matter movement. This case study of interrogating embedded biases in a model reveals how similar applications in newsroom settings can lead to unexpected outputs that could impact a wide variety of potential uses of any large language model-story discovery, audience targeting, summarization, etc. The fundamental tension this exposes for newsrooms is how to adopt AI-enabled workflow tools while reducing the risk of reproducing historical biases in news coverage.", "date": "2025-12-18", "categories": []}
{"id": "2510.04755", "title": "A New Digital Divide? Coder Worldviews, the Slop Economy, and Democracy in the Age of AI", "abstract": "Digital technologies are transforming democratic life in conflicting ways. This article bridges two perspectives to unpack these tensions. First, we present an original survey of software developers in Silicon Valley, interrogating how coder worldviews, ethics, and workplace cultures shape the democratic potential and social impact of the technologies they build. Results indicate that while most developers recognize the power of their products to influence civil liberties and political discourse, they often face ethical dilemmas and top-down pressures that can lead to design choices undermining democratic ideals. Second, we critically investigate these findings in the context of an emerging new digital divide, not of internet access but of information quality. We interrogate the survey findings in the context of the Slop Economy, in which billions of users unable to pay for high-quality content experience an internet dominated by low-quality, AI-generated ad-driven content. We find a reinforcing cycle between tech creator beliefs and the digital ecosystems they spawn. We discuss implications for democratic governance, arguing for more ethically informed design and policy interventions to help bridge the digital divide to ensure that technological innovation supports rather than subverts democratic values in the next chapter of the digital age.", "date": "2025-10-06", "categories": []}
{"id": "2512.14887", "title": "Integrating Large Language Models and Knowledge Graphs to Capture Political Viewpoints in News Media", "abstract": "News sources play a central role in democratic societies by shaping political and social discourse through specific topics, viewpoints and voices. Understanding these dynamics is essential for assessing whether the media landscape offers a balanced and fair account of public debate. In earlier work, we introduced a pipeline that, given a news corpus, i) uses a hybrid human-machine approach to identify the range of viewpoints expressed about a given topic, and ii) classifies relevant claims with respect to the identified viewpoints, defined as sets of semantically and ideologically congruent claims (e.g., positions arguing that immigration positively impacts the UK economy). In this paper, we improve this pipeline by i) fine-tuning Large Language Models (LLMs) for viewpoint classification and ii) enriching claim representations with semantic descriptions of relevant actors drawn from Wikidata. We evaluate our approach against alternative solutions on a benchmark centred on the UK immigration debate. Results show that while both mechanisms independently improve classification performance, their integration yields the best results, particularly when using LLMs capable of processing long inputs.", "date": "2025-12-16", "categories": []}
{"id": "2512.15547", "title": "When a Nation Speaks: Machine Learning and NLP in People's Sentiment Analysis During Bangladesh's 2024 Mass Uprising", "abstract": "Sentiment analysis, an emerging research area within natural language processing (NLP), has primarily been explored in contexts like elections and social media trends, but there remains a significant gap in understanding emotional dynamics during civil unrest, particularly in the Bangla language. Our study pioneers sentiment analysis in Bangla during a national crisis by examining public emotions amid Bangladesh's 2024 mass uprising. We curated a unique dataset of 2,028 annotated news headlines from major Facebook news portals, classifying them into Outrage, Hope, and Despair. Through Latent Dirichlet Allocation (LDA), we identified prevalent themes like political corruption and public protests, and analyzed how events such as internet blackouts shaped sentiment patterns. It outperformed multilingual transformers (mBERT: 67%, XLM-RoBERTa: 71%) and traditional machine learning methods (SVM and Logistic Regression: both 70%). These results highlight the effectiveness of language-specific models and offer valuable insights into public sentiment during political turmoil.", "date": "2025-12-17", "categories": []}
{"id": "2511.05310", "title": "Listening Between the Lines: Decoding Podcast Narratives with Language Modeling", "abstract": "Podcasts have become a central arena for shaping public opinion, making them a vital source for understanding contemporary discourse. Their typically unscripted, multi-themed, and conversational style offers a rich but complex form of data. To analyze how podcasts persuade and inform, we must examine their narrative structures -- specifically, the narrative frames they employ.\n  The fluid and conversational nature of podcasts presents a significant challenge for automated analysis. We show that existing large language models, typically trained on more structured text such as news articles, struggle to capture the subtle cues that human listeners rely on to identify narrative frames. As a result, current approaches fall short of accurately analyzing podcast narratives at scale.\n  To solve this, we develop and evaluate a fine-tuned BERT model that explicitly links narrative frames to specific entities mentioned in the conversation, effectively grounding the abstract frame in concrete details. Our approach then uses these granular frame labels and correlates them with high-level topics to reveal broader discourse trends. The primary contributions of this paper are: (i) a novel frame-labeling methodology that more closely aligns with human judgment for messy, conversational data, and (ii) a new analysis that uncovers the systematic relationship between what is being discussed (the topic) and how it is being presented (the frame), offering a more robust framework for studying influence in digital media.", "date": "2025-11-07", "categories": []}
{"id": "2507.19929", "title": "DynamiX: Large-Scale Dynamic Social Network Simulator", "abstract": "Understanding the intrinsic mechanisms of social platforms is an urgent demand to maintain social stability. The rise of large language models provides significant potential for social network simulations to capture attitude dynamics and reproduce collective behaviors. However, existing studies mainly focus on scaling up agent populations, neglecting the dynamic evolution of social relationships. To address this gap, we introduce DynamiX, a novel large-scale social network simulator dedicated to dynamic social network modeling. DynamiX uses a dynamic hierarchy module for selecting core agents with key characteristics at each timestep, enabling accurate alignment of real-world adaptive switching of user roles. Furthermore, we design distinct dynamic social relationship modeling strategies for different user types. For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances, simulating homogeneous connections, and autonomous behavior decisions. For ordinary users, we construct an inequality-oriented behavior decision-making module, effectively addressing unequal social interactions and capturing the patterns of relationship adjustments driven by multi-dimensional factors. Experimental results demonstrate that DynamiX exhibits marked improvements in attitude evolution simulation and collective behavior analysis compared to static networks. Besides, DynamiX opens a new theoretical perspective on follower growth prediction, providing empirical evidence for opinion leaders cultivation.", "date": "2025-07-26", "categories": []}
{"id": "2511.12010", "title": "Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles", "abstract": "We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges -- such as missing demographic attributes, missing wage data, and noisy occupation labels -- through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known as FewSOC that achieves accuracy significantly higher than the original occupation labels in the resume dataset. Analysis of 228,710 career trajectories reveals that intra-firm occupation change has been found to facilitate upward mobility most strongly, followed by inter-firm occupation change and inter-firm lateral move. Women and Black college graduates experience significantly lower returns from job changes than men and White peers. Multilevel sensitivity analyses confirm that these disparities are robust to cluster-level heterogeneity and reveal additional intersectional patterns.", "date": "2025-11-15", "categories": []}
{"id": "2512.11875", "title": "The Art of Storytelling in Authoritarian Regimes: Crafting State Narratives on Chinese Social Media", "abstract": "This article examines how authoritarian regimes construct state narratives about politically consequential events. Building on the narrative policy framework and existing research on authoritarian propaganda, we propose two dimensions that shape narrative construction: legitimacy implications -- whether events enhance or threaten regime legitimacy, and citizen verification capacity -- the extent to which citizens can evaluate official narratives through alternative sources. Using quantitative narrative analysis of Chinese social media posts by government, state media, and celebrity accounts, we extract subject-verb-object (SVO) triplets to map dominant narrative structures across four major events. Our findings show that legitimacy implications of the event shape regime's efforts in storytelling and the beliefs highlighted in the narratives, while citizen's verification capacity could balance the strategic choice between a top-down manipulation and bottom-up responsiveness of state narratives. Together, the results reveal propaganda as a complex process of narrative construction adaptive to specific contexts, offering new insights into how dynamic storytelling sustains authoritarian resilience.", "date": "2025-12-07", "categories": []}
{"id": "2512.13559", "title": "Verifying Rumors via Stance-Aware Structural Modeling", "abstract": "Verifying rumors on social media is critical for mitigating the spread of false information. The stances of conversation replies often provide important cues to determine a rumor's veracity. However, existing models struggle to jointly capture semantic content, stance information, and conversation strructure, especially under the sequence length constraints of transformer-based encoders. In this work, we propose a stance-aware structural modeling that encodes each post in a discourse with its stance signal and aggregates reply embedddings by stance category enabling a scalable and semantically enriched representation of the entire thread. To enhance structural awareness, we introduce stance distribution and hierarchical depth as covariates, capturing stance imbalance and the influence of reply depth. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms prior methods in the ability to predict truthfulness of a rumor. We also demonstrate that our model is versatile for early detection and cross-platfrom generalization.", "date": "2025-12-15", "categories": []}
{"id": "2512.05832", "title": "Heard or Halted? Gender, Interruptions, and Emotional Tone in U.S. Supreme Court Oral Arguments", "abstract": "This study examines how interruptions during U.S. Supreme Court oral arguments shape both the semantic content and emotional tone of advocates' speech, with a focus on gendered dynamics in judicial discourse. Using the ConvoKit Supreme Court Corpus (2010-2019), we analyze 12,663 speech chunks from advocate-justice interactions to assess whether interruptions alter the meaning of an advocate's argument and whether interruptions toward female advocates exhibit more negative emotional valence. Semantic shifts are quantified using GloVe-based sentence embeddings, while sentiment is measured through lexicon-based analysis. We find that semantic similarity between pre- and post-interruption speech remains consistently high, suggesting that interruptions do not substantially alter argumentative content. However, interruptions directed at female advocates contain significantly higher levels of negative sentiment. These results deepen empirical understanding of gendered communication in elite institutional settings and demonstrate the value of computational linguistic methods for studying power, discourse, and equity in judicial proceedings.", "date": "2025-12-05", "categories": []}
{"id": "2407.03551", "title": "Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums", "abstract": "The pervasive expectations about ideal body types in Western society can lead to body image concerns, dissatisfaction, and in extreme cases, eating disorders and other psychopathologies related to body image. While previous research has focused on online pro-anorexia communities glorifying the \"thin ideal,\" less attention has been given to the broader spectrum of body image concerns or how emerging disorders like muscle dysmorphia (\"bigorexia\") present on online platforms. To address this gap, we analyze 46 Reddit forums related to diet, fitness, and mental health. We map these communities along gender and body ideal dimensions, revealing distinct patterns of emotional expression and community support. Feminine-oriented communities, especially those endorsing the thin ideal, express higher levels of negative emotions and receive caring comments in response. In contrast, muscular ideal communities display less negativity, regardless of gender orientation, but receive aggressive compliments in response, marked by admiration and toxicity. Mental health discussions align more with thin ideal, feminine-leaning spaces. By uncovering these gendered emotional dynamics, our findings can inform the development of moderation strategies that foster supportive interactions while reducing exposure to harmful content.", "date": "2024-07-04", "categories": []}
{"id": "2312.08968", "title": "Detecting value-expressive text posts in Russian social media", "abstract": "Basic values are concepts or beliefs which pertain to desirable end-states and transcend specific situations. Studying personal values in social media can illuminate how and why societal values evolve especially when the stimuli-based methods, such as surveys, are inefficient, for instance, in hard-to-reach populations. On the other hand, user-generated content is driven by the massive use of stereotyped, culturally defined speech constructions rather than authentic expressions of personal values. We aimed to find a model that can accurately detect value-expressive posts in Russian social media VKontakte. A training dataset of 5,035 posts was annotated by three experts, 304 crowd-workers and ChatGPT. Crowd-workers and experts showed only moderate agreement in categorizing posts. ChatGPT was more consistent but struggled with spam detection. We applied an ensemble of human- and AI-assisted annotation involving active learning approach, subsequently trained several classification models using embeddings from various pre-trained transformer-based language models. The best performance was achieved with embeddings from a fine-tuned rubert-tiny2 model, yielding high value detection quality (F1 = 0.77, F1-macro = 0.83). This model provides a crucial step to a study of values within and between Russian social media users.", "date": "2023-12-14", "categories": []}
{"id": "2506.22930", "title": "Towards Explainable Bilingual Multimodal Misinformation Detection and Localization", "abstract": "The increasing realism of multimodal content has made misinformation more subtle and harder to detect, especially in news media where images are frequently paired with bilingual (e.g., Chinese-English) subtitles. Such content often includes localized image edits and cross-lingual inconsistencies that jointly distort meaning while remaining superficially plausible. We introduce BiMi, a bilingual multimodal framework that jointly performs region-level localization, cross-modal and cross-lingual consistency detection, and natural language explanation for misinformation analysis. To support generalization, BiMi integrates an online retrieval module that supplements model reasoning with up-to-date external context. We further release BiMiBench, a large-scale and comprehensive benchmark constructed by systematically editing real news images and subtitles, comprising 104,000 samples with realistic manipulations across visual and linguistic modalities. To enhance interpretability, we apply Group Relative Policy Optimization (GRPO) to improve explanation quality, marking the first use of GRPO in this domain. Extensive experiments demonstrate that BiMi outperforms strong baselines by up to +8.9 in classification accuracy, +15.9 in localization accuracy, and +2.5 in explanation BERTScore, advancing state-of-the-art performance in realistic, multilingual misinformation detection. Code, models, and datasets will be released.", "date": "2025-06-28", "categories": []}
{"id": "2512.06656", "title": "The Online Discourse of Virtual Reality and Anxiety", "abstract": "VR in the treatment of clinical concerns such as generalized anxiety disorder or social anxiety. VR has created additional pathways to support patient well-being and care. Understanding online discussion of what users think about this technology may further support its efficacy. The purpose of this study was to employ a corpus linguistic methodology to identify the words and word networks that shed light on the online discussion of virtual reality and anxiety. Using corpus linguistics, frequently used words in discussion along with collocation were identified by utilizing Sketch Engine software. The results of the study, based upon the English Trends corpus, identified VR, Oculus, and headset as the most frequently discussed within the VR and anxiety subcorpus. These results point to the development of the virtual system, along with the physical apparatus that makes viewing and engaging with the virtual environment possible. Additional results point to collocation of prepositional phrases such as of virtual reality, in virtual reality, and for virtual reality relating to the design, experience, and development, respectively. These findings offer new perspective on how VR and anxiety together are discussed in general discourse and offer pathways for future opportunities to support counseling needs through development and accessibility. Keywords: anxiety disorders, corpus linguistics, Sketch Engine, and virtual reality VR", "date": "2025-12-07", "categories": []}
{"id": "2512.07195", "title": "MASim: Multilingual Agent-Based Simulation for Social Science", "abstract": "Multi-agent role-playing has recently shown promise for studying social behavior with language agents, but existing simulations are mostly monolingual and fail to model cross-lingual interaction, an essential property of real societies. We introduce MASim, the first multilingual agent-based simulation framework that supports multi-turn interaction among generative agents with diverse sociolinguistic profiles. MASim offers two key analyses: (i) global public opinion modeling, by simulating how attitudes toward open-domain hypotheses evolve across languages and cultures, and (ii) media influence and information diffusion, via autonomous news agents that dynamically generate content and shape user behavior. To instantiate simulations, we construct the MAPS benchmark, which combines survey questions and demographic personas drawn from global population distributions. Experiments on calibration, sensitivity, consistency, and cultural case studies show that MASim reproduces sociocultural phenomena and highlights the importance of multilingual simulation for scalable, controlled computational social science.", "date": "2025-12-08", "categories": []}
{"id": "2511.13238", "title": "Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms", "abstract": "This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms, methods designed to infer latent political positions from textual data. These algorithms are widely used in political science, communication, computational social science, and computer science to estimate ideological preferences from parliamentary speeches, party manifestos, and social media. Over the past two decades, their development has closely followed broader NLP trends -- beginning with word-frequency models and most recently turning to large language models (LLMs). While this trajectory has greatly expanded the methodological toolkit, it has also produced a fragmented field that lacks systematic comparison and clear guidance for applied use. To address this gap, we identified 25 CT-IPE algorithms through a systematic literature review and conducted a manual content analysis of their modeling assumptions and development contexts. To compare them meaningfully, we introduce a conceptual framework that distinguishes how algorithms generate, capture, and aggregate textual variance. On this basis, we identify four methodological families -- word-frequency, topic modeling, word embedding, and LLM-based approaches -- and critically assess their assumptions, interpretability, scalability, and limitations. Our review offers three contributions. First, it provides a structured synthesis of two decades of algorithm development, clarifying how diverse methods relate to one another. Second, it translates these insights into practical guidance for applied researchers, highlighting trade-offs in transparency, technical requirements, and validation strategies that shape algorithm choice. Third, it emphasizes that differences in estimation outcomes across algorithms are themselves informative, underscoring the need for systematic benchmarking.", "date": "2025-11-17", "categories": []}
{"id": "2511.22809", "title": "AI summaries in online search influence users' attitudes", "abstract": "This study examined how AI-generated summaries, which have become visually prominent in online search results, affect how users think about different issues. In a preregistered randomized controlled experiment, participants (N = 2,004) viewed mock search result pages varying in the presence (vs. absence), placement (top vs. middle), and stance (benefit-framed vs. harm-framed) of AI-generated summaries across four publicly debated topics. Compared to a no-summary control group, participants exposed to AI-generated summaries reported issue attitudes, behavioral intentions, and policy support that aligned more closely with the AI summary stance. The summaries placed at the top of the page produced stronger shifts in users' issue attitudes (but not behavioral intentions or policy support) than those placed at the middle of the page. We also observed moderating effects from issue familiarity and general trust toward AI. In addition, users perceived the AI summaries more useful when it emphasized health harms versus benefits. These findings suggest that AI-generated search summaries can significantly shape public perceptions, raising important implications for the design and regulation of AI-integrated information ecosystems.", "date": "2025-11-27", "categories": []}
{"id": "2512.04639", "title": "When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit", "abstract": "AI-generated content and misinformation are increasingly prevalent on social networks. While prior research primarily examined textual misinformation, fewer studies have focused on visual content's role in virality. In this work, we present the first large-scale analysis of how misinformation and AI-generated images propagate through repost cascades across five ideologically diverse Reddit communities. By integrating textual sentiment, visual attributes, and diffusion metrics (e.g., time-to-first repost, community reach), our framework accurately predicts both immediate post-level virality (AUC=0.83) and long-term cascade-level spread (AUC=0.998). These findings offer essential insights for moderating synthetic and misleading visual content online.", "date": "2025-12-04", "categories": []}
{"id": "2512.03582", "title": "Fine-grained Narrative Classification in Biased News Articles", "abstract": "Narratives are the cognitive and emotional scaffolds of propaganda. They organize isolated persuasive techniques into coherent stories that justify actions, attribute blame, and evoke identification with ideological camps. In this paper, we propose a novel fine-grained narrative classification in biased news articles. We also explore article-bias classification as the precursor task to narrative classification and fine-grained persuasive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grained narrative dataset with multi-level annotation for analyzing propaganda in Indian news media. Our dataset INDI-PROP comprises 1,266 articles focusing on two polarizing socio-political events in recent times: CAA and the Farmers' protest. Each article is annotated at three hierarchical levels: (i) ideological article-bias (pro-government, pro-opposition, neutral), (ii) event-specific fine-grained narrative frames anchored in ideological polarity and communicative intent, and (iii) persuasive techniques. We propose FANTA and TPTC, two GPT-4o-mini guided multi-hop prompt-based reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomena by integrating information extraction and contextual framing for hierarchical reasoning. On the other hand, TPTC adopts systematic decomposition of persuasive cues via a two-stage approach. Our evaluation suggests substantial improvement over underlying baselines in each case.", "date": "2025-12-03", "categories": []}
{"id": "2512.01369", "title": "MARSAD: A Multi-Functional Tool for Real-Time Social Media Analysis", "abstract": "MARSAD is a multifunctional natural language processing (NLP) platform designed for real-time social media monitoring and analysis, with a particular focus on the Arabic-speaking world. It enables researchers and non-technical users alike to examine both live and archived social media content, producing detailed visualizations and reports across various dimensions, including sentiment analysis, emotion analysis, propaganda detection, fact-checking, and hate speech detection. The platform also provides secure data-scraping capabilities through API keys for accessing public social media data. MARSAD's backend architecture integrates flexible document storage with structured data management, ensuring efficient processing of large and multimodal datasets. Its user-friendly frontend supports seamless data upload and interaction.", "date": "2025-12-01", "categories": []}
{"id": "2512.01728", "title": "Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference", "abstract": "This paper investigates the detection of misinformation, which deceives readers by explicitly fabricating misleading content or implicitly omitting important information necessary for informed judgment. While the former has been extensively studied, omission-based deception remains largely overlooked, even though it can subtly guide readers toward false conclusions under the illusion of completeness. To pioneer in this direction, this paper presents OmiGraph, the first omission-aware framework for misinformation detection. Specifically, OmiGraph constructs an omission-aware graph for the target news by utilizing a contextual environment that captures complementary perspectives of the same event, thereby surfacing potentially omitted contents. Based on this graph, omission-oriented relation modeling is then proposed to identify the internal contextual dependencies, as well as the dynamic omission intents, formulating a comprehensive omission relation representation. Finally, to extract omission patterns for detection, OmiGraph introduces omission-aware message-passing and aggregation that establishes holistic deception perception by integrating the omission contents and relations. Experiments show that, by considering the omission perspective, our approach attains remarkable performance, achieving average improvements of +5.4% F1 and +5.3% ACC on two large-scale benchmarks.", "date": "2025-12-01", "categories": []}
{"id": "2411.14652", "title": "Reranking partisan animosity in algorithmic social media feeds alters affective polarization", "abstract": "Today, social media platforms hold sole power to study the effects of feed ranking algorithms. We developed a platform-independent method that reranks participants' feeds in real-time and used this method to conduct a preregistered 10-day field experiment with 1,256 participants on X during the 2024 U.S. presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by two points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.", "date": "2024-11-22", "categories": []}
{"id": "2511.22109", "title": "A Hybrid Theory and Data-driven Approach to Persuasion Detection with Large Language Models", "abstract": "Traditional psychological models of belief revision focus on face-to-face interactions, but with the rise of social media, more effective models are needed to capture belief revision at scale, in this rich text-based online discourse. Here, we use a hybrid approach, utilizing large language models (LLMs) to develop a model that predicts successful persuasion using features derived from psychological experiments.\n  Our approach leverages LLM generated ratings of features previously examined in the literature to build a random forest classification model that predicts whether a message will result in belief change. Of the eight features tested, \\textit{epistemic emotion} and \\textit{willingness to share} were the top-ranking predictors of belief change in the model. Our findings provide insights into the characteristics of persuasive messages and demonstrate how LLMs can enhance models of successful persuasion based on psychological theory. Given these insights, this work has broader applications in fields such as online influence detection and misinformation mitigation, as well as measuring the effectiveness of online narratives.", "date": "2025-11-27", "categories": []}
{"id": "2410.18122", "title": "Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models", "abstract": "This article introduces misinfo-general, a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalization. Misinformation changes rapidly, much more quickly than moderators can annotate at scale, resulting in a shift between the training and inference data distributions. As a result, misinformation detectors need to be able to perform out-of-distribution generalization, an attribute they currently lack. Our benchmark uses distant labelling to enable simulating covariate shifts in misinformation content. We identify time, event, topic, publisher, political bias, misinformation type as important axes for generalization, and we evaluate a common class of baseline models on each. Using article metadata, we show how this model fails desiderata, which is not necessarily obvious from classification metrics. Finally, we analyze properties of the data to ensure limited presence of modelling shortcuts. We make the dataset and accompanying code publicly available: https://github.com/ioverho/misinfo-general", "date": "2024-10-12", "categories": []}
{"id": "2505.18685", "title": "From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation", "abstract": "Infodemics and health misinformation have significant negative impact on individuals and society, exacerbating confusion and increasing hesitancy in adopting recommended health measures. Recent advancements in generative AI, capable of producing realistic, human like text and images, have significantly accelerated the spread and expanded the reach of health misinformation, resulting in an alarming surge in its dissemination. To combat the infodemics, most existing work has focused on developing misinformation datasets from social media and fact checking platforms, but has faced limitations in topical coverage, inclusion of AI generation, and accessibility of raw content. To address these issues, we present MM Health, a large scale multimodal misinformation dataset in the health domain consisting of 34,746 news article encompassing both textual and visual information. MM Health includes human-generated multimodal information (5,776 articles) and AI generated multimodal information (28,880 articles) from various SOTA generative AI models. Additionally, We benchmarked our dataset against three tasks (reliability checks, originality checks, and fine-grained AI detection) demonstrating that existing SOTA models struggle to accurately distinguish the reliability and origin of information. Our dataset aims to support the development of misinformation detection across various health scenarios, facilitating the detection of human and machine generated content at multimodal levels.", "date": "2025-05-24", "categories": []}
{"id": "2310.18089", "title": "Lost in translation: using global fact-checks to measure multilingual misinformation prevalence, spread, and evolution", "abstract": "Misinformation and disinformation are growing threats in the digital age, affecting people across languages and borders. However, no research has investigated the prevalence of multilingual misinformation and quantified the extent to which misinformation diffuses across languages. This paper investigates the prevalence and dynamics of multilingual misinformation through an analysis of 264,487 fact-checks spanning 95 languages. To study the evolution of claims over time and mutations across languages, we represent fact-checks with multilingual sentence embeddings and build a graph where semantically similar claims are linked. We provide quantitative evidence of repeated fact-checking efforts and establish that claims diffuse across languages. Specifically, we find that while the majority of misinformation claims are only fact-checked once, 10.26%, corresponding to more than 27,000 claims, are checked multiple times. Using fact-checks as a proxy for the spread of misinformation, we find 32.26% of repeated claims cross linguistic boundaries, suggesting that some misinformation permeates language barriers. However, spreading patterns exhibit strong assortativity, with misinformation more likely to spread within the same language or language family. Next we show that fact-checkers take more time to fact-check claims that have crossed language barriers and model the temporal and cross-lingual evolution of claims. We analyze connected components and shortest paths connecting different versions of a claim finding that claims gradually drift over time and undergo greater alteration when traversing languages. Misinformation changes over time, reducing the effectiveness of static claim matching algorithms. The findings advocate for expanded information sharing between fact-checkers globally while underscoring the importance of localized verification.", "date": "2023-10-27", "categories": []}
{"id": "2511.17574", "title": "Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation", "abstract": "In the past two decades, open access to news and information has increased rapidly, empowering educated political growth within democratic societies. News recommender systems (NRSs) have shown to be useful in this process, minimizing political disengagement and information overload by providing individuals with articles on topics that matter to them. Unfortunately, NRSs often conflate underlying user interest with the partisan bias of the articles in their reading history and with the most popular biases present in the coverage of their favored topics. Over extended interaction, this can result in the formation of filter bubbles and the polarization of user partisanship. In this paper, we propose a novel embedding space called Constructed Political Coordinates (CPC), which models the political partisanship of users over a given topic-space, relative to a larger sample population. We apply a simple collaborative filtering (CF) framework using CPC-based correlation to recommend articles sourced from oppositional users, who have different biases from the user in question. We compare against classical CF methods and find that CPC-based methods promote pointed bias diversity and better match the true political tolerance of users, while classical methods implicitly exploit biases to maximize interaction.", "date": "2025-11-14", "categories": []}
{"id": "2511.17682", "title": "A Cross-Cultural Assessment of Human Ability to Detect LLM-Generated Fake News about South Africa", "abstract": "This study investigates how cultural proximity affects the ability to detect AI-generated fake news by comparing South African participants with those from other nationalities. As large language models increasingly enable the creation of sophisticated fake news, understanding human detection capabilities becomes crucial, particularly across different cultural contexts. We conducted a survey where 89 participants (56 South Africans, 33 from other nationalities) evaluated 10 true South African news articles and 10 AI-generated fake versions. Results reveal an asymmetric pattern: South Africans demonstrated superior performance in detecting true news about their country (40% deviation from ideal rating) compared to other participants (52%), but performed worse at identifying fake news (62% vs. 55%). This difference may reflect South Africans' higher overall trust in news sources. Our analysis further shows that South Africans relied more on content knowledge and contextual understanding when judging credibility, while participants from other countries emphasised formal linguistic features such as grammar and structure. Overall, the deviation from ideal rating was similar between groups (51% vs. 53%), suggesting that cultural familiarity appears to aid verification of authentic information but may also introduce bias when evaluating fabricated content. These insights contribute to understanding cross-cultural dimensions of misinformation detection and inform strategies for combating AI-generated fake news in increasingly globalised information ecosystems where content crosses cultural and geographical boundaries.", "date": "2025-11-21", "categories": []}
{"id": "2511.17746", "title": "Computational frame analysis revisited: On LLMs for studying news coverage", "abstract": "Computational approaches have previously shown various promises and pitfalls when it comes to the reliable identification of media frames. Generative LLMs like GPT and Claude are increasingly being used as content analytical tools, but how effective are they for frame analysis? We address this question by systematically evaluating them against their computational predecessors: bag-of-words models and encoder-only transformers; and traditional manual coding procedures. Our analysis rests on a novel gold standard dataset that we inductively and iteratively developed through the study, investigating six months of news coverage of the US Mpox epidemic of 2022. While we discover some potential applications for generative LLMs, we demonstrate that they were consistently outperformed by manual coders, and in some instances, by smaller language models. Some form of human validation was always necessary to determine appropriate model choice. Additionally, by examining how the suitability of various approaches depended on the nature of different tasks that were part of our frame analytical workflow, we provide insights as to how researchers may leverage the complementarity of these approaches to use them in tandem. We conclude by endorsing a methodologically pluralistic approach and put forth a roadmap for computational frame analysis for researchers going forward.", "date": "2025-11-21", "categories": []}
{"id": "2507.10810", "title": "Testing Hypotheses from the Social Approval Theory of Online Hate: An Analysis of 110 Million Messages from Parler", "abstract": "We examined how online hate is motivated by receiving social approval via Walther's (2024) social approval theory of online hate, which argues (H1a) more signals of social approval on hate messages predicts more subsequent hate messages, and (H1b) as social approval increases, hate speech becomes more extreme. Using 110 million messages from Parler (2018-2021), we observed the number of upvotes received on a hate speech post was unassociated with hate speech in one's next post and during the next month, three-months, and six-months. The number of upvotes received on (extreme) hate speech comments, however, was positively associated with (extreme) hate speech during the next week, month, three-months, and six-months. Between-person effects revealed an average positive relationship between social approval and hate speech production at all time intervals. For comments, social approval linked more strongly to online hate than social disapproval. Social approval is a critical mechanism facilitating online hate propagation.", "date": "2025-07-14", "categories": []}
{"id": "2511.16832", "title": "The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media", "abstract": "In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 2019) and three years after the outbreak was first reported (2020 to 2022). Drawing on theories from social cognition and the stereotype content model in Social Psychology, we analyze how English speakers talk about vaccines on social media to understand the evolving narrative around vaccines in social media posts. To do that, we first introduce a novel dataset comprising 18.7 million curated posts on vaccine discourse from 2013 to 2022. This extensive collection-filtered down from an initial 129 million posts through rigorous preprocessing-captures both pre-COVID and COVID-19 periods, offering valuable insights into the evolution of English-speaking X users' perceptions related to vaccines. Our analysis shows that the COVID-19 pandemic led to complex shifts in X users' sentiment and discourse around vaccines. We observe that negative emotion word usage decreased during the pandemic, with notable rises in usage of surprise, and trust related emotion words. Furthermore, vaccine-related language tended to use more warmth-focused words associated with trustworthiness, along with positive, competence-focused words during the early days of the pandemic, with a marked rise in negative word usage towards the end of the pandemic, possibly reflecting a growing vaccine hesitancy and skepticism.", "date": "2025-11-20", "categories": []}
{"id": "2511.17241", "title": "Social-Media Based Personas Challenge: Hybrid Prediction of Common and Rare User Actions on Bluesky", "abstract": "Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus primarily on common actions like retweeting and liking, the prediction of rare but significant behaviors remains largely unexplored. This paper presents a hybrid methodology for social media user behavior prediction that addresses both frequent and infrequent actions across a diverse action vocabulary. We evaluate our approach on a large-scale Bluesky dataset containing 6.4 million conversation threads spanning 12 distinct user actions across 25 persona clusters. Our methodology combines four complementary approaches: (i) a lookup database system based on historical response patterns; (ii) persona-specific LightGBM models with engineered temporal and semantic features for common actions; (iii) a specialized hybrid neural architecture fusing textual and temporal representations for rare action classification; and (iv) generation of text replies. Our persona-specific models achieve an average macro F1-score of 0.64 for common action prediction, while our rare action classifier achieves 0.56 macro F1-score across 10 rare actions. These results demonstrate that effective social media behavior prediction requires tailored modeling strategies recognizing fundamental differences between action types. Our approach achieved first place in the SocialSim: Social-Media Based Personas challenge organized at the Social Simulation with LLMs workshop at COLM 2025.", "date": "2025-11-21", "categories": []}
{"id": "2511.15857", "title": "A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios", "abstract": "Similar to social media bots that shape public opinion, healthcare and financial decisions, LLM-based ChatBots like ChatGPT can persuade users to alter their behavior. Unlike prior work that persuades via overt-partisan bias or misinformation, we test whether framing alone suffices. We conducted a crowdsourced study, where 336 participants interacted with a neutral or one of two value-framed ChatBots while deciding to alter US defense spending. In this single policy domain with controlled content, participants exposed to value-framed ChatBots significantly changed their budget choices relative to the neutral control. When the frame misaligned with their values, some participants reinforced their original preference, revealing a potentially replicable backfire effect, originally considered rare in the literature. These findings suggest that value-framing alone lowers the barrier for manipulative uses of LLMs, revealing risks distinct from overt bias or misinformation, and clarifying risks to countering misinformation.", "date": "2025-11-19", "categories": []}
{"id": "2511.16191", "title": "CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality", "abstract": "Rumor detection on social media remains a challenging task due to the complex propagation dynamics and the limited interpretability of existing models. While recent neural architectures capture content and structural features, they often fail to reveal the underlying causal mechanisms of misinformation spread. We propose CausalMamba, a novel framework that integrates Mamba-based sequence modeling, graph convolutional networks (GCNs), and differentiable causal discovery via NOTEARS. CausalMamba learns joint representations of temporal tweet sequences and reply structures, while uncovering latent causal graphs to identify influential nodes within each propagation chain. Experiments on the Twitter15 dataset show that our model achieves competitive classification performance compared to strong baselines, and uniquely enables counterfactual intervention analysis. Qualitative results demonstrate that removing top-ranked causal nodes significantly alters graph connectivity, offering interpretable insights into rumor dynamics. Our framework provides a unified approach for rumor classification and influence analysis, paving the way for more explainable and actionable misinformation detection systems.", "date": "2025-11-20", "categories": []}
{"id": "2511.14930", "title": "Fifty Shades of Greenwashing: The Political Economy of Climate Change Advertising on Social Media", "abstract": "In this paper, we provide a novel measure for greenwashing -- i.e., climate-related misinformation -- that shows how polluting companies can use social media advertising related to climate change to redirect criticism. To do so, we identify greenwashing content in 11 million social-political ads in Meta's Ad Targeting Datset with a measurement technique that combines large language models, human coders, and advances in Bayesian item response theory. We show that what is called greenwashing has diverse actors and components, but we also identify a very pernicious form, which we call political greenwashing, that appears to be promoted by fossil fuel companies and related interest groups. Based on ad targeting data, we show that much of this advertising happens via organizations with undisclosed links to the fossil fuel industry. Furthermore, we show that greenwashing ad content is being micro-targeted at left-leaning communities with fossil fuel assets, though we also find comparatively little evidence of ad targeting aimed at influencing public opinion at the national level.", "date": "2025-11-18", "categories": []}
{"id": "2511.03758", "title": "Leveraging LLM-based agents for social science research: insights from citation network simulations", "abstract": "The emergence of Large Language Models (LLMs) demonstrates their potential to encapsulate the logic and patterns inherent in human behavior simulation by leveraging extensive web data pre-training. However, the boundaries of LLM capabilities in social simulation remain unclear. To further explore the social attributes of LLMs, we introduce the CiteAgent framework, designed to generate citation networks based on human-behavior simulation with LLM-based agents. CiteAgent successfully captures predominant phenomena in real-world citation networks, including power-law distribution, citational distortion, and shrinking diameter. Building on this realistic simulation, we establish two LLM-based research paradigms in social science: LLM-SE (LLM-based Survey Experiment) and LLM-LE (LLM-based Laboratory Experiment). These paradigms facilitate rigorous analyses of citation network phenomena, allowing us to validate and challenge existing theories. Additionally, we extend the research scope of traditional science of science studies through idealized social experiments, with the simulation experiment results providing valuable insights for real-world academic environments. Our work demonstrates the potential of LLMs for advancing science of science research in social science.", "date": "2025-11-05", "categories": []}
{"id": "2411.03895", "title": "Computational Analysis of Gender Depiction in the Comedias of Calder\\'on de la Barca", "abstract": "In theatre, playwrights use the portrayal of characters to explore culturally based gender norms. In this paper, we develop quantitative methods to study gender depiction in the non-religious works (comedias) of Pedro Calder\\'on de la Barca, a prolific Spanish 17th century author. We gather insights from a corpus of more than 100 plays by using a gender classifier and applying model explainability (attribution) methods to determine which text features are most influential in the model's decision to classify speech as 'male' or 'female', indicating the most gendered elements of dialogue in Calder\\'on's comedias in a human accessible manner. We find that female and male characters are portrayed differently and can be identified by the gender prediction model at practically useful accuracies (up to f=0.83). Analysis reveals semantic aspects of gender portrayal, and demonstrates that the model is even useful in providing a relatively accurate scene-by-scene prediction of cross-dressing characters.", "date": "2024-11-06", "categories": []}
{"id": "2511.10542", "title": "Two Americas of Well-Being: Divergent Rural-Urban Patterns of Life Satisfaction and Happiness from 2.6 B Social Media Posts", "abstract": "Using 2.6 billion geolocated social-media posts (2014-2022) and a fine-tuned generative language model, we construct county-level indicators of life satisfaction and happiness for the United States. We document an apparent rural-urban paradox: rural counties express higher life satisfaction while urban counties exhibit greater happiness. We reconcile this by treating the two as distinct layers of subjective well-being, evaluative vs. hedonic, showing that each maps differently onto place, politics, and time. Republican-leaning areas appear more satisfied in evaluative terms, but partisan gaps in happiness largely flatten outside major metros, indicating context-dependent political effects. Temporal shocks dominate the hedonic layer: happiness falls sharply during 2020-2022, whereas life satisfaction moves more modestly. These patterns are robust across logistic and OLS specifications and align with well-being theory. Interpreted as associations for the population of social-media posts, the results show that large-scale, language-based indicators can resolve conflicting findings about the rural-urban divide by distinguishing the type of well-being expressed, offering a transparent, reproducible complement to traditional surveys.", "date": "2025-11-13", "categories": []}
{"id": "2511.07593", "title": "Leveraging the Power of AI and Social Interactions to Restore Trust in Public Polls", "abstract": "The emergence of crowdsourced data has significantly reshaped social science, enabling extensive exploration of collective human actions, viewpoints, and societal dynamics. However, ensuring safe, fair, and reliable participation remains a persistent challenge. Traditional polling methods have seen a notable decline in engagement over recent decades, raising concerns about the credibility of collected data. Meanwhile, social and peer-to-peer networks have become increasingly widespread, but data from these platforms can suffer from credibility issues due to fraudulent or ineligible participation. In this paper, we explore how social interactions can help restore credibility in crowdsourced data collected over social networks. We present an empirical study to detect ineligible participation in a polling task through AI-based graph analysis of social interactions among imperfect participants composed of honest and dishonest actors. Our approach focuses solely on the structure of social interaction graphs, without relying on the content being shared. We simulate different levels and types of dishonest behavior among participants who attempt to propagate the task within their social networks. We conduct experiments on real-world social network datasets, using different eligibility criteria and modeling diverse participation patterns. Although structural differences in social interaction graphs introduce some performance variability, our study achieves promising results in detecting ineligibility across diverse social and behavioral profiles, with accuracy exceeding 90% in some configurations.", "date": "2025-11-10", "categories": []}
{"id": "2411.00702", "title": "Extracting narrative signals from public discourse: a network-based approach", "abstract": "Narratives are key interpretative devices by which humans make sense of political reality. As the significance of narratives for understanding current societal issues such as polarization and misinformation becomes increasingly evident, there is a growing demand for methods that support their empirical analysis. To this end, we propose a graph-based formalism and machine-guided method for extracting, representing, and analyzing selected narrative signals from digital textual corpora, based on Abstract Meaning Representation (AMR). The formalism and method introduced here specifically cater to the study of political narratives that figure in texts from digital media such as archived political speeches, social media posts, transcripts of parliamentary debates, and political manifestos on party websites. We approach the study of such political narratives as a problem of information retrieval: starting from a textual corpus, we first extract a graph-like representation of the meaning of each sentence in the corpus using AMR. Drawing on transferable concepts from narratology, we then apply a set of heuristics to filter these graphs for representations of 1) actors and their relationships, 2) the events in which these actors figure, and 3) traces of the perspectivization of these events. We approach these references to actors, events, and instances of perspectivization as core narrative signals that allude to larger political narratives. By systematically analyzing and re-assembling these signals into networks that guide the researcher to the relevant parts of the text, the underlying narratives can be reconstructed through a combination of distant and close reading. A case study of State of the European Union addresses (2010 -- 2023) demonstrates how the formalism can be used to inductively surface signals of political narratives from public discourse.", "date": "2024-11-01", "categories": []}
{"id": "2511.04692", "title": "SARC: Sentiment-Augmented Deep Role Clustering for Fake News Detection", "abstract": "Fake news detection has been a long-standing research focus in social networks. Recent studies suggest that incorporating sentiment information from both news content and user comments can enhance detection performance. However, existing approaches typically treat sentiment features as auxiliary signals, overlooking role differentiation, that is, the same sentiment polarity may originate from users with distinct roles, thereby limiting their ability to capture nuanced patterns for effective detection. To address this issue, we propose SARC, a Sentiment-Augmented Role Clustering framework which utilizes sentiment-enhanced deep clustering to identify user roles for improved fake news detection. The framework first generates user features through joint comment text representation (with BiGRU and Attention mechanism) and sentiment encoding. It then constructs a differentiable deep clustering module to automatically categorize user roles. Finally, unlike existing approaches which take fake news label as the unique supervision signal, we propose a joint optimization objective integrating role clustering and fake news detection to further improve the model performance. Experimental results on two benchmark datasets, RumourEval-19 and Weibo-comp, demonstrate that SARC achieves superior performance across all metrics compared to baseline models. The code is available at: https://github.com/jxshang/SARC.", "date": "2025-10-28", "categories": []}
{"id": "2511.03915", "title": "The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013--2023", "abstract": "Quantifying human flourishing, a multidimensional construct including happiness, health, purpose, virtue, relationships, and financial stability, is critical for understanding societal well-being beyond economic indicators. Existing measures often lack fine spatial and temporal resolution. Here we introduce the Human Flourishing Geographic Index (HFGI), derived from analyzing approximately 2.6 billion geolocated U.S. tweets (2013-2023) using fine-tuned large language models to classify expressions across 48 indicators aligned with Harvard's Global Flourishing Study framework plus attitudes towards migration and perception of corruption. The dataset offers monthly and yearly county- and state-level indicators of flourishing-related discourse, validated to confirm that the measures accurately represent the underlying constructs and show expected correlations with established indicators. This resource enables multidisciplinary analyses of well-being, inequality, and social change at unprecedented resolution, offering insights into the dynamics of human flourishing as reflected in social media discourse across the United States over the past decade.", "date": "2025-11-05", "categories": []}
{"id": "2410.16301", "title": "Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models", "abstract": "The recent wave of artificial intelligence, epitomized by large language models (LLMs),has presented opportunities and challenges for methodological innovation in political science,sparking discussions on a potential paradigm shift in the social sciences. However, how can weunderstand the impact of LLMs on knowledge production and paradigm transformation in thesocial sciences from a comprehensive perspective that integrates technology and methodology? What are LLMs' specific applications and representative innovative methods in political scienceresearch? These questions, particularly from a practical methodological standpoint, remainunderexplored. This paper proposes the \"Intelligent Computing Social Modeling\" (ICSM) methodto address these issues by clarifying the critical mechanisms of LLMs. ICSM leverages thestrengths of LLMs in idea synthesis and action simulation, advancing intellectual exploration inpolitical science through \"simulated social construction\" and \"simulation validation.\" Bysimulating the U.S. presidential election, this study empirically demonstrates the operationalpathways and methodological advantages of ICSM. By integrating traditional social scienceparadigms, ICSM not only enhances the quantitative paradigm's capability to apply big data toassess the impact of factors but also provides qualitative paradigms with evidence for socialmechanism discovery at the individual level, offering a powerful tool that balances interpretabilityand predictability in social science research. The findings suggest that LLMs will drivemethodological innovation in political science through integration and improvement rather thandirect substitution.", "date": "2024-10-07", "categories": []}
{"id": "2511.03378", "title": "Beyond Citations: Measuring Idea-level Knowledge Diffusion from Research to Journalism and Policy-making", "abstract": "Despite the importance of social science knowledge for various stakeholders, measuring its diffusion into different domains remains a challenge. This study uses a novel text-based approach to measure the idea-level diffusion of social science knowledge from the research domain to the journalism and policy-making domains. By doing so, we expand the detection of knowledge diffusion beyond the measurements of direct references. Our study focuses on media effects theories as key research ideas in the field of communication science. Using 72,703 documents (2000-2019) from three domains (i.e., research, journalism, and policy-making) that mention these ideas, we count the mentions of these ideas in each domain, estimate their domain-specific contexts, and track and compare differences across domains and over time. Overall, we find that diffusion patterns and dynamics vary considerably between ideas, with some ideas diffusing between other domains, while others do not. Based on the embedding regression approach, we compare contextualized meanings across domains and find that the distances between research and policy are typically larger than between research and journalism. We also find that ideas largely shift roles across domains - from being the theories themselves in research to sense-making in news to applied, administrative use in policy. Over time, we observe semantic convergence mainly for ideas that are practically oriented. Our results characterize the cross-domain diffusion patterns and dynamics of social science knowledge at the idea level, and we discuss the implications for measuring knowledge diffusion beyond citations.", "date": "2025-11-05", "categories": []}
{"id": "2511.02370", "title": "AI Credibility Signals Outrank Institutions and Engagement in Shaping News Perception on Social Media", "abstract": "AI-generated content is rapidly becoming a salient component of online information ecosystems, yet its influence on public trust and epistemic judgments remains poorly understood. We present a large-scale mixed-design experiment (N = 1,000) investigating how AI-generated credibility scores affect user perception of political news. Our results reveal that AI feedback significantly moderates partisan bias and institutional distrust, surpassing traditional engagement signals such as likes and shares. These findings demonstrate the persuasive power of generative AI and suggest a need for design strategies that balance epistemic influence with user autonomy.", "date": "2025-11-04", "categories": []}
{"id": "2412.14500", "title": "The Digital Ecosystem of Beliefs: does evolution favour AI over humans?", "abstract": "As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understand such questions, this paper proposes the Digital Ecosystem of Beliefs (Digico), the first evolutionary framework for controlled experimentation with multi-population interactions in simulated social networks. Following a Universal Darwinism approach, the framework models a population of agents which change their messaging strategies due to evolutionary updates. They interact via messages, update their beliefs following a contagion model, and maintain their beliefs through cognitive Lamarckian inheritance. Initial experiments with Digico implement two types of agents, which are modelled to represent AIs vs humans based on higher rates of communication, higher rates of evolution, seeding fixed beliefs with propaganda aims, and higher influence on the recommendation algorithm. These experiments show that: a) when AIs have faster messaging, evolution, and more influence on the recommendation algorithm, they get 80% to 95% of the views; b) AIs designed for propaganda can typically convince 50% of humans to adopt extreme beliefs, and up to 85% when agents believe only a limited number of channels; c) a penalty for content that violates agents' beliefs reduces propaganda effectiveness up to 8%. We further discuss Digico as a tool for systematic experimentation across multi-agent configurations, the implications for legislation, personal use, and platform design, and the use of Digico for studying evolutionary principles.", "date": "2024-12-19", "categories": []}
{"id": "2509.16464", "title": "Computational Analysis of Conversation Dynamics through Participant Responsivity", "abstract": "Growing literature explores toxicity and polarization in discourse, with comparatively less work on characterizing what makes dialogue prosocial and constructive. We explore conversational discourse and investigate a method for characterizing its quality built upon the notion of ``responsivity'' -- whether one person's conversational turn is responding to a preceding turn. We develop and evaluate methods for quantifying responsivity -- first through semantic similarity of speaker turns, and second by leveraging state-of-the-art large language models (LLMs) to identify the relation between two speaker turns. We evaluate both methods against a ground truth set of human-annotated conversations. Furthermore, selecting the better performing LLM-based approach, we characterize the nature of the response -- whether it responded to that preceding turn in a substantive way or not.\n  We view these responsivity links as a fundamental aspect of dialogue but note that conversations can exhibit significantly different responsivity structures. Accordingly, we then develop conversation-level derived metrics to address various aspects of conversational discourse. We use these derived metrics to explore other conversations and show that they support meaningful characterizations and differentiations across a diverse collection of conversations.", "date": "2025-09-19", "categories": []}
{"id": "2510.05743", "title": "Artificially intelligent agents in the social and behavioral sciences: A history and outlook", "abstract": "We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.", "date": "2025-10-07", "categories": []}
{"id": "2510.27003", "title": "Are Online Sports Fan Communities Becoming More Offensive? A Quantitative Review of Topics, Trends, and Toxicity of r/PremierLeague", "abstract": "Online communities for sports fans have surged in popularity, with Reddit's r/PremierLeague emerging as a focal point for fans of one of the globe's most celebrated sports leagues. This boom has helped the Premier League make significant inroads into the US market, increasing viewership and sparking greater interest in its matches. Despite the league's broad appeal, there's still a notable gap in understanding its online fan community. Therefore, we analyzed a substantial dataset of over 1.1 million comments posted from 2013-2022 on r/PremierLeague. Our study delves into the sentiment, topics, and toxicity of these discussions, tracking trends over time, aiming to map out the conversation landscape. The rapid expansion has brought more diverse discussions, but also a worrying rise in negative sentiment and toxicity. Additionally, the subreddit has become a venue for users to voice frustrations about broader societal issues like racism, the COVID-19 pandemic, and political tensions.", "date": "2025-10-30", "categories": []}
{"id": "2507.19756", "title": "Are You There God? Lightweight Narrative Annotation of Christian Fiction with LMs", "abstract": "In addition to its more widely studied cultural movements, American Evangelicalism has a well-developed but less externally visible literary side. Christian Fiction, however, has been little studied, and what scholarly attention there is has focused on the explosively popular Left Behind series. In this work, we use computational tools to provide both a broad topical overview of Christian Fiction as a genre and a more directed exploration of how its authors depict divine acts. Working with human annotators, we first developed a codebook for identifying \"acts of God.\" We then adapted the codebook for use by a recent, lightweight LM with the assistance of a much larger model. The laptop-scale LM is largely capable of matching human annotations, even when the task is subtle and challenging. Using these annotations, we show that significant and meaningful differences exist between divine acts depicted by the Left Behind books and Christian Fiction more broadly.", "date": "2025-07-26", "categories": []}
{"id": "2510.26494", "title": "Simulating and Experimenting with Social Media Mobilization Using LLM Agents", "abstract": "Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Building on the landmark 61-million-person Facebook experiment \\citep{bond201261}, we develop an agent-based simulation framework that integrates real U.S. Census demographic distributions, authentic Twitter network topology, and heterogeneous large language model (LLM) agents to examine the effect of mobilization messages on voter turnout. Each simulated agent is assigned demographic attributes, a personal political stance, and an LLM variant (\\texttt{GPT-4.1}, \\texttt{GPT-4.1-Mini}, or \\texttt{GPT-4.1-Nano}) reflecting its political sophistication. Agents interact over realistic social network structures, receiving personalized feeds and dynamically updating their engagement behaviors and voting intentions. Experimental conditions replicate the informational and social mobilization treatments of the original Facebook study. Across scenarios, the simulator reproduces qualitative patterns observed in field experiments, including stronger mobilization effects under social message treatments and measurable peer spillovers. Our framework provides a controlled, reproducible environment for testing counterfactual designs and sensitivity analyses in political mobilization research, offering a bridge between high-validity field experiments and flexible computational modeling.\\footnote{Code and data available at https://github.com/CausalMP/LLM-SocioPol}", "date": "2025-10-30", "categories": []}
{"id": "2509.26080", "title": "Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research", "abstract": "Large Language Models (LLMs) are being increasingly used as synthetic agents in social science, in applications ranging from augmenting survey responses to powering multi-agent simulations. This paper outlines cautions that should be taken when interpreting LLM outputs and proposes a pragmatic reframing for the social sciences in which LLMs are used as high-capacity pattern matchers for quasi-predictive interpolation under explicit scope conditions and not as substitutes for probabilistic inference. Practical guardrails such as independent draws, preregistered human baselines, reliability-aware validation, and subgroup calibration, are introduced so that researchers may engage in useful prototyping and forecasting while avoiding category errors.", "date": "2025-09-30", "categories": []}
{"id": "2504.07830", "title": "MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations", "abstract": "We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content. This simulation combines LLM agents with a directed social graph to analyze emergent deception behaviors and gain a better understanding of how users determine the veracity of online social content. By constructing user representations from diverse fine-grained personas, our system enables multi-agent simulations that model content dissemination and engagement dynamics at scale. Within this framework, we evaluate three different content moderation strategies with simulated misinformation dissemination, and we find that they not only mitigate the spread of non-factual content but also increase user engagement. In addition, we analyze the trajectories of popular content in our simulations, and explore whether simulation agents' articulated reasoning for their social interactions truly aligns with their collective engagement patterns. We open-source our simulation software to encourage further research within AI and social sciences.", "date": "2025-04-10", "categories": []}
{"id": "2509.24877", "title": "The Emergence of Social Science of Large Language Models", "abstract": "The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform human activity and institutions. We conducted a systematic review of 270 studies, combining text embeddings, unsupervised clustering and topic modeling to build a computational taxonomy. Three domains emerge organically across the reviewed literature. LLM as Social Minds examines whether and when models display behaviors that elicit attributions of cognition, morality and bias, while addressing challenges such as test leakage and surface cues. LLM Societies examines multi-agent settings where interaction protocols, architectures and mechanism design shape coordination, norms, institutions and collective epistemic processes. LLM-Human Interactions examines how LLMs reshape tasks, learning, trust, work and governance, and how risks arise at the human-AI interface. This taxonomy provides a reproducible map of a fragmented field, clarifies evidentiary standards across levels of analysis, and highlights opportunities for cumulative progress in the social science of artificial intelligence.", "date": "2025-09-29", "categories": []}
{"id": "2510.22904", "title": "Modeling Political Discourse with Sentence-BERT and BERTopic", "abstract": "Social media has reshaped political discourse, offering politicians a platform for direct engagement while reinforcing polarization and ideological divides. This study introduces a novel topic evolution framework that integrates BERTopic-based topic modeling with Moral Foundations Theory (MFT) to analyze the longevity and moral dimensions of political topics in Twitter activity during the 117th U.S. Congress. We propose a methodology for tracking dynamic topic shifts over time and measuring their association with moral values and quantifying topic persistence. Our findings reveal that while overarching themes remain stable, granular topics tend to dissolve rapidly, limiting their long-term influence. Moreover, moral foundations play a critical role in topic longevity, with Care and Loyalty dominating durable topics, while partisan differences manifest in distinct moral framing strategies. This work contributes to the field of social network analysis and computational political discourse by offering a scalable, interpretable approach to understanding moral-driven topic evolution on social media.", "date": "2025-10-27", "categories": []}
{"id": "2506.06958", "title": "Simulating Society Requires Simulating Thought", "abstract": "Simulating society with large language models (LLMs), we argue, requires more than generating plausible behavior; it demands cognitively grounded reasoning that is structured, revisable, and traceable. LLM-based agents are increasingly used to emulate individual and group behavior, primarily through prompting and supervised fine-tuning. Yet current simulations remain grounded in a behaviorist \"demographics in, behavior out\" paradigm, focusing on surface-level plausibility. As a result, they often lack internal coherence, causal reasoning, and belief traceability, making them unreliable for modeling how people reason, deliberate, and respond to interventions.\n  To address this, we present a conceptual modeling paradigm, Generative Minds (GenMinds), which draws from cognitive science to support structured belief representations in generative agents. To evaluate such agents, we introduce the RECAP (REconstructing CAusal Paths) framework, a benchmark designed to assess reasoning fidelity via causal traceability, demographic grounding, and intervention consistency. These contributions advance a broader shift: from surface-level mimicry to generative agents that simulate thought, not just language, for social simulations.", "date": "2025-06-08", "categories": []}
{"id": "2510.21180", "title": "Social Simulations with Large Language Model Risk Utopian Illusion", "abstract": "Reliable simulation of human behavior is essential for explaining, predicting, and intervening in our society. Recent advances in large language models (LLMs) have shown promise in emulating human behaviors, interactions, and decision-making, offering a powerful new lens for social science studies. However, the extent to which LLMs diverge from authentic human behavior in social contexts remains underexplored, posing risks of misinterpretation in scientific studies and unintended consequences in real-world applications. Here, we introduce a systematic framework for analyzing LLMs' behavior in social simulation. Our approach simulates multi-agent interactions through chatroom-style conversations and analyzes them across five linguistic dimensions, providing a simple yet effective method to examine emergent social cognitive biases. We conduct extensive experiments involving eight representative LLMs across three families. Our findings reveal that LLMs do not faithfully reproduce genuine human behavior but instead reflect overly idealized versions of it, shaped by the social desirability bias. In particular, LLMs show social role bias, primacy effect, and positivity bias, resulting in \"Utopian\" societies that lack the complexity and variability of real human interactions. These findings call for more socially grounded LLMs that capture the diversity of human social behavior.", "date": "2025-10-24", "categories": []}
{"id": "2510.21220", "title": "The \"Right\" Discourse on Migration: Analysing Migration-Related Tweets in Right and Far-Right Political Movements", "abstract": "The rise of right-wing populism in Europe has brought to the forefront the significance of analysing social media discourse to understand the dissemination of extremist ideologies and their impact on political outcomes. Twitter, as a platform for interaction and mobilisation, provides a unique window into the everyday communication of far-right supporters. In this paper, we propose a methodology that uses state-of-the-art natural language processing techniques with sociological insights to analyse the MIGR-TWIT corpus of far-right tweets in English and French. We aim to uncover patterns of discourse surrounding migration, hate speech, and persuasion techniques employed by right and far-right actors. By integrating linguistic, sociological, and computational approaches, we seek to offer cross-disciplinary insights into societal dynamics and contribute to a better understanding of contemporary challenges posed by right-wing extremism on social media platforms.", "date": "2025-10-24", "categories": []}
{"id": "2510.19882", "title": "Quantifying Feature Importance for Online Content Moderation", "abstract": "Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear understanding of which user characteristics are associated with different behavioural responses, which is the goal of this work. We investigate the informativeness of 753 socio-behavioural, linguistic, relational, and psychological features, in predicting the behavioural changes of 16.8K users affected by a major moderation intervention on Reddit. To reach this goal, we frame the problem in terms of \"quantification\", a task well-suited to estimating shifts in aggregate user behaviour. We then apply a greedy feature selection strategy with the double goal of (i) identifying the features that are most predictive of changes in user activity, toxicity, and participation diversity, and (ii) estimating their importance. Our results allow identifying a small set of features that are consistently informative across all tasks, and determining that many others are either task-specific or of limited utility altogether. We also find that predictive performance varies according to the task, with changes in activity and toxicity being easier to estimate than changes in diversity. Overall, our results pave the way for the development of accurate systems that predict user reactions to moderation interventions. Furthermore, our findings highlight the complexity of post-moderation user behaviour, and indicate that effective moderation should be tailored not only to user traits but also to the specific objective of the intervention.", "date": "2025-10-22", "categories": []}
{"id": "2510.18582", "title": "Beyond the Explicit: A Bilingual Dataset for Dehumanization Detection in Social Media", "abstract": "Digital dehumanization, although a critical issue, remains largely overlooked within the field of computational linguistics and Natural Language Processing. The prevailing approach in current research concentrating primarily on a single aspect of dehumanization that identifies overtly negative statements as its core marker. This focus, while crucial for understanding harmful online communications, inadequately addresses the broader spectrum of dehumanization. Specifically, it overlooks the subtler forms of dehumanization that, despite not being overtly offensive, still perpetuate harmful biases against marginalized groups in online interactions. These subtler forms can insidiously reinforce negative stereotypes and biases without explicit offensiveness, making them harder to detect yet equally damaging. Recognizing this gap, we use different sampling methods to collect a theory-informed bilingual dataset from Twitter and Reddit. Using crowdworkers and experts to annotate 16,000 instances on a document- and span-level, we show that our dataset covers the different dimensions of dehumanization. This dataset serves as both a training resource for machine learning models and a benchmark for evaluating future dehumanization detection techniques. To demonstrate its effectiveness, we fine-tune ML models on this dataset, achieving performance that surpasses state-of-the-art models in zero and few-shot in-context settings.", "date": "2025-10-21", "categories": []}
{"id": "2501.01284", "title": "Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation", "abstract": "This study introduces a novel framework for analysing and mitigating media bias by tracing partisan stances to their linguistic roots in emotional language. We posit that partisan bias is not merely an abstract stance but materialises as quantifiable 'emotional fingerprints' within news texts. These fingerprints are systematically measured using the Valence-Arousal-Dominance (VAD) framework, allowing us to decode the affective strategies behind partisan framing. Our analysis of the Allsides dataset confirms this hypothesis, revealing distinct and statistically significant emotional fingerprints for left, centre, and right-leaning media. Based on this evidence-driven approach, we then propose a computational approach to mitigation through NeutraSum, a model designed to neutralise these identified emotional patterns. By explicitly targeting the VAD characteristics of biased language, NeutraSum generates summaries that are not only coherent but also demonstrably closer to an emotionally neutral baseline. Experimental results validate our framework: NeutraSum successfully erases the partisan emotional fingerprints from its summaries, achieving a demonstrably lower emotional bias score than other models. This work pioneers a new path for bias mitigation, shifting the focus from treating symptoms (political labels) to addressing the cause: the emotional encoding of partisan bias in language.", "date": "2025-01-02", "categories": []}
{"id": "2502.09004", "title": "Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech", "abstract": "This paper makes three contributions. First, via a substantial corpus of 1,419,047 comments posted on 3,161 YouTube news videos of major US cable news outlets, we analyze how users engage with LGBTQ+ news content. Our analyses focus both on positive and negative content. In particular, we construct a fine-grained hope speech classifier that detects positive (hope speech), negative, neutral, and irrelevant content. Second, in consultation with a public health expert specializing on LGBTQ+ health, we conduct an annotation study with a balanced and diverse political representation and release a dataset of 3,750 instances with fine-grained labels and detailed annotator demographic information. Finally, beyond providing a vital resource for the LGBTQ+ community, our annotation study and subsequent in-the-wild assessments reveal (1) strong association between rater political beliefs and how they rate content relevant to a marginalized community; (2) models trained on individual political beliefs exhibit considerable in-the-wild disagreement; and (3) zero-shot large language models (LLMs) align more with liberal raters.", "date": "2025-02-13", "categories": []}
{"id": "2506.21974", "title": "Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism", "abstract": "The ability of Large Language Models (LLMs) to mimic human behavior triggered a plethora of computational social science research, assuming that empirical studies of humans can be conducted with AI agents instead. Since there have been conflicting research findings on whether and when this hypothesis holds, there is a need to better understand the differences in their experimental designs. We focus on replicating the behavior of social network users with the use of LLMs for the analysis of communication on social networks. First, we provide a formal framework for the simulation of social networks, before focusing on the sub-task of imitating user communication. We empirically test different approaches to imitate user behavior on X in English and German. Our findings suggest that social simulations should be validated by their empirical realism measured in the setting in which the simulation components were fitted. With this paper, we argue for more rigor when applying generative-agent-based modeling for social simulation.", "date": "2025-06-27", "categories": []}
{"id": "2510.16140", "title": "The Cultural Mapping and Pattern Analysis (CMAP) Visualization Toolkit: Open Source Text Analysis for Qualitative and Computational Social Science", "abstract": "The CMAP (cultural mapping and pattern analysis) visualization toolkit introduced in this paper is an open-source suite for analyzing and visualizing text data - from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scaped data like message board posts or blogs. The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in qualitative and/or computational social science (CSS). Despite the existence of off-the-shelf commercial qualitative data analysis software, there is a dearth of highly scalable open source options that can work with large data sets, and allow advanced statistical and language modeling. The foundation of the toolkit is a pragmatic approach that aligns research tools with social science project goals- empirical explanation, theory-guided measurement, comparative design, or evidence-based recommendations- guided by the principle that research paradigm and questions should determine methods. Consequently, the CMAP visualization toolkit offers a range of possibilities through the adjustment of relatively small number of parameters, and allows integration with other python tools.", "date": "2025-10-17", "categories": []}
{"id": "2510.16359", "title": "Utilising Large Language Models for Generating Effective Counter Arguments to Anti-Vaccine Tweets", "abstract": "In an era where public health is increasingly influenced by information shared on social media, combatting vaccine skepticism and misinformation has become a critical societal goal. Misleading narratives around vaccination have spread widely, creating barriers to achieving high immunisation rates and undermining trust in health recommendations. While efforts to detect misinformation have made significant progress, the generation of real time counter-arguments tailored to debunk such claims remains an insufficiently explored area. In this work, we explore the capabilities of LLMs to generate sound counter-argument rebuttals to vaccine misinformation. Building on prior research in misinformation debunking, we experiment with various prompting strategies and fine-tuning approaches to optimise counter-argument generation. Additionally, we train classifiers to categorise anti-vaccine tweets into multi-labeled categories such as concerns about vaccine efficacy, side effects, and political influences allowing for more context aware rebuttals. Our evaluation, conducted through human judgment, LLM based assessments, and automatic metrics, reveals strong alignment across these methods. Our findings demonstrate that integrating label descriptions and structured fine-tuning enhances counter-argument effectiveness, offering a promising approach for mitigating vaccine misinformation at scale.", "date": "2025-10-18", "categories": []}
{"id": "2510.13195", "title": "Emotional Cognitive Modeling Framework with Desire-Driven Objective Optimization for LLM-empowered Agent in Social Simulation", "abstract": "The advent of large language models (LLMs) has enabled agents to represent virtual humans in societal simulations, facilitating diverse interactions within complex social systems. However, existing LLM-based agents exhibit severe limitations in affective cognition: They fail to simulate the bounded rationality essential for bridging virtual and real-world services; They lack empirically validated integration mechanisms embedding emotions within agent decision architectures. This paper constructs an emotional cognition framework incorporating desire generation and objective management, designed to achieve emotion alignment between LLM-based agents and humans, modeling the complete decision-making process of LLM-based agents, encompassing state evolution, desire generation, objective optimization, decision generation, and action execution. This study implements the proposed framework within our proprietary multi-agent interaction environment. Experimental results demonstrate that agents governed by our framework not only exhibit behaviors congruent with their emotional states but also, in comparative assessments against other agent types, demonstrate superior ecological validity and generate decision outcomes that significantly more closely approximate human behavioral patterns.", "date": "2025-10-15", "categories": []}
{"id": "2510.11746", "title": "Evolution of wartime discourse on Telegram: A comparative study of Ukrainian and Russian policymakers' communication before and after Russia's full-scale invasion of Ukraine", "abstract": "This study examines elite-driven political communication on Telegram during the ongoing Russo-Ukrainian war, the first large-scale European war in the social media era. Using a unique dataset of Telegram public posts from Ukrainian and Russian policymakers (2019-2024), we analyze changes in communication volume, thematic content, and actor engagement following Russia's 2022 full-scale invasion. Our findings show a sharp increase in Telegram activity after the invasion, particularly among ruling-party policymakers. Ukrainian policymakers initially focused on war-related topics, but this emphasis declined over time In contrast, Russian policymakers largely avoided war-related discussions, instead emphasizing unrelated topics, such as Western crises, to distract public attention. We also identify differences in communication strategies between large and small parties, as well as individual policymakers. Our findings shed light on how policymakers adapt to wartime communication challenges and offer critical insights into the dynamics of online political discourse during times of war.", "date": "2025-10-11", "categories": []}
{"id": "2409.17990", "title": "Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models", "abstract": "This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users, and extract longitudinal aggregates of emotions and attitudes with established questionnaires. We focus our analysis on the beginning of the COVID-19 pandemic that had a strong impact on public opinion and collective emotions. We validate our estimates against representative British survey data and find strong positive, significant correlations for several collective emotions. The obtained estimates are robust across multiple training seeds and prompt formulations, and in line with collective emotions extracted using a traditional classification model trained on labeled data. We demonstrate the flexibility of our method on questions of public opinion for which no pre-trained classifier is available. Our work extends the analysis of affect in LLMs to a longitudinal setting through Temporal Adapters. It enables flexible, new approaches towards the longitudinal analysis of social media data.", "date": "2024-09-26", "categories": []}
{"id": "2508.18208", "title": "On the Interplay between Musical Preferences and Personality through the Lens of Language", "abstract": "Music serves as a powerful reflection of individual identity, often aligning with deeper psychological traits. Prior research has established correlations between musical preferences and personality, while separate studies have demonstrated that personality is detectable through linguistic analysis. Our study bridges these two research domains by investigating whether individuals' musical preferences leave traces in their spontaneous language through the lens of the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism). Using a carefully curated dataset of over 500,000 text samples from nearly 5,000 authors with reliably identified musical preferences, we build advanced models to assess personality characteristics. Our results reveal significant personality differences across fans of five musical genres. We release resources for future research at the intersection of computational linguistics, music psychology and personality analysis.", "date": "2025-08-25", "categories": []}
{"id": "2510.09739", "title": "Machine learning methods fail to provide cohesive atheoretical construction of personality traits from semantic embeddings", "abstract": "The lexical hypothesis posits that personality traits are encoded in language and is foundational to models like the Big Five. We created a bottom-up personality model from a classic adjective list using machine learning and compared its descriptive utility against the Big Five by analyzing one million Reddit comments. The Big Five, particularly Agreeableness, Conscientiousness, and Neuroticism, provided a far more powerful and interpretable description of these online communities. In contrast, our machine-learning clusters provided no meaningful distinctions, failed to recover the Extraversion trait, and lacked the psychometric coherence of the Big Five. These results affirm the robustness of the Big Five and suggest personality's semantic structure is context-dependent. Our findings show that while machine learning can help check the ecological validity of established psychological theories, it may not be able to replace them.", "date": "2025-10-10", "categories": []}
{"id": "2510.10658", "title": "You're Not Gonna Believe This: A Computational Analysis of Factual Appeals and Sourcing in Partisan News", "abstract": "While media bias is widely studied, the epistemic strategies behind factual reporting remain computationally underexplored. This paper analyzes these strategies through a large-scale comparison of CNN and Fox News. To isolate reporting style from topic selection, we employ an article matching strategy to compare reports on the same events and apply the FactAppeal framework to a corpus of over 470K articles covering two highly politicized periods: the COVID-19 pandemic and the Israel-Hamas war. We find that CNN's reporting contains more factual statements and is more likely to ground them in external sources. The outlets also exhibit sharply divergent sourcing patterns: CNN builds credibility by citing Experts} and Expert Documents, constructing an appeal to formal authority, whereas Fox News favors News Reports and direct quotations. This work quantifies how partisan outlets use systematically different epistemic strategies to construct reality, adding a new dimension to the study of media bias.", "date": "2025-10-12", "categories": []}
{"id": "2510.11277", "title": "Towards Real-Time Fake News Detection under Evidence Scarcity", "abstract": "Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily on external evidence and therefore struggle to generalize under evidence scarcity. To address this issue, we propose Evaluation-Aware Selection of Experts (EASE), a novel framework for real-time fake news detection that dynamically adapts its decision-making process according to the assessed sufficiency of available evidence. EASE introduces a sequential evaluation mechanism comprising three independent perspectives: (1) Evidence-based evaluation, which assesses evidence and incorporates it into decision-making only when the evidence is sufficiently supportive; (2) Reasoning-based evaluation, which leverages the world knowledge of large language models (LLMs) and applies them only when their reliability is adequately established; and (3) Sentiment-based fallback, which integrates sentiment cues when neither evidence nor reasoning is reliable. To enhance the accuracy of evaluation processes, EASE employs instruction tuning with pseudo labels to guide each evaluator in justifying its perspective-specific knowledge through interpretable reasoning. Furthermore, the expert modules integrate the evaluators' justified assessments with the news content to enable evaluation-aware decision-making, thereby enhancing overall detection accuracy. Moreover, we introduce RealTimeNews-25, a new benchmark comprising recent news for evaluating model generalization on emerging news with limited evidence. Extensive experiments demonstrate that EASE not only achieves state-of-the-art performance across multiple benchmarks, but also significantly improves generalization to real-time news. The code and dataset are available: https://github.com/wgyhhhh/EASE.", "date": "2025-10-13", "categories": []}
{"id": "2505.16592", "title": "What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse", "abstract": "Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions often in alignment with the authors' opinions and stances. However, the interaction between stance and media frame remains largely unexplored. In this work, we apply an interdisciplinary approach to conceptualize and computationally explore this interaction with internet memes on climate change. We curate CLIMATEMEMES, the first dataset of climate-change memes annotated with both stance and media frames, inspired by research in communication science. CLIMATEMEMES includes 1,184 memes sourced from 47 subreddits, enabling analysis of frame prominence over time and communities, and sheds light on the framing preferences of different stance holders. We propose two meme understanding tasks: stance detection and media frame detection. We evaluate LLaVA-NeXT and Molmo in various setups, and report the corresponding results on their LLM backbone. Human captions consistently enhance performance. Synthetic captions and human-corrected OCR also help occasionally. Our findings highlight that VLMs perform well on stance, but struggle on frames, where LLMs outperform VLMs. Finally, we analyze VLMs' limitations in handling nuanced frames and stance expressions on climate change internet memes.", "date": "2025-05-22", "categories": []}
{"id": "2510.07821", "title": "From Keywords to Clusters: AI-Driven Analysis of YouTube Comments to Reveal Election Issue Salience in 2024", "abstract": "This paper aims to explore two competing data science methodologies to attempt answering the question, \"Which issues contributed most to voters' choice in the 2024 presidential election?\" The methodologies involve novel empirical evidence driven by artificial intelligence (AI) techniques. By using two distinct methods based on natural language processing and clustering analysis to mine over eight thousand user comments on election-related YouTube videos from one right leaning journal, Wall Street Journal, and one left leaning journal, New York Times, during pre-election week, we quantify the frequency of selected issue areas among user comments to infer which issues were most salient to potential voters in the seven days preceding the November 5th election. Empirically, we primarily demonstrate that immigration and democracy were the most frequently and consistently invoked issues in user comments on the analyzed YouTube videos, followed by the issue of identity politics, while inflation was significantly less frequently referenced. These results corroborate certain findings of post-election surveys but also refute the supposed importance of inflation as an election issue. This indicates that variations on opinion mining, with their analysis of raw user data online, can be more revealing than polling and surveys for analyzing election outcomes.", "date": "2025-10-09", "categories": []}
{"id": "2510.02360", "title": "Spiral of Silence in Large Language Model Agents", "abstract": "The Spiral of Silence (SoS) theory holds that individuals with minority views often refrain from speaking out for fear of social isolation, enabling majority positions to dominate public discourse. When the 'agents' are large language models (LLMs), however, the classical psychological explanation is not directly applicable, since SoS was developed for human societies. This raises a central question: can SoS-like dynamics nevertheless emerge from purely statistical language generation in LLM collectives? We propose an evaluation framework for examining SoS in LLM agents. Specifically, we consider four controlled conditions that systematically vary the availability of 'History' and 'Persona' signals. Opinion dynamics are assessed using trend tests such as Mann-Kendall and Spearman's rank, along with concentration measures including kurtosis and interquartile range. Experiments across open-source and closed-source models show that history and persona together produce strong majority dominance and replicate SoS patterns; history signals alone induce strong anchoring; and persona signals alone foster diverse but uncorrelated opinions, indicating that without historical anchoring, SoS dynamics cannot emerge. The work bridges computational sociology and responsible AI design, highlighting the need to monitor and mitigate emergent conformity in LLM-agent systems.", "date": "2025-09-28", "categories": []}
{"id": "2510.06264", "title": "A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Machine Learning and Statistical Modeling", "abstract": "The 2024 July Revolution in Bangladesh represents a landmark event in the study of civil resistance. This study investigates the central paradox of the success of this student-led civilian uprising: how state violence, intended to quell dissent, ultimately fueled the movement's victory. We employ a mixed-methods approach. First, we develop a qualitative narrative of the conflict's timeline to generate specific, testable hypotheses. Then, using a disaggregated, event-level dataset, we employ a multi-method quantitative analysis to dissect the complex relationship between repression and mobilisation. We provide a framework to analyse explosive modern uprisings like the July Revolution. Initial pooled regression models highlight the crucial role of protest momentum in sustaining the movement. To isolate causal effects, we specify a Two-Way Fixed Effects panel model, which provides robust evidence for a direct and statistically significant local suppression backfire effect. Our Vector Autoregression (VAR) analysis provides clear visual evidence of an immediate, nationwide mobilisation in response to increased lethal violence. We further demonstrate that this effect was non-linear. A structural break analysis reveals that the backfire dynamic was statistically insignificant in the conflict's early phase but was triggered by the catalytic moral shock of the first wave of lethal violence, and its visuals circulated around July 16th. A complementary machine learning analysis (XGBoost, out-of-sample R$^{2}$=0.65) corroborates this from a predictive standpoint, identifying \"excessive force against protesters\" as the single most dominant predictor of nationwide escalation. We conclude that the July Revolution was driven by a contingent, non-linear backfire, triggered by specific catalytic moral shocks and accelerated by the viral reaction to the visual spectacle of state brutality.", "date": "2025-10-05", "categories": []}
{"id": "2510.07226", "title": "Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit", "abstract": "Generative Artificial Intelligence is reshaping online communication by enabling large-scale production of Machine-Generated Text (MGT) at low cost. While its presence is rapidly growing across the Web, little is known about how MGT integrates into social media environments. In this paper, we present the first large-scale characterization of MGT on Reddit. Using a state-of-the-art statistical method for detection of MGT, we analyze over two years of activity (2022-2024) across 51 subreddits representative of Reddit's main community types such as information seeking, social support, and discussion. We study the concentration of MGT across communities and over time, and compared MGT to human-authored text in terms of social signals it expresses and engagement it receives. Our very conservative estimate of MGT prevalence indicates that synthetic text is marginally present on Reddit, but it can reach peaks of up to 9% in some communities in some months. MGT is unevenly distributed across communities, more prevalent in subreddits focused on technical knowledge and social support, and often concentrated in the activity of a small fraction of users. MGT also conveys distinct social signals of warmth and status giving typical of language of AI assistants. Despite these stylistic differences, MGT achieves engagement levels comparable than human-authored content and in a few cases even higher, suggesting that AI-generated text is becoming an organic component of online social discourse. This work offers the first perspective on the MGT footprint on Reddit, paving the way for new investigations involving platform governance, detection strategies, and community dynamics.", "date": "2025-10-08", "categories": []}
{"id": "2410.20140", "title": "MAD-Sherlock: Multi-Agent Debate for Visual Misinformation Detection", "abstract": "One of the most challenging forms of misinformation involves pairing images with misleading text to create false narratives. Existing AI-driven detection systems often require domain-specific finetuning, limiting generalizability, and offer little insight into their decisions, hindering trust and adoption. We introduce MAD-Sherlock, a multi-agent debate system for out-of-context misinformation detection. MAD-Sherlock frames detection as a multi-agent debate, reflecting the diverse and conflicting discourse found online. Multimodal agents collaborate to assess contextual consistency and retrieve external information to support cross-context reasoning. Our framework is domain- and time-agnostic, requiring no finetuning, yet achieves state-of-the-art accuracy with in-depth explanations. Evaluated on NewsCLIPpings, VERITE, and MMFakeBench, it outperforms prior methods by 2%, 3%, and 5%, respectively. Ablation and user studies show that the debate and resultant explanations significantly improve detection performance and improve trust for both experts and non-experts, positioning MAD-Sherlock as a robust tool for autonomous citizen intelligence.", "date": "2024-10-26", "categories": []}
{"id": "2501.05482", "title": "HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models", "abstract": "During the COVID-19 pandemic, community tensions intensified, contributing to discriminatory sentiments against various religious groups, including Hindu communities. Recent advances in language models have shown promise for social media analysis with potential for longitudinal studies of social media platforms, such as X (Twitter). We present a computational framework for analyzing anti-Hindu sentiment (Hinduphobia) during the COVID-19 period, introducing an abuse detection and sentiment analysis approach for longitudinal analysis on X. We curate and release a \"Hinduphobic COVID-19 XDataset\" containing 8,000 annotated and manually verified tweets. We then develop the Hinduphobic BERT (HP-BERT) model using this dataset and achieve 94.72\\% accuracy, outperforming baseline Transformer-based language models. The model incorporates multi-label sentiment analysis capabilities through additional fine-tuning. Our analysis encompasses approximately 27.4 million tweets from six countries, including Australia, Brazil, India, Indonesia, Japan, and the United Kingdom. Statistical analysis reveals moderate correlations (r = 0.312-0.428) between COVID-19 case increases and Hinduphobic content volume, highlighting how pandemic-related stress may contribute to discriminatory discourse. This study provides evidence of social media-based religious discrimination during a COVID-19 crisis.", "date": "2025-01-07", "categories": []}
{"id": "2509.10127", "title": "Population-Aligned Persona Generation for LLM-based Social Simulation", "abstract": "Recent advances in large language models (LLMs) have enabled human-like social simulations at unprecedented scale and fidelity, offering new opportunities for computational social science. A key challenge, however, is the construction of persona sets that authentically represent the diversity and distribution of real-world populations. Most existing LLM-based social simulation studies focus primarily on designing agentic frameworks and simulation environments, often overlooking the complexities of persona generation and the potential biases introduced by unrepresentative persona sets. In this paper, we propose a systematic framework for synthesizing high-quality, population-aligned persona sets for LLM-driven social simulation. Our approach begins by leveraging LLMs to generate narrative personas from long-term social media data, followed by rigorous quality assessment to filter out low-fidelity profiles. We then apply importance sampling to achieve global alignment with reference psychometric distributions, such as the Big Five personality traits. To address the needs of specific simulation contexts, we further introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations. Extensive experiments demonstrate that our method significantly reduces population-level bias and enables accurate, flexible social simulation for a wide range of research and policy applications.", "date": "2025-09-12", "categories": []}
{"id": "2510.03541", "title": "What is a protest anyway? Codebook conceptualization is still a first-order concern in LLM-era classification", "abstract": "Generative large language models (LLMs) are now used extensively for text classification in computational social science (CSS). In this work, focus on the steps before and after LLM prompting -- conceptualization of concepts to be classified and using LLM predictions in downstream statistical inference -- which we argue have been overlooked in much of LLM-era CSS. We claim LLMs can tempt analysts to skip the conceptualization step, creating conceptualization errors that bias downstream estimates. Using simulations, we show that this conceptualization-induced bias cannot be corrected for solely by increasing LLM accuracy or post-hoc bias correction methods. We conclude by reminding CSS analysts that conceptualization is still a first-order concern in the LLM-era and provide concrete advice on how to pursue low-cost, unbiased, low-variance downstream estimates.", "date": "2025-10-03", "categories": []}
{"id": "2510.02811", "title": "A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media", "abstract": "Personality refers to individual differences in behavior, thinking, and feeling. With the growing availability of digital footprints, especially from social media, automated methods for personality assessment have become increasingly important. Natural language processing (NLP) enables the analysis of unstructured text data to identify personality indicators. However, two main challenges remain central to this thesis: the scarcity of large, personality-labeled datasets and the disconnect between personality psychology and NLP, which restricts model validity and interpretability. To address these challenges, this thesis presents two datasets -- MBTI9k and PANDORA -- collected from Reddit, a platform known for user anonymity and diverse discussions. The PANDORA dataset contains 17 million comments from over 10,000 users and integrates the MBTI and Big Five personality models with demographic information, overcoming limitations in data size, quality, and label coverage. Experiments on these datasets show that demographic variables influence model validity. In response, the SIMPA (Statement-to-Item Matching Personality Assessment) framework was developed - a computational framework for interpretable personality assessment that matches user-generated statements with validated questionnaire items. By using machine learning and semantic similarity, SIMPA delivers personality assessments comparable to human evaluations while maintaining high interpretability and efficiency. Although focused on personality assessment, SIMPA's versatility extends beyond this domain. Its model-agnostic design, layered cue detection, and scalability make it suitable for various research and practical applications involving complex label taxonomies and variable cue associations with target concepts.", "date": "2025-10-03", "categories": []}
{"id": "2509.23340", "title": "CrediBench: Building Web-Scale Network Datasets for Information Integrity", "abstract": "Online misinformation poses an escalating threat, amplified by the Internet's open nature and increasingly capable LLMs that generate persuasive yet deceptive content. Existing misinformation detection methods typically focus on either textual content or network structure in isolation, failing to leverage the rich, dynamic interplay between website content and hyperlink relationships that characterizes real-world misinformation ecosystems. We introduce CrediBench: a large-scale data processing pipeline for constructing temporal web graphs that jointly model textual content and hyperlink structure for misinformation detection. Unlike prior work, our approach captures the dynamic evolution of general misinformation domains, including changes in both content and inter-site references over time. Our processed one-month snapshot extracted from the Common Crawl archive in December 2024 contains 45 million nodes and 1 billion edges, representing the largest web graph dataset made publicly available for misinformation research to date. From our experiments on this graph snapshot, we demonstrate the strength of both structural and webpage content signals for learning credibility scores, which measure source reliability. The pipeline and experimentation code are all available here, and the dataset is in this folder.", "date": "2025-09-27", "categories": []}
{"id": "2510.01451", "title": "Financial Stability Implications of Generative AI: Taming the Animal Spirits", "abstract": "This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in trading decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered trading advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias.", "date": "2025-10-01", "categories": []}
{"id": "2507.04612", "title": "Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments", "abstract": "When a news article describes immigration as an \"economic burden\" or a \"humanitarian crisis,\" it selectively emphasizes certain aspects of the issue. Although \\textit{framing} shapes how the public interprets such issues, audiences do not absorb frames passively but actively reorganize the presented information. While this relationship between source content and audience response is well-documented in the social sciences, NLP approaches often ignore it, detecting frames in articles and responses in isolation. We present the first computational framework for large-scale analysis of framing across source content (news articles) and audience responses (reader comments). Methodologically, we refine frame labels and develop a framework that reconstructs dominant frames in articles and comments from sentence-level predictions, and aligns articles with topically relevant comments. Applying our framework across eleven topics and two news outlets, we find that frame reuse in comments correlates highly across outlets, while topic-specific patterns vary. We release a frame classifier that performs well on both articles and comments, a dataset of article and comment sentences manually labeled for frames, and a large-scale dataset of articles and comments with predicted frame labels.", "date": "2025-07-07", "categories": []}
{"id": "2510.00021", "title": "IA aplicada al an\\'alisis del conflicto Ir\\'an-Israel: Mapeo de discursos en YouTube", "abstract": "Purpose. This study analyzes the digital representation of the Iran-Israel conflict that occurred in June 2025, based on 120,000 comments posted on YouTube. It sought to identify discursive positions regarding the actors involved and to examine how media and algorithmic biases shape digital conversations. Methodology. A mixed-methods design with triangulation was adopted. In the quantitative phase, natural language processing techniques and machine learning models (BERT and XLM-RoBERTa) were used to classify comments into ten categories. In the qualitative phase, a critical analysis of media context and ideological narratives was conducted, complemented by manual annotation and supervised training. This strategy enabled the integration of statistical robustness with contextual understanding. Results and conclusions. The findings reveal a clear overrepresentation of pro-Palestinian and anti-United States/Israel discourses, while pro-United States and anti-Palestinian positions were marginal. Iran, usually rendered invisible in global media, emerged as a central actor in the digital conversation during the conflict, suggesting a narrative shift away from previous hegemonic frameworks. Likewise, the results confirm the influence of algorithmic biases in amplifying certain discourses while limiting others. Original contributions. This work combines computational analysis and philosophical critique for the study of digital controversies, providing a methodological framework replicable in geopolitical contexts. It is one of the first Spanish-language studies to map, through artificial intelligence and critical analysis, discourses on an international conflict on YouTube, highlighting asymmetries and narrative disputes that are often overlooked.", "date": "2025-09-24", "categories": []}
{"id": "2503.15484", "title": "Value Profiles for Encoding Human Variation", "abstract": "Modelling human variation in rating tasks is crucial for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using natural language value profiles -- descriptions of underlying values compressed from in-context demonstrations -- along with a steerable decoder model that estimates individual ratings from a rater representation. To measure the predictive information in a rater representation, we introduce an information-theoretic methodology and find that demonstrations contain the most information, followed by value profiles, then demographics. However, value profiles effectively compress the useful information from demonstrations (>70% information preservation) and offer advantages in terms of scrutability, interpretability, and steerability. Furthermore, clustering value profiles to identify similarly behaving individuals better explains rater variation than the most predictive demographic groupings. Going beyond test set performance, we show that the decoder predictions change in line with semantic profile differences, are well-calibrated, and can help explain instance-level disagreement by simulating an annotator population. These results demonstrate that value profiles offer novel, predictive ways to describe individual variation beyond demographics or group information.", "date": "2025-03-19", "categories": []}
{"id": "2509.25244", "title": "Neo-Grounded Theory: A Methodological Innovation Integrating High-Dimensional Vector Clustering and Multi-Agent Collaboration for Qualitative Research", "abstract": "Purpose: Neo Grounded Theory (NGT) integrates vector clustering with multi agent systems to resolve qualitative research's scale depth paradox, enabling analysis of massive datasets in hours while preserving interpretive rigor. Methods: We compared NGT against manual coding and ChatGPT-assisted analysis using 40,000 character Chinese interview transcripts. NGT employs 1536-dimensional embeddings, hierarchical clustering, and parallel agent-based coding. Two experiments tested pure automation versus human guided refinement. Findings: NGT achieved 168-fold speed improvement (3 hours vs 3 weeks), superior quality (0.904 vs 0.883), and 96% cost reduction. Human AI collaboration proved essential: automation alone produced abstract frameworks while human guidance yielded actionable dual pathway theories. The system discovered patterns invisible to manual coding, including identity bifurcation phenomena. Contributions: NGT demonstrates computational objectivity and human interpretation are complementary. Vector representations provide reproducible semantic measurement while preserving meaning's interpretive dimensions. Researchers shift from mechanical coding to theoretical guidance, with AI handling pattern recognition while humans provide creative insight. Implications: Cost reduction from \\$50,000 to \\$500 democratizes qualitative research, enabling communities to study themselves. Real-time analysis makes qualitative insights contemporaneous with events. The framework shows computational methods can strengthen rather than compromise qualitative research's humanistic commitments.\n  Keywords: Grounded theory; Vector embeddings; Multi agent systems; Human AI collaboration; Computational qualitative analysis", "date": "2025-09-26", "categories": []}
{"id": "2509.25649", "title": "The Media Bias Detector: A Framework for Annotating and Analyzing the News at Scale", "abstract": "Mainstream news organizations shape public perception not only directly through the articles they publish but also through the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. However, measuring these subtle forms of media bias at scale remains a challenge. Here, we introduce a large, ongoing (from January 1, 2024 to present), near real-time dataset and computational framework developed to enable systematic study of selection and framing bias in news coverage. Our pipeline integrates large language models (LLMs) with scalable, near-real-time news scraping to extract structured annotations -- including political lean, tone, topics, article type, and major events -- across hundreds of articles per day. We quantify these dimensions of coverage at multiple levels -- the sentence level, the article level, and the publisher level -- expanding the ways in which researchers can analyze media bias in the modern news landscape. In addition to a curated dataset, we also release an interactive web platform for convenient exploration of these data. Together, these contributions establish a reusable methodology for studying media bias at scale, providing empirical resources for future research. Leveraging the breadth of the corpus over time and across publishers, we also present some examples (focused on the 150,000+ articles examined in 2024) that illustrate how this novel data set can reveal insightful patterns in news coverage and bias, supporting academic research and real-world efforts to improve media accountability.", "date": "2025-09-30", "categories": []}
{"id": "2404.15067", "title": "Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives", "abstract": "Textual personality detection aims to identify personality characteristics by analyzing user-generated content on social media platforms. Extensive psychological literature highlights that personality encompasses both long-term stable traits and short-term dynamic states. However, existing studies often concentrate only on either long-term or short-term personality representations, neglecting the integration of both aspects. This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital. To bridge this gap, we propose a Dual Enhanced Network (DEN) to jointly model users' long-term and short-term personality traits. In DEN, the Long-term Personality Encoding module models stable long-term personality traits by analyzing consistent patterns in the usage of psychological entities. The Short-term Personality Encoding module captures dynamic short-term personality states by modeling the contextual information of individual posts in real-time. The Bi-directional Interaction module integrates both aspects of personality, creating a cohesive and comprehensive representation of the user's personality. Experimental results on two personality detection datasets demonstrate the effectiveness of the DEN model and underscore the importance of considering both stable and dynamic aspects of personality in textual personality detection.", "date": "2024-04-23", "categories": []}
{"id": "2509.23270", "title": "Socio-Economic Model of AI Agents", "abstract": "Modern socio-economic systems are undergoing deep integration with artificial intelligence technologies. This paper constructs a heterogeneous agent-based modeling framework that incorporates both human workers and autonomous AI agents, to study the impact of AI collaboration under resource constraints on aggregate social output. We build five progressively extended models: Model 1 serves as the baseline of pure human collaboration; Model 2 introduces AI as collaborators; Model 3 incorporates network effects among agents; Model 4 treats agents as independent producers; and Model 5 integrates both network effects and independent agent production. Through theoretical derivation and simulation analysis, we find that the introduction of AI agents can significantly increase aggregate social output. When considering network effects among agents, this increase exhibits nonlinear growth far exceeding the simple sum of individual contributions. Under the same resource inputs, treating agents as independent producers provides higher long-term growth potential; introducing network effects further demonstrates strong characteristics of increasing returns to scale.", "date": "2025-09-27", "categories": []}
{"id": "2503.07690", "title": "Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide", "abstract": "Digital deliberation has expanded democratic participation, yet challenges remain. This includes processing information at scale, moderating discussions, fact-checking, or attracting people to participate. Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored. Beyond efficiency, democratic deliberation is about voice and recognition. If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected. We conducted a preregistered survey experiment with a representative sample in Germany (n=1850) to examine how information about AI-enabled deliberation influences willingness to participate and perceptions of deliberative quality. Respondents were randomly assigned to treatments that provided them information about deliberative tasks facilitated by either AI or humans. Our findings reveal a significant AI-penalty. Participants were less willing to engage in AI-facilitated deliberation and rated its quality lower than human-led formats. These effects were moderated by individual predispositions. Perceptions of AI's societal benefits and anthropomorphization of AI showed positive interaction effects on people's interest to participate in AI-enabled deliberative formats and positive quality assessments, while AI risk assessments showed negative interactions with information about AI-enabled deliberation. These results suggest AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide. Unlike traditional participation gaps based on education or demographics, this divide is shaped by attitudes toward AI. As democratic engagement increasingly moves online, ensuring AI's role in deliberation does not discourage participation or deepen inequalities will be a key challenge for future research and policy.", "date": "2025-03-10", "categories": []}
{"id": "2509.22537", "title": "The Emergence of Altruism in Large-Language-Model Agents Society", "abstract": "Leveraging Large Language Models (LLMs) for social simulation is a frontier in computational social science. Understanding the social logics these agents embody is critical to this attempt. However, existing research has primarily focused on cooperation in small-scale, task-oriented games, overlooking how altruism, which means sacrificing self-interest for collective benefit, emerges in large-scale agent societies. To address this gap, we introduce a Schelling-variant urban migration model that creates a social dilemma, compelling over 200 LLM agents to navigate an explicit conflict between egoistic (personal utility) and altruistic (system utility) goals. Our central finding is a fundamental difference in the social tendencies of LLMs. We identify two distinct archetypes: \"Adaptive Egoists\", which default to prioritizing self-interest but whose altruistic behaviors significantly increase under the influence of a social norm-setting message board; and \"Altruistic Optimizers\", which exhibit an inherent altruistic logic, consistently prioritizing collective benefit even at a direct cost to themselves. Furthermore, to qualitatively analyze the cognitive underpinnings of these decisions, we introduce a method inspired by Grounded Theory to systematically code agent reasoning. In summary, this research provides the first evidence of intrinsic heterogeneity in the egoistic and altruistic tendencies of different LLMs. We propose that for social simulation, model selection is not merely a matter of choosing reasoning capability, but of choosing an intrinsic social action logic. While \"Adaptive Egoists\" may offer a more suitable choice for simulating complex human societies, \"Altruistic Optimizers\" are better suited for modeling idealized pro-social actors or scenarios where collective welfare is the primary consideration.", "date": "2025-09-26", "categories": []}
{"id": "2410.07302", "title": "Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits", "abstract": "Broadly accessible generative AI models like Dall-E have made it possible for anyone to create compelling visual art. In online communities, the introduction of AI-generated content (AIGC) may impact social dynamics, for example causing changes in who is posting content, or shifting the norms or the discussions around the posted content if posts are suspected of being generated by AI. We take steps towards examining the potential impact of AIGC on art-related communities on Reddit. We distinguish between communities that disallow AI content and those without such a direct policy. We look at image-based posts in these communities where the author transparently shares that the image was created by AI, and at comments in these communities that suspect or accuse authors of using generative AI. We find that AI posts (and accusations) have played a surprisingly small part in these communities through the end of 2023, accounting for fewer than 0.5% of the image-based posts. However, even as the absolute number of author-labeled AI posts dwindles over time, accusations of AI use remain more persistent. We show that AI content is more readily used by newcomers and may help increase participation if it aligns with community rules. However, the tone of comments suspecting AI use by others has become more negative over time, especially in communities that do not have explicit rules about AI. Overall, the results show the changing norms and interactions around AIGC in online communities designated for creativity.", "date": "2024-10-09", "categories": []}
{"id": "2509.20419", "title": "Wartime Media Dynamics in Emerging Democracies: Case Study of Pakistani Media in May 2025 Indo-Pak Conflict", "abstract": "Democracies rely on opposition and dissent to function, but in emerging democracies, freedom of speech is often restricted. This effect intensifies during regional conflicts. This study examines how the India-Pakistan conflict of May 2025 influenced Pakistani media coverage. Analyzing approximately 2,600 news articles from three major newspapers using a large language model (LLM), the study found that war-related reporting significantly overshadowed coverage of political opposition and dissent. These findings highlight how conflict can marginalize democratic discourse, reinforcing the need to safeguard press freedom in volatile regions.", "date": "2025-09-24", "categories": []}
{"id": "2509.20724", "title": "Visual Authority and the Rhetoric of Health Misinformation: A Multimodal Analysis of Social Media Videos", "abstract": "Short form video platforms are central sites for health advice, where alternative narratives mix useful, misleading, and harmful content. Rather than adjudicating truth, this study examines how credibility is packaged in nutrition and supplement videos by analyzing the intersection of authority signals, narrative techniques, and monetization. We assemble a cross platform corpus of 152 public videos from TikTok, Instagram, and YouTube and annotate each on 26 features spanning visual authority, presenter attributes, narrative strategies, and engagement cues. A transparent annotation pipeline integrates automatic speech recognition, principled frame selection, and a multimodal model, with human verification on a stratified subsample showing strong agreement. Descriptively, a confident single presenter in studio or home settings dominates, and clinical contexts are rare. Analytically, authority cues such as titles, slides and charts, and certificates frequently occur with persuasive elements including jargon, references, fear or urgency, critiques of mainstream medicine, and conspiracies, and with monetization including sales links and calls to subscribe. References and science like visuals often travel with emotive and oppositional narratives rather than signaling restraint.", "date": "2025-09-25", "categories": []}
{"id": "2509.16893", "title": "DRES: Fake news detection by dynamic representation and ensemble selection", "abstract": "The rapid spread of information via social media has made text-based fake news detection critically important due to its societal impact. This paper presents a novel detection method called Dynamic Representation and Ensemble Selection (DRES) for identifying fake news based solely on text. DRES leverages instance hardness measures to estimate the classification difficulty for each news article across multiple textual feature representations. By dynamically selecting the textual representation and the most competent ensemble of classifiers for each instance, DRES significantly enhances prediction accuracy. Extensive experiments show that DRES achieves notable improvements over state-of-the-art methods, confirming the effectiveness of representation selection based on instance hardness and dynamic ensemble selection in boosting performance. Codes and data are available at: https://github.com/FFarhangian/FakeNewsDetection_DRES", "date": "2025-09-21", "categories": []}
{"id": "2509.19497", "title": "Generative AI as a catalyst for democratic Innovation: Enhancing citizen engagement in participatory budgeting", "abstract": "This research examines the role of Generative Artificial Intelligence (AI) in enhancing citizen engagement in participatory budgeting. In response to challenges like declining civic participation and increased societal polarization, the study explores how online political participation can strengthen democracy and promote social equity. By integrating Generative AI into public consultation platforms, the research aims to improve citizen proposal formulation and foster effective dialogue between citizens and government. It assesses the capacities governments need to implement AI-enhanced participatory tools, considering technological dependencies and vulnerabilities. Analyzing technological structures, actors, interests, and strategies, the study contributes to understanding how technological advancements can reshape participatory institutions to better facilitate citizen involvement. Ultimately, the research highlights how Generative AI can transform participatory institutions, promoting inclusive, democratic engagement and empowering citizens.", "date": "2025-09-23", "categories": []}
{"id": "2410.05401", "title": "Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation", "abstract": "Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups. This study presents a post-hoc analysis of microtargeting practices within climate campaigns by leveraging large language models (LLMs) to examine Meta (previously known as Facebook) advertisements. Our analysis focuses on two key aspects: demographic targeting and fairness. We evaluate the ability of LLMs to accurately predict the intended demographic targets, such as gender and age group. Furthermore, we instruct the LLMs to generate explanations for their classifications, providing transparent reasoning behind each decision. These explanations reveal the specific thematic elements used to engage different demographic segments, highlighting distinct strategies tailored to various audiences. Our findings show that young adults are primarily targeted through messages emphasizing activism and environmental consciousness, while women are engaged through themes related to caregiving roles and social advocacy. Additionally, we conduct a comprehensive fairness analysis to uncover biases in model predictions. We assess disparities in accuracy and error rates across demographic groups using established fairness metrics such as Demographic Parity, Equal Opportunity, and Predictive Equality. Our findings indicate that while LLMs perform well overall, certain biases exist, particularly in the classification of male audiences. The analysis of thematic explanations uncovers recurring patterns in messaging strategies tailored to various demographic groups, while the fairness analysis underscores the need for more inclusive targeting methods. This study provides a valuable framework for future research aimed at enhancing transparency, accountability, and inclusivity in social media-driven climate campaigns.", "date": "2024-10-07", "categories": []}
{"id": "2507.11049", "title": "Journalism-Guided Agentic In-Context Learning for News Stance Detection", "abstract": "As online news consumption grows, personalized recommendation systems have become integral to digital journalism. However, these systems risk reinforcing filter bubbles and political polarization by failing to incorporate diverse perspectives. Stance detection -- identifying a text's position on a target -- can help mitigate this by enabling viewpoint-aware recommendations and data-driven analyses of media bias. Yet, existing stance detection research remains largely limited to short texts and high-resource languages. To address these gaps, we introduce \\textsc{K-News-Stance}, the first Korean dataset for article-level stance detection, comprising 2,000 news articles with article-level and 21,650 segment-level stance annotations across 47 societal issues. We also propose \\textsc{JoA-ICL}, a \\textbf{Jo}urnalism-guided \\textbf{A}gentic \\textbf{I}n-\\textbf{C}ontext \\textbf{L}earning framework that employs a language model agent to predict the stances of key structural segments (e.g., leads, quotations), which are then aggregated to infer the overall article stance. Experiments showed that \\textsc{JoA-ICL} outperforms existing stance detection methods, highlighting the benefits of segment-level agency in capturing the overall position of long-form news articles. Two case studies further demonstrate its broader utility in promoting viewpoint diversity in news recommendations and uncovering patterns of media bias.", "date": "2025-07-15", "categories": []}
{"id": "2509.15793", "title": "RAVE: Retrieval and Scoring Aware Verifiable Claim Detection", "abstract": "The rapid spread of misinformation on social media underscores the need for scalable fact-checking tools. A key step is claim detection, which identifies statements that can be objectively verified. Prior approaches often rely on linguistic cues or claim check-worthiness, but these struggle with vague political discourse and diverse formats such as tweets. We present RAVE (Retrieval and Scoring Aware Verifiable Claim Detection), a framework that combines evidence retrieval with structured signals of relevance and source credibility. Experiments on CT22-test and PoliClaim-test show that RAVE consistently outperforms text-only and retrieval-based baselines in both accuracy and F1.", "date": "2025-09-19", "categories": []}
{"id": "2509.15896", "title": "The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection", "abstract": "Misinformation remains one of the most significant issues in the digital age. While automated fact-checking has emerged as a viable solution, most current systems are limited to evaluating factual accuracy. However, the detrimental effect of misinformation transcends simple falsehoods; it takes advantage of how individuals perceive, interpret, and emotionally react to information. This underscores the need to move beyond factuality and adopt more human-centered detection frameworks. In this survey, we explore the evolving interplay between traditional fact-checking approaches and psychological concepts such as cognitive biases, social dynamics, and emotional responses. By analyzing state-of-the-art misinformation detection systems through the lens of human psychology and behavior, we reveal critical limitations of current methods and identify opportunities for improvement. Additionally, we outline future research directions aimed at creating more robust and adaptive frameworks, such as neuro-behavioural models that integrate technological factors with the complexities of human cognition and social influence. These approaches offer promising pathways to more effectively detect and mitigate the societal harms of misinformation.", "date": "2025-09-19", "categories": []}
{"id": "2506.00308", "title": "MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform", "abstract": "Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains a challenge, particularly for high-stakes but understudied topics like opioid-use disorder (OUD)--a leading cause of death in the U.S. We present the first large-scale study of OUD-related myths on YouTube, a widely-used platform for health information. With clinical experts, we validate 8 pervasive myths and release an expert-labeled video dataset. To scale labeling, we introduce MythTriage, an efficient triage pipeline that uses a lightweight model for routine cases and defers harder ones to a high-performing, but costlier, large language model (LLM). MythTriage achieves up to 0.86 macro F1-score while estimated to reduce annotation time and financial cost by over 76% compared to experts and full LLM labeling. We analyze 2.9K search results and 343K recommendations, uncovering how myths persist on YouTube and offering actionable insights for public health and platform moderation.", "date": "2025-05-30", "categories": []}
{"id": "2509.12577", "title": "An AI-Powered Framework for Analyzing Collective Idea Evolution in Deliberative Assemblies", "abstract": "In an era of increasing societal fragmentation, political polarization, and erosion of public trust in institutions, representative deliberative assemblies are emerging as a promising democratic forum for developing effective policy outcomes on complex global issues. Despite theoretical attention, there remains limited empirical work that systematically traces how specific ideas evolve, are prioritized, or are discarded during deliberation to form policy recommendations. Addressing these gaps, this work poses two central questions: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To address these questions, we develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced in-person deliberative assembly. The framework identifies and visualizes the space of expressed suggestions. We also empirically reconstruct each delegate's evolving perspective throughout the assembly. Our methods contribute novel empirical insights into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs.", "date": "2025-09-16", "categories": []}
{"id": "2509.14197", "title": "Framing Migration: A Computational Analysis of UK Parliamentary Discourse", "abstract": "We present a large-scale computational analysis of migration-related discourse in UK parliamentary debates spanning over 75 years and compare it with US congressional discourse. Using open-weight LLMs, we annotate each statement with high-level stances toward migrants and track the net tone toward migrants across time and political parties. For the UK, we extend this with a semi-automated framework for extracting fine-grained narrative frames to capture nuances of migration discourse. Our findings show that, while US discourse has grown increasingly polarised, UK parliamentary attitudes remain relatively aligned across parties, with a persistent ideological gap between Labour and the Conservatives, reaching its most negative level in 2025. The analysis of narrative frames in the UK parliamentary statements reveals a shift toward securitised narratives such as border control and illegal immigration, while longer-term integration-oriented frames such as social integration have declined. Moreover, discussions of national law about immigration have been replaced over time by international law and human rights, revealing nuances in discourse trends. Taken together broadly, our findings demonstrate how LLMs can support scalable, fine-grained discourse analysis in political and historical contexts.", "date": "2025-09-17", "categories": []}
{"id": "2509.13197", "title": "Podcasts as a Medium for Participation in Collective Action: A Case Study of Black Lives Matter", "abstract": "We study how participation in collective action is articulated in podcast discussions, using the Black Lives Matter (BLM) movement as a case study. While research on collective action discourse has primarily focused on text-based content, this study takes a first step toward analyzing audio formats by using podcast transcripts. Using the Structured Podcast Research Corpus (SPoRC), we investigated spoken language expressions of participation in collective action, categorized as problem-solution, call-to-action, intention, and execution. We identified podcast episodes discussing racial justice after important BLM-related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on social media. We examined the emotional dimensions of these statements, detecting eight key emotions and their association with varying stages of activism. We found that emotional profiles vary by stage, with different positive emotions standing out during calls-to-action, intention, and execution. We detected negative associations between collective action and negative emotions, contrary to theoretical expectations. Our work contributes to a better understanding of how activism is expressed in spoken digital discourse and how emotional framing may depend on the format of the discussion.", "date": "2025-09-16", "categories": []}
{"id": "2407.11862", "title": "LML: A Novel Lexicon for the Moral Foundation of Liberty", "abstract": "The moral value of liberty is a central concept in our inference system when it comes to taking a stance towards controversial social issues such as vaccine hesitancy, climate change, or the right to abortion. Here, we propose a novel Liberty lexicon evaluated on more than 3,000 manually annotated data both in in- and out-of-domain scenarios. As a result of this evaluation, we produce a combined lexicon that constitutes the main outcome of this work. This final lexicon incorporates information from an ensemble of lexicons that have been generated using word embedding similarity (WE) and compositional semantics (CS). Our key contributions include enriching the liberty annotations, developing a robust liberty lexicon for broader application, and revealing the complexity of expressions related to liberty across different platforms. Through the evaluation, we show that the difficulty of the task calls for designing approaches that combine knowledge, in an effort of improving the representations of learning systems.", "date": "2024-07-16", "categories": []}
{"id": "2506.21587", "title": "A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies", "abstract": "This study evaluates the ability of DeepSeek, an open-source large language model (LLM), to simulate public opinions in comparison to LLMs developed by major tech companies. By comparing DeepSeek-R1 and DeepSeek-V3 with Qwen2.5, GPT-4o, and Llama-3.3 and utilizing survey data from the American National Election Studies (ANES) and the Zuobiao dataset of China, we assess these models' capacity to predict public opinions on social issues in both China and the United States, highlighting their comparative capabilities between countries. Our findings indicate that DeepSeek-V3 performs best in simulating U.S. opinions on the abortion issue compared to other topics such as climate change, gun control, immigration, and services for same-sex couples, primarily because it more accurately simulates responses when provided with Democratic or liberal personas. For Chinese samples, DeepSeek-V3 performs best in simulating opinions on foreign aid and individualism but shows limitations in modeling views on capitalism, particularly failing to capture the stances of low-income and non-college-educated individuals. It does not exhibit significant differences from other models in simulating opinions on traditionalism and the free market. Further analysis reveals that all LLMs exhibit the tendency to overgeneralize a single perspective within demographic groups, often defaulting to consistent responses within groups. These findings highlight the need to mitigate cultural and demographic biases in LLM-driven public opinion modeling, calling for approaches such as more inclusive training methodologies.", "date": "2025-06-17", "categories": []}
{"id": "2509.11687", "title": "A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection", "abstract": "As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of retrieved content and interference from noisy information. We propose a dynamic knowledge update-driven model for fake news detection (DYNAMO), which leverages knowledge graphs to achieve continuous updating of new knowledge and integrates with large language models to fulfill dual functions: news authenticity detection and verification of new knowledge correctness, solving the two key problems of ensuring the authenticity of new knowledge and deeply mining news semantics. Specifically, we first construct a news-domain-specific knowledge graph. Then, we use Monte Carlo Tree Search to decompose complex news and verify them step by step. Finally, we extract and update new knowledge from verified real news texts and reasoning paths. Experimental results demonstrate that DYNAMO achieves the best performance on two real-world datasets.", "date": "2025-09-15", "categories": []}
{"id": "2506.11095", "title": "Persistent Homology of Topic Networks for the Prediction of Reader Curiosity", "abstract": "Reader curiosity, the drive to seek information, is crucial for textual engagement, yet remains relatively underexplored in NLP. Building on Loewenstein's Information Gap Theory, we introduce a framework that models reader curiosity by quantifying semantic information gaps within a text's semantic structure. Our approach leverages BERTopic-inspired topic modeling and persistent homology to analyze the evolving topology (connected components, cycles, voids) of a dynamic semantic network derived from text segments, treating these features as proxies for information gaps. To empirically evaluate this pipeline, we collect reader curiosity ratings from participants (n = 49) as they read S. Collins's ''The Hunger Games'' novel. We then use the topological features from our pipeline as independent variables to predict these ratings, and experimentally show that they significantly improve curiosity prediction compared to a baseline model (73% vs. 30% explained deviance), validating our approach. This pipeline offers a new computational method for analyzing text structure and its relation to reader engagement.", "date": "2025-06-06", "categories": []}
{"id": "2504.08776", "title": "SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics", "abstract": "With the shift from traditional to digital media, the online landscape now hosts not only reliable news articles but also a significant amount of unreliable content. Digital media has faster reachability by significantly influencing public opinion and advancing political agendas. While newspaper readers may be familiar with their preferred outlets political leanings or credibility, determining unreliable news articles is much more challenging. The credibility of many online sources is often opaque, with AI generated content being easily disseminated at minimal cost. Unreliable news articles, particularly those that followed the Russian invasion of Ukraine in 2022, closely mimic the topics and writing styles of credible sources, making them difficult to distinguish. To address this, we introduce SemCAFE, a system designed to detect news reliability by incorporating entity relatedness into its assessment. SemCAFE employs standard Natural Language Processing techniques, such as boilerplate removal and tokenization, alongside entity level semantic analysis using the YAGO knowledge base. By creating a semantic fingerprint for each news article, SemCAFE could assess the credibility of 46,020 reliable and 3,407 unreliable articles on the 2022 Russian invasion of Ukraine. Our approach improved the macro F1 score by 12% over state of the art methods. The sample data and code are available on GitHub", "date": "2025-04-03", "categories": []}
{"id": "2509.03832", "title": "Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model", "abstract": "Social media echo chambers play a central role in the spread of misinformation, yet existing models often overlook the influence of individual confirmation bias. An existing model of echo chambers is the \"gravity well\" model, which creates an analog between echo chambers and spatial gravity wells. We extend this established model by introducing a dynamic confirmation bias variable that adjusts the strength of pull based on a user's susceptibility to belief-reinforcing content. This variable is calculated for each user through comparisons between their posting history and their responses to posts of a wide range of viewpoints.\n  Incorporating this factor produces a confirmation-bias-integrated gravity well model that more accurately identifies echo chambers and reveals community-level markers of information health. We validated the approach on nineteen Reddit communities, demonstrating improved detection of echo chambers.\n  Our contribution is a framework for systematically capturing the role of confirmation bias in online group dynamics, enabling more effective identification of echo chambers. By flagging these high-risk environments, the model supports efforts to curb the spread of misinformation at its most common points of amplification.", "date": "2025-09-04", "categories": []}
{"id": "2509.05566", "title": "Ad hoc conventions generalize to new referents", "abstract": "How do people talk about things they've never talked about before? One view suggests that a new shared naming system establishes an arbitrary link to a specific target, like proper names that cannot extend beyond their bearers. An alternative view proposes that forming a shared way of describing objects involves broader conceptual alignment, reshaping each individual's semantic space in ways that should generalize to new referents. We test these competing accounts in a dyadic communication study (N=302) leveraging the recently-released KiloGram dataset containing over 1,000 abstract tangram images. After pairs of participants coordinated on referential conventions for one set of images through repeated communication, we measured the extent to which their descriptions aligned for undiscussed images. We found strong evidence for generalization: partners showed increased alignment relative to their pre-test labels. Generalization also decayed nonlinearly with visual similarity (consistent with Shepard's law) and was robust across levels of the images' nameability. These findings suggest that ad hoc conventions are not arbitrary labels but reflect genuine conceptual coordination, with implications for theories of reference and the design of more adaptive language agents.", "date": "2025-09-06", "categories": []}
{"id": "2509.04823", "title": "Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media", "abstract": "Digital social media platforms frequently contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. While cognitive-behavioral fixation has been extensively studied in psychology, methods for computationally detecting and evaluating such fixation remain underexplored. To address this gap, we propose a novel framework for assessing cognitive-behavioral fixation by analyzing users' multimodal social media engagement patterns. Specifically, we introduce a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior. Experiments on existing benchmarks and a newly curated multimodal dataset demonstrate the effectiveness of our approach, laying the groundwork for scalable computational analysis of cognitive fixation. All code in this project is publicly available for research purposes at https://github.com/Liskie/cognitive-fixation-evaluation.", "date": "2025-09-05", "categories": []}
{"id": "2503.24220", "title": "BAR-Analytics: A Web-based Platform for Analyzing Information Spreading Barriers in News: Comparative Analysis Across Multiple Barriers and Events", "abstract": "This paper presents BAR-Analytics, a web-based, open-source platform designed to analyze news dissemination across geographical, economic, political, and cultural boundaries. Using the Russian-Ukrainian and Israeli-Palestinian conflicts as case studies, the platform integrates four analytical methods: propagation analysis, trend analysis, sentiment analysis, and temporal topic modeling. Over 350,000 articles were collected and analyzed, with a focus on economic disparities and geographical influences using metadata enrichment. We evaluate the case studies using coherence, sentiment polarity, topic frequency, and trend shifts as key metrics. Our results show distinct patterns in news coverage: the Israeli-Palestinian conflict tends to have more negative sentiment with a focus on human rights, while the Russia-Ukraine conflict is more positive, emphasizing election interference. These findings highlight the influence of political, economic, and regional factors in shaping media narratives across different conflicts.", "date": "2025-03-31", "categories": []}
{"id": "2509.02605", "title": "Synthetic Founders: AI-Generated Social Simulations for Startup Validation Research in Computational Social Science", "abstract": "We present a comparative docking experiment that aligns human-subject interview data with large language model (LLM)-driven synthetic personas to evaluate fidelity, divergence, and blind spots in AI-enabled simulation. Fifteen early-stage startup founders were interviewed about their hopes and concerns regarding AI-powered validation, and the same protocol was replicated with AI-generated founder and investor personas. A structured thematic synthesis revealed four categories of outcomes: (1) Convergent themes - commitment-based demand signals, black-box trust barriers, and efficiency gains were consistently emphasized across both datasets; (2) Partial overlaps - founders worried about outliers being averaged away and the stress of real customer validation, while synthetic personas highlighted irrational blind spots and framed AI as a psychological buffer; (3) Human-only themes - relational and advocacy value from early customer engagement and skepticism toward moonshot markets; and (4) Synthetic-only themes - amplified false positives and trauma blind spots, where AI may overstate adoption potential by missing negative historical experiences.\n  We interpret this comparative framework as evidence that LLM-driven personas constitute a form of hybrid social simulation: more linguistically expressive and adaptable than traditional rule-based agents, yet bounded by the absence of lived history and relational consequence. Rather than replacing empirical studies, we argue they function as a complementary simulation category - capable of extending hypothesis space, accelerating exploratory validation, and clarifying the boundaries of cognitive realism in computational social science.", "date": "2025-08-29", "categories": []}
{"id": "2508.18916", "title": "Affective Polarization across European Parliaments", "abstract": "Affective polarization, characterized by increased negativity and hostility towards opposing groups, has become a prominent feature of political discourse worldwide. Our study examines the presence of this type of polarization in a selection of European parliaments in a fully automated manner. Utilizing a comprehensive corpus of parliamentary speeches from the parliaments of six European countries, we employ natural language processing techniques to estimate parliamentarian sentiment. By comparing the levels of negativity conveyed in references to individuals from opposing groups versus one's own, we discover patterns of affectively polarized interactions. The findings demonstrate the existence of consistent affective polarization across all six European parliaments. Although activity correlates with negativity, there is no observed difference in affective polarization between less active and more active members of parliament. Finally, we show that reciprocity is a contributing mechanism in affective polarization between parliamentarians across all six parliaments.", "date": "2025-08-26", "categories": []}
{"id": "2509.00696", "title": "Queuing for Civility: Regulating Emotions and Reducing Toxicity in Digital Discourse", "abstract": "The pervasiveness of online toxicity, including hate speech and trolling, disrupts digital interactions and online well-being. Previous research has mainly focused on post-hoc moderation, overlooking the real-time emotional dynamics of online conversations and the impact of users' emotions on others. This paper presents a graph-based framework to identify the need for emotion regulation within online conversations. This framework promotes self-reflection to manage emotional responses and encourage responsible behaviour in real time. Additionally, a comment queuing mechanism is proposed to address intentional trolls who exploit emotions to inflame conversations. This mechanism introduces a delay in publishing comments, giving users time to self-regulate before further engaging in the conversation and helping maintain emotional balance. Analysis of social media data from Twitter and Reddit demonstrates that the graph-based framework reduced toxicity by 12%, while the comment queuing mechanism decreased the spread of anger by 15%, with only 4% of comments being temporarily held on average. These findings indicate that combining real-time emotion regulation with delayed moderation can significantly improve well-being in online environments.", "date": "2025-08-31", "categories": []}
{"id": "2509.01824", "title": "Journalists' Perceptions of Artificial Intelligence and Disinformation Risks", "abstract": "This study examines journalists' perceptions of the impact of artificial intelligence (AI) on disinformation, a growing concern in journalism due to the rapid expansion of generative AI and its influence on news production and media organizations. Using a quantitative approach, a structured survey was administered to 504 journalists in the Basque Country, identified through official media directories and with the support of the Basque Association of Journalists. This survey, conducted online and via telephone between May and June 2024, included questions on sociodemographic and professional variables, as well as attitudes toward AI's impact on journalism. The results indicate that a large majority of journalists (89.88%) believe AI will considerably or significantly increase the risks of disinformation, and this perception is consistent across genders and media types, but more pronounced among those with greater professional experience. Statistical analyses reveal a significant association between years of experience and perceived risk, and between AI use and risk perception. The main risks identified are the difficulty in detecting false content and deepfakes, and the risk of obtaining inaccurate or erroneous data. Co-occurrence analysis shows that these risks are often perceived as interconnected. These findings highlight the complex and multifaceted concerns of journalists regarding AI's role in the information ecosystem.", "date": "2025-09-01", "categories": []}
{"id": "2501.12640", "title": "Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts", "abstract": "Tackling toxic behavior in digital communication continues to be a pressing concern for both academics and industry professionals. While significant research has explored toxicity on platforms like social networks and discussion boards, podcasts despite their rapid rise in popularity remain relatively understudied in this context. This work seeks to fill that gap by curating a dataset of political podcast transcripts and analyzing them with a focus on conversational structure. Specifically, we investigate how toxicity surfaces and intensifies through sequences of replies within these dialogues, shedding light on the organic patterns by which harmful language can escalate across conversational turns. Warning: Contains potentially abusive/toxic contents.", "date": "2025-01-22", "categories": []}
{"id": "2407.08952", "title": "Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection", "abstract": "Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have demonstrated competitive performance with the help of their rich prior knowledge and excellent in-context learning abilities. However, existing methods face significant limitations, such as the Understanding Ambiguity and Information Scarcity, which significantly undermine the potential of LLMs. To address these shortcomings, we propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DKFND first identifies the knowledge concepts of each news article through a Detection Module. Subsequently, DKFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to evaluate the relevance and confidence of them. Finally, a Determination Module further derives two respective predictions and obtain the final result. Extensive experiments on two public datasets show the efficacy of our proposed method, particularly in low-resource settings.", "date": "2024-07-12", "categories": []}
{"id": "2501.04820", "title": "Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization", "abstract": "The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across a range of online community forums. By focusing on verbal behavioral signatures of extremist traits, we develop a framework for quantifying extremism at both user and community levels. Our research identifies 11 distinct factors, which we term ``The Extremist Eleven,'' as a generalized psychosocial model of extremism. Applying our method to various online communities, we demonstrate an ability to characterize ideologically diverse communities across the 11 extremist traits. We demonstrate the power of this method by analyzing user histories from members of the incel community. We find that our framework accurately predicts which users join the incel community up to 10 months before their actual entry with an AUC of $>0.6$, steadily increasing to AUC ~0.9 three to four months before the event. Further, we find that upon entry into an extremist forum, the users tend to maintain their level of extremism within the community, while still remaining distinguishable from the general online discourse. Our findings contribute to the study of extremism by introducing a more holistic, cross-ideological approach that transcends traditional, trait-specific models.", "date": "2025-01-08", "categories": []}
{"id": "2505.07581", "title": "YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models", "abstract": "Leveraging large language model (LLM) based agents to simulate human social behaviors has recently gained significant attention. In this paper, we introduce a novel social simulator called YuLan-OneSim. Compared to previous works, YuLan-OneSim distinguishes itself in five key aspects: (1) Code-free scenario construction: Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator. All simulation code is automatically generated, significantly reducing the need for programming expertise. (2) Comprehensive default scenarios: We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication, broadening access for a diverse range of social researchers. (3) Evolvable simulation: Our simulator is capable of receiving external feedback and automatically fine-tuning the backbone LLMs, significantly enhancing the simulation quality. (4) Large-scale simulation: By developing a fully responsive agent framework and a distributed simulation architecture, our simulator can handle up to 100,000 agents, ensuring more stable and reliable simulation results. (5) AI social researcher: Leveraging the above features, we develop an AI social researcher. Users only need to propose a research topic, and the AI researcher will automatically analyze the input, construct simulation environments, summarize results, generate technical reports, review and refine the reports--completing the social science research loop. To demonstrate the advantages of YuLan-OneSim, we conduct experiments to evaluate the quality of the automatically generated scenarios, the reliability, efficiency, and scalability of the simulation process, as well as the performance of the AI social researcher.", "date": "2025-05-12", "categories": []}
{"id": "2506.04616", "title": "Subjective Perspectives within Learned Representations Predict High-Impact Innovation", "abstract": "Existing studies of innovation emphasize the power of social structures to shape innovation capacity. Emerging machine learning approaches, however, enable us to model innovators' personal perspectives and interpersonal innovation opportunities as a function of their prior experience. We theorize and then quantify subjective perspectives and their interaction based on innovator positions within the geometric space of concepts inscribed by dynamic machine-learned language representations. Using data on millions of scientists, inventors, screenplay writers, entrepreneurs, and Wikipedia contributors across their respective creative domains, here we show that measured subjective perspectives predict which ideas individuals and groups will creatively attend to and successfully combine in the future. Across all cases and time periods we examine, when perspective diversity is decomposed as the difference between collaborators' perspectives on their creation, and background diversity as the difference between their experiences, the former consistently anticipates creative achievement while the latter portends its opposite. We analyze a natural experiment and simulate creative collaborations between AI agents designed with various perspective and background diversity, which support our observational findings. We explore mechanisms underlying these findings and identify how successful collaborators leverage common language to weave together diverse experiences obtained through trajectories of prior work. These perspectives converge and provoke one another to innovate. We examine the significance of these findings for team formation and research policy.", "date": "2025-06-05", "categories": []}
{"id": "2508.17151", "title": "Integrative Experiments Identify How Punishment Impacts Welfare in Public Goods Games", "abstract": "Punishment as a mechanism for promoting cooperation has been studied extensively for more than two decades, but its effectiveness remains a matter of dispute. Here, we examine how punishment's impact varies across cooperative settings through a large-scale integrative experiment. We vary 14 parameters that characterize public goods games, sampling 360 experimental conditions and collecting 147,618 decisions from 7,100 participants. Our results reveal striking heterogeneity in punishment effectiveness: while punishment consistently increases contributions, its impact on payoffs (i.e., efficiency) ranges from dramatically enhancing welfare (up to 43% improvement) to severely undermining it (up to 44% reduction) depending on the cooperative context. To characterize these patterns, we developed models that outperformed human forecasters (laypeople and domain experts) in predicting punishment outcomes in new experiments. Communication emerged as the most predictive feature, followed by contribution framing (opt-out vs. opt-in), contribution type (variable vs. all-or-nothing), game length (number of rounds), peer outcome visibility (whether participants can see others' earnings), and the availability of a reward mechanism. Interestingly, however, most of these features interact to influence punishment effectiveness rather than operating independently. For example, the extent to which longer games increase the effectiveness of punishment depends on whether groups can communicate. Together, our results refocus the debate over punishment from whether or not it \"works\" to the specific conditions under which it does and does not work. More broadly, our study demonstrates how integrative experiments can be combined with machine learning to uncover generalizable patterns, potentially involving interactions between multiple features, and help generate novel explanations in complex social phenomena.", "date": "2025-08-23", "categories": []}
{"id": "2508.17855", "title": "Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning", "abstract": "Introducing MARK, the Multi-stAge Reasoning frameworK for cultural value survey response simulation, designed to enhance the accuracy, steerability, and interpretability of large language models in this task. The system is inspired by the type dynamics theory in the MBTI psychological framework for personality research. It effectively predicts and utilizes human demographic information for simulation: life-situational stress analysis, group-level personality prediction, and self-weighted cognitive imitation. Experiments on the World Values Survey show that MARK outperforms existing baselines by 10% accuracy and reduces the divergence between model predictions and human preferences. This highlights the potential of our framework to improve zero-shot personalization and help social scientists interpret model predictions.", "date": "2025-08-25", "categories": []}
{"id": "2508.16223", "title": "Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media", "abstract": "With the rapid evolution of technology and the Internet, the proliferation of fake news on social media has become a critical issue, leading to widespread misinformation that can cause societal harm. Traditional fact checking methods are often too slow to prevent the dissemination of false information. Therefore, the need for rapid, automated detection of fake news is paramount. We introduce DaCFake, a novel fake news detection model using a divide and conquer strategy that combines content and context based features. Our approach extracts over eighty linguistic features from news articles and integrates them with either a continuous bag of words or a skipgram model for enhanced detection accuracy. We evaluated the performance of DaCFake on three datasets including Kaggle, McIntire + PolitiFact, and Reuter achieving impressive accuracy rates of 97.88%, 96.05%, and 97.32%, respectively. Additionally, we employed a ten-fold cross validation to further enhance the model's robustness and accuracy. These results highlight the effectiveness of DaCFake in early detection of fake news, offering a promising solution to curb misinformation on social media platforms.", "date": "2025-08-22", "categories": []}
{"id": "2508.10143", "title": "MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection", "abstract": "The large spread of disinformation across digital platforms creates significant challenges to information integrity. This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles, focusing on titles and short text snippets. The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent (which relies on named entity recognition), (iii) a coherence detection agent (using LLM prompt engineering), and (iv) a web-scraped data analyzer that extracts relational triplets for fact checking. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches. The weighted aggregation method, mathematically derived from individual agent misclassification rates, proves superior to algorithmic threshold optimization. The modular architecture makes the system easily scalable, while also maintaining details of the decision processes.", "date": "2025-08-13", "categories": []}
{"id": "2508.04830", "title": "Federal Reserve Communication and the COVID-19 Pandemic", "abstract": "In this study, we examine the Federal Reserve's communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, characterized by notable contextual uncertainty. Through comparative analysis, we juxtapose the Fed's communication during the COVID-19 crisis with its responses during the dot-com and global financial crises, examining content, sentiment, and timing dimensions. Our findings reveal that Fed communication and policy actions were more reactive to the COVID-19 crisis than to previous crises. Additionally, declining sentiment related to financial stability in interest rate announcements and minutes anticipated subsequent accommodative monetary policy decisions. We further document that communicating about UMP has become the \"new normal\" for the Fed's Federal Open Market Committee meeting minutes and Chairman's speeches since the Global Financial Crisis, reflecting an institutional adaptation in communication strategy following periods of economic distress. These findings contribute to our understanding of how central bank communication evolves during crises and how communication strategies adapt to exceptional economic circumstances.", "date": "2025-08-06", "categories": []}
{"id": "2508.04913", "title": "Advancing Hate Speech Detection with Transformers: Insights from the MetaHate", "abstract": "Hate speech is a widespread and harmful form of online discourse, encompassing slurs and defamatory posts that can have serious social, psychological, and sometimes physical impacts on targeted individuals and communities. As social media platforms such as X (formerly Twitter), Facebook, Instagram, Reddit, and others continue to facilitate widespread communication, they also become breeding grounds for hate speech, which has increasingly been linked to real-world hate crimes. Addressing this issue requires the development of robust automated methods to detect hate speech in diverse social media environments. Deep learning approaches, such as vanilla recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs), have achieved good results, but are often limited by issues such as long-term dependencies and inefficient parallelization. This study represents the comprehensive exploration of transformer-based models for hate speech detection using the MetaHate dataset--a meta-collection of 36 datasets with 1.2 million social media samples. We evaluate multiple state-of-the-art transformer models, including BERT, RoBERTa, GPT-2, and ELECTRA, with fine-tuned ELECTRA achieving the highest performance (F1 score: 0.8980). We also analyze classification errors, revealing challenges with sarcasm, coded language, and label noise.", "date": "2025-08-06", "categories": []}
{"id": "2411.18383", "title": "Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy", "abstract": "Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan.", "date": "2024-11-27", "categories": []}
{"id": "2508.04638", "title": "Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech", "abstract": "Counterspeech, i.e. the practice of responding to online hate speech, has gained traction in NLP as a promising intervention. While early work emphasised collaboration with non-governmental organisation stakeholders, recent research trends have shifted toward automated pipelines that reuse a small set of legacy datasets, often without input from affected communities. This paper presents a systematic review of 74 NLP studies on counterspeech, analysing the extent to which stakeholder participation influences dataset creation, model development, and evaluation. To complement this analysis, we conducted a participatory case study with five NGOs specialising in online Gender-Based Violence (oGBV), identifying stakeholder-informed practices for counterspeech generation. Our findings reveal a growing disconnect between current NLP research and the needs of communities most impacted by toxic online content. We conclude with concrete recommendations for re-centring stakeholder expertise in counterspeech research.", "date": "2025-08-06", "categories": []}
{"id": "2508.03262", "title": "Pay What LLM Wants: Can LLM Simulate Economics Experiment with 522 Real-human Persona?", "abstract": "Recent advances in Large Language Models (LLMs) have generated significant interest in their capacity to simulate human-like behaviors, yet most studies rely on fictional personas rather than actual human data. We address this limitation by evaluating LLMs' ability to predict individual economic decision-making using Pay-What-You-Want (PWYW) pricing experiments with real 522 human personas. Our study systematically compares three state-of-the-art multimodal LLMs using detailed persona information from 522 Korean participants in cultural consumption scenarios. We investigate whether LLMs can accurately replicate individual human choices and how persona injection methods affect prediction performance. Results reveal that while LLMs struggle with precise individual-level predictions, they demonstrate reasonable group-level behavioral tendencies. Also, we found that commonly adopted prompting techniques are not much better than naive prompting methods; reconstruction of personal narrative nor retrieval augmented generation have no significant gain against simple prompting method. We believe that these findings can provide the first comprehensive evaluation of LLMs' capabilities on simulating economic behavior using real human data, offering empirical guidance for persona-based simulation in computational social science.", "date": "2025-08-05", "categories": []}
{"id": "2508.03420", "title": "Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations", "abstract": "The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.", "date": "2025-08-05", "categories": []}
{"id": "2508.03599", "title": "OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War", "abstract": "This paper examines the role of Open Source Intelligence (OSINT) on Twitter regarding the Russo-Ukrainian war, distinguishing between genuine OSINT and deceptive misinformation efforts, termed \"BULLSHINT.\" Utilizing a dataset spanning from January 2022 to July 2023, we analyze nearly 2 million tweets from approximately 1,040 users involved in discussing real-time military engagements, strategic analyses, and misinformation related to the conflict. Using sentiment analysis, partisanship detection, misinformation identification, and Named Entity Recognition (NER), we uncover communicative patterns and dissemination strategies within the OSINT community. Significant findings reveal a predominant negative sentiment influenced by war events, a nuanced distribution of pro-Ukrainian and pro-Russian partisanship, and the potential strategic manipulation of information. Additionally, we apply community detection techniques, which are able to identify distinct clusters partisanship, topics, and misinformation, highlighting the complex dynamics of information spread on social media. This research contributes to the understanding of digital warfare and misinformation dynamics, offering insights into the operationalization of OSINT in geopolitical conflicts.", "date": "2025-08-05", "categories": []}
{"id": "2410.08642", "title": "More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram", "abstract": "To address the increasing prevalence of (audio-)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approaches for analyzing multimodal online content. However, existing research often neglects visual content beyond memes, and in addition lacks methods to compare topic models across modalities. Our study addresses these gaps by applying multimodal topic modeling for analyzing conspiracy theories in German-language Telegram channels. We use BERTopic with CLIP for the analysis of textual and visual data in a corpus of ~40, 000 Telegram messages posted in October 2023 in 571 German-language Telegram channels known for disseminating conspiracy theories. Through this dataset, we provide insights into unimodal and multimodal topic models by analyzing symmetry and intersections of topics across modalities. We demonstrate the variety of textual and visual content shared in the channels discovered through the topic modeling, and propose a conceptual framework for the analysis of textual and visual discursive strategies in the communication of conspiracy theories. We apply the framework in a case study of the topic group Israel Gaza.", "date": "2024-10-11", "categories": []}
{"id": "2411.05060", "title": "A Guide to Misinformation Detection Data and Evaluation", "abstract": "Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of 36 datasets that consist of statements or claims, as well as the 9 datasets that consist of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as spurious correlations, or examples that are ambiguous or otherwise impossible to assess for veracity. We find the latter issue is particularly severe and affects most datasets in the literature. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. Finally, we propose and highlight Evaluation Quality Assurance (EQA) as a tool to guide the field toward systemic solutions rather than inadvertently propagating issues in evaluation. Overall, this guide aims to provide a roadmap for higher quality data and better grounded evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.", "date": "2024-11-07", "categories": []}
{"id": "2507.21055", "title": "Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?", "abstract": "Modern news is often comprehensive, weaving together information from diverse domains, including technology, finance, and agriculture. This very comprehensiveness creates a challenge for interpretation, as audiences typically possess specialized knowledge related to their expertise, age, or standpoint. Consequently, a reader might fully understand the financial implications of a story but fail to grasp or even actively misunderstand its legal or technological dimensions, resulting in critical comprehension gaps. In this work, we investigate how to identify these comprehension gaps and provide solutions to improve audiences' understanding of news content, particularly in the aspects of articles outside their primary domains of knowledge. We propose MADES, an agent-based framework designed to simulate societal communication. The framework utilizes diverse agents, each configured to represent a specific occupation or age group. Each agent is equipped with a memory system. These agents are then simulated to discuss the news. This process enables us to monitor and analyze their behavior and cognitive processes. Our findings indicate that the framework can identify confusions and misunderstandings within news content through its iterative discussion process. Based on these accurate identifications, the framework then designs supplementary material. We validated these outcomes using both statistical analysis and human evaluation, and the results show that agents exhibit significantly improved news understanding after receiving this supplementary material.", "date": "2025-04-30", "categories": []}
{"id": "2508.02540", "title": "What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)", "abstract": "In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint three-fold objective, as opposed to the previous work separately addressing these types of bias. In a pipeline concept, we describe the goals and tasks of its steps toward bias identification and provide an example of a visualization that leverages the extracted features and patterns of text reuse.", "date": "2025-08-04", "categories": []}
{"id": "2412.15239", "title": "Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs", "abstract": "Understanding when and why consumers engage with stories is crucial for content creators and platforms. While existing theories suggest that audience beliefs of what is going to happen should play an important role in engagement decisions, empirical work has mostly focused on developing techniques to directly extract features from actual content, rather than capturing forward-looking beliefs, due to the lack of a principled way to model such beliefs in unstructured narrative data. To complement existing feature extraction techniques, this paper introduces a novel framework that leverages large language models to model audience forward-looking beliefs about how stories might unfold. Our method generates multiple potential continuations for each story and extracts features related to expectations, uncertainty, and surprise using established content analysis techniques. Applying our method to over 30,000 book chapters, we demonstrate that our framework complements existing feature engineering techniques by amplifying their marginal explanatory power on average by 31%. The results reveal that different types of engagement-continuing to read, commenting, and voting-are driven by distinct combinations of current and anticipated content features. Our framework provides a novel way to study and explore how audience forward-looking beliefs shape their engagement with narrative media, with implications for marketing strategy in content-focused industries.", "date": "2024-12-13", "categories": []}
{"id": "2505.08841", "title": "Will AI Take My Job? Evolving Perceptions of Automation and Labor Risk in Latin America", "abstract": "As artificial intelligence and robotics increasingly reshape the global labor market, understanding public perceptions of these technologies becomes critical. We examine how these perceptions have evolved across Latin America, using survey data from the 2017, 2018, 2020, and 2023 waves of the Latinobar\\'ometro. Drawing on responses from over 48,000 individuals across 16 countries, we analyze fear of job loss due to artificial intelligence and robotics. Using statistical modeling and latent class analysis, we identify key structural and ideological predictors of concern, with education level and political orientation emerging as the most consistent drivers. Our findings reveal substantial temporal and cross-country variation, with a notable peak in fear during 2018 and distinct attitudinal profiles emerging from latent segmentation. These results offer new insights into the social and structural dimensions of AI anxiety in emerging economies and contribute to a broader understanding of public attitudes toward automation beyond the Global North.", "date": "2025-05-13", "categories": []}
{"id": "2507.21089", "title": "Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations", "abstract": "Social media platforms increasingly employ proactive moderation techniques, such as detecting and curbing toxic and uncivil comments, to prevent the spread of harmful content. Despite these efforts, such approaches are often criticized for creating a climate of censorship and failing to address the underlying causes of uncivil behavior. Our work makes both theoretical and practical contributions by proposing and evaluating two types of emotion monitoring dashboards to users' emotional awareness and mitigate hate speech. In a study involving 211 participants, we evaluate the effects of the two mechanisms on user commenting behavior and emotional experiences. The results reveal that these interventions effectively increase users' awareness of their emotional states and reduce hate speech. However, our findings also indicate potential unintended effects, including increased expression of negative emotions (Angry, Fear, and Sad) when discussing sensitive issues. These insights provide a basis for further research on integrating proactive emotion regulation tools into social media platforms to foster healthier digital interactions.", "date": "2025-06-24", "categories": []}
{"id": "2507.21242", "title": "Bangla BERT for Hyperpartisan News Detection: A Semi-Supervised and Explainable AI Approach", "abstract": "In the current digital landscape, misinformation circulates rapidly, shaping public perception and causing societal divisions. It is difficult to identify hyperpartisan news in Bangla since there aren't many sophisticated natural language processing methods available for this low-resource language. Without effective detection methods, biased content can spread unchecked, posing serious risks to informed discourse. To address this gap, our research fine-tunes Bangla BERT. This is a state-of-the-art transformer-based model, designed to enhance classification accuracy for hyperpartisan news. We evaluate its performance against traditional machine learning models and implement semi-supervised learning to enhance predictions further. Not only that, we use LIME to provide transparent explanations of the model's decision-making process, which helps to build trust in its outcomes. With a remarkable accuracy score of 95.65%, Bangla BERT outperforms conventional approaches, according to our trial data. The findings of this study demonstrate the usefulness of transformer models even in environments with limited resources, which opens the door to further improvements in this area.", "date": "2025-07-28", "categories": []}
{"id": "2507.20528", "title": "Dialogues of Dissent: Thematic and Rhetorical Dimensions of Hate and Counter-Hate Speech in Social Media Conversations", "abstract": "We introduce a novel multi-labeled scheme for joint annotation of hate and counter-hate speech in social media conversations, categorizing hate and counter-hate messages into thematic and rhetorical dimensions. The thematic categories outline different discursive aspects of each type of speech, while the rhetorical dimension captures how hate and counter messages are communicated, drawing on Aristotle's Logos, Ethos and Pathos. We annotate a sample of 92 conversations, consisting of 720 tweets, and conduct statistical analyses, incorporating public metrics, to explore patterns of interaction between the thematic and rhetorical dimensions within and between hate and counter-hate speech. Our findings provide insights into the spread of hate messages on social media, the strategies used to counter them, and their potential impact on online behavior.", "date": "2025-07-28", "categories": []}
{"id": "2507.20614", "title": "Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior", "abstract": "Antisocial behavior (ASB) on social media-including hate speech, harassment, and trolling-poses growing challenges for platform safety and societal wellbeing. While prior work has primarily focused on detecting harmful content after it appears, predictive approaches aim to forecast future harmful behaviors-such as hate speech propagation, conversation derailment, or user recidivism-before they fully unfold. Despite increasing interest, the field remains fragmented, lacking a unified taxonomy or clear synthesis of existing methods. This paper presents a systematic review of over 49 studies on ASB prediction, offering a structured taxonomy of five core task types: early harm detection, harm emergence prediction, harm propagation prediction, behavioral risk prediction, and proactive moderation support. We analyze how these tasks differ by temporal framing, prediction granularity, and operational goals. In addition, we examine trends in modeling techniques-from classical machine learning to pre-trained language models-and assess the influence of dataset characteristics on task feasibility and generalization. Our review highlights methodological challenges, such as dataset scarcity, temporal drift, and limited benchmarks, while outlining emerging research directions including multilingual modeling, cross-platform generalization, and human-in-the-loop systems. By organizing the field around a coherent framework, this survey aims to guide future work toward more robust and socially responsible ASB prediction.", "date": "2025-07-28", "categories": []}
{"id": "2507.19300", "title": "Negative news posts are less prevalent and generate lower user engagement than non-negative news posts across six countries", "abstract": "Although news negativity is often studied, missing is comparative evidence on the prevalence of and engagement with negative political and non-political news posts on social media. We use 6,081,134 Facebook posts published between January 1, 2020, and April 1, 2024, by 97 media organizations in six countries (U.S., UK, Ireland, Poland, France, Spain) and develop two multilingual classifiers for labeling posts as (non-)political and (non-)negative. We show that: (1) negative news posts constitute a relatively small fraction (12.6%); (2) political news posts are neither more nor less negative than non-political news posts; (3) U.S. political news posts are less negative relative to the other countries on average (40% lower odds); (4) Negative news posts get 15% fewer likes and 13% fewer comments than non-negative news posts. Lastly, (5) we provide estimates of the proportion of the total volume of user engagement with negative news posts and show that only between 10.2% to 13.1% of engagement is linked to negative posts by the analyzed news organizations.", "date": "2025-07-25", "categories": []}
{"id": "2401.01405", "title": "Quantifying the Uniqueness and Divisiveness of Presidential Discourse", "abstract": "Do American presidents speak discernibly different from each other? If so, in what ways? And are these differences confined to any single medium of communication? To investigate these questions, this paper introduces a novel metric of uniqueness based on large language models, develops a new lexicon for divisive speech, and presents a framework for assessing the distinctive ways in which presidents speak about their political opponents. Applying these tools to a variety of corpora of presidential speeches, we find considerable evidence that Donald Trump's speech patterns diverge from those of all major party nominees for the presidency in recent history. Trump is significantly more distinctive than his fellow Republicans, whose uniqueness values appear closer to those of the Democrats. Contributing to these differences is Trump's employment of divisive and antagonistic language, particularly when targeting his political opponents. These differences hold across a variety of measurement strategies, arise on both the campaign trail and in official presidential addresses, and do not appear to be an artifact of secular changes in presidential communications.", "date": "2024-01-02", "categories": []}
{"id": "2507.16848", "title": "Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots", "abstract": "In the human-bot symbiotic information ecosystem, social bots play key roles in spreading and correcting disinformation. Understanding their influence is essential for risk control and better governance. However, current studies often rely on simplistic user and network modeling, overlook the dynamic behavior of bots, and lack quantitative evaluation of correction strategies. To fill these gaps, we propose MADD, a Multi Agent based framework for Disinformation Dissemination. MADD constructs a more realistic propagation network by integrating the Barabasi Albert Model for scale free topology and the Stochastic Block Model for community structures, while designing node attributes based on real world user data. Furthermore, MADD incorporates both malicious and legitimate bots, with their controlled dynamic participation allows for quantitative analysis of correction strategies. We evaluate MADD using individual and group level metrics. We experimentally verify the real world consistency of MADD user attributes and network structure, and we simulate the dissemination of six disinformation topics, demonstrating the differential effects of fact based and narrative based correction strategies.", "date": "2025-07-21", "categories": []}
{"id": "2507.16185", "title": "Characterizing Online Activities Contributing to Suicide Mortality among Youth", "abstract": "The recent rise in youth suicide highlights the urgent need to understand how online experiences contribute to this public health issue. Our mixed-methods approach responds to this challenge by developing a set of themes focused on risk factors for suicide mortality in online spaces among youth ages 10-24, and a framework to model these themes at scale. Using 29,124 open text summaries of death investigations between 2013-2022, we conducted a thematic analysis to identify 12 types of online activities that were considered by investigators or next of kin to be relevant in contextualizing a given suicide death. We then develop a zero-shot learning framework to model these 12 themes at scale, and analyze variation in these themes by decedent characteristics and over time. Our work uncovers several online activities related to harm to self, harm to others, interpersonal interactions, activity levels online, and life events, which correspond to different phases of suicide risk from two prominent suicide theories. We find an association between these themes and decedent characteristics like age, means of death, and interpersonal problems, and many themes became more prevalent during the 2020 COVID-19 lockdowns. While digital spaces have taken some steps to address expressions of suicidality online, our work illustrates the opportunities for developing interventions related to less explicit indicators of suicide risk by combining suicide theories with computational research.", "date": "2025-07-22", "categories": []}
{"id": "2507.16298", "title": "WhatsApp Tiplines and Multilingual Claims in the 2021 Indian Assembly Elections", "abstract": "WhatsApp tiplines, first launched in 2019 to combat misinformation, enable users to interact with fact-checkers to verify misleading content. This study analyzes 580 unique claims (tips) from 451 users, covering both high-resource languages (English, Hindi) and a low-resource language (Telugu) during the 2021 Indian assembly elections using a mixed-method approach. We categorize the claims into three categories, election, COVID-19, and others, and observe variations across languages. We compare content similarity through frequent word analysis and clustering of neural sentence embeddings. We also investigate user overlap across languages and fact-checking organizations. We measure the average time required to debunk claims and inform tipline users. Results reveal similarities in claims across languages, with some users submitting tips in multiple languages to the same fact-checkers. Fact-checkers generally require a couple of days to debunk a new claim and share the results with users. Notably, no user submits claims to multiple fact-checking organizations, indicating that each organization maintains a unique audience. We provide practical recommendations for using tiplines during elections with ethical consideration of users' information.", "date": "2025-07-22", "categories": []}
{"id": "2311.09675", "title": "Where Do People Tell Stories Online? Story Detection Across Online Communities", "abstract": "Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert annotations, including binary story labels, story spans, and event spans. We evaluate a range of detection methods using our data, and we identify the distinctive textual features of online storytelling, focusing on storytelling spans. We illuminate distributional characteristics of storytelling on a large community-centric social media platform, and we also conduct a case study on r/ChangeMyView, where storytelling is used as one of many persuasive strategies, illustrating that our data and models can be used for both inter- and intra-community research. Finally, we discuss implications of our tools and analyses for narratology and the study of online communities.", "date": "2023-11-16", "categories": []}
{"id": "2507.14236", "title": "Mining Voter Behaviour and Confidence: A Rule-Based Analysis of the 2022 U.S. Elections", "abstract": "This study explores the relationship between voter trust and their experiences during elections by applying a rule-based data mining technique to the 2022 Survey of the Performance of American Elections (SPAE). Using the Apriori algorithm and setting parameters to capture meaningful associations (support >= 3%, confidence >= 60%, and lift > 1.5), the analysis revealed a strong connection between demographic attributes and voting-related challenges, such as registration hurdles, accessibility issues, and queue times. For instance, respondents who indicated that accessing polling stations was \"very easy\" and who reported moderate confidence were found to be over six times more likely (lift = 6.12) to trust their county's election outcome and experience no registration issues. A further analysis, which adjusted the support threshold to 2%, specifically examined patterns among minority voters. It revealed that 98.16 percent of Black voters who reported easy access to polling locations also had smooth registration experiences. Additionally, those who had high confidence in the vote-counting process were almost two times as likely to identify as Democratic Party supporters. These findings point to the important role that enhancing voting access and offering targeted support can play in building trust in the electoral system, particularly among marginalized communities.", "date": "2025-07-17", "categories": []}
{"id": "2507.14741", "title": "Disparities in Peer Review Tone and the Role of Reviewer Anonymity", "abstract": "The peer review process is often regarded as the gatekeeper of scientific integrity, yet increasing evidence suggests that it is not immune to bias. Although structural inequities in peer review have been widely debated, much less attention has been paid to the subtle ways in which language itself may reinforce disparities. This study undertakes one of the most comprehensive linguistic analyses of peer review to date, examining more than 80,000 reviews in two major journals. Using natural language processing and large-scale statistical modeling, it uncovers how review tone, sentiment, and supportive language vary across author demographics, including gender, race, and institutional affiliation. Using a data set that includes both anonymous and signed reviews, this research also reveals how the disclosure of reviewer identity shapes the language of evaluation. The findings not only expose hidden biases in peer feedback, but also challenge conventional assumptions about anonymity's role in fairness. As academic publishing grapples with reform, these insights raise critical questions about how review policies shape career trajectories and scientific progress.", "date": "2025-07-19", "categories": []}
{"id": "2507.13913", "title": "Political Leaning and Politicalness Classification of Texts", "abstract": "This paper addresses the challenge of automatically classifying text according to political leaning and politicalness using transformer models. We compose a comprehensive overview of existing datasets and models for these tasks, finding that current approaches create siloed solutions that perform poorly on out-of-distribution texts. To address this limitation, we compile a diverse dataset by combining 12 datasets for political leaning classification and creating a new dataset for politicalness by extending 18 existing datasets with the appropriate label. Through extensive benchmarking with leave-one-in and leave-one-out methodologies, we evaluate the performance of existing models and train new ones with enhanced generalization capabilities.", "date": "2025-07-18", "categories": []}
{"id": "2507.13325", "title": "Social and Political Framing in Search Engine Results", "abstract": "Search engines play a crucial role in shaping public discourse by influencing how information is accessed and framed. While prior research has extensively examined various dimensions of search bias -- such as content prioritization, indexical bias, political polarization, and sources of bias -- an important question remains underexplored: how do search engines and ideologically-motivated user queries contribute to bias in search results. This study analyzes the outputs of major search engines using a dataset of political and social topics. The findings reveal that search engines not only prioritize content in ways that reflect underlying biases but also that ideologically-driven user queries exacerbate these biases, resulting in the amplification of specific narratives. Moreover, significant differences were observed across search engines in terms of the sources they prioritize. These results suggest that search engines may play a pivotal role in shaping public perceptions by reinforcing ideological divides, thereby contributing to the broader issue of information polarization.", "date": "2025-07-17", "categories": []}
{"id": "2507.10577", "title": "Truth Sleuth and Trend Bender: AI Agents to fact-check YouTube videos and influence opinions", "abstract": "Misinformation poses a significant threat in today's digital world, often spreading rapidly through platforms like YouTube. This paper introduces a novel approach to combating misinformation by developing an AI-powered system that not only fact-checks claims made in YouTube videos but also actively engages users in the comment section and challenge misleading narratives. Our system comprises two main agents: Truth Sleuth and Trend Bender.\n  Truth Sleuth extracts claims from a YouTube video, uses a Retrieval-Augmented Generation (RAG) approach - drawing on sources like Wikipedia, Google Search, Google FactCheck - to accurately assess their veracity and generates a nuanced and comprehensive report. Through rigorous prompt engineering, Trend Bender leverages this report along with a curated corpus of relevant articles to generate insightful and persuasive comments designed to stimulate a productive debate. With a carefully set up self-evaluation loop, this agent is able to iteratively improve its style and refine its output.\n  We demonstrate the system's capabilities through experiments on established benchmark datasets and a real-world deployment on YouTube, showcasing its potential to engage users and potentially influence perspectives. Our findings highlight the high accuracy of our fact-checking agent, and confirm the potential of AI-driven interventions in combating misinformation and fostering a more informed online space.", "date": "2025-07-11", "categories": []}
{"id": "2305.01723", "title": "Stance Detection: A Practical Guide to Classifying Political Beliefs in Text", "abstract": "Stance detection is identifying expressed beliefs in a document. While researchers widely use sentiment analysis for this, recent research demonstrates that sentiment and stance are distinct. This paper advances text analysis methods by precisely defining stance detection and presenting three distinct approaches: supervised classification, natural language inference, and in-context learning with generative language models. I discuss how document context and trade-offs between resources and workload should inform your methods. For all three approaches I provide guidance on application and validation techniques, as well as coding tutorials for implementation. Finally, I demonstrate how newer classification approaches can replicate supervised classifiers.", "date": "2023-05-02", "categories": []}
{"id": "2504.10157", "title": "SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users", "abstract": "Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recent advances in large language models (LLMs), this approach has shown growing potential in capturing individual differences and predicting group behaviors. However, existing methods face alignment challenges related to the environment, target users, interaction mechanisms, and behavioral patterns. To this end, we introduce SocioVerse, an LLM-agent-driven world model for social simulation. Our framework features four powerful alignment components and a user pool of 10 million real individuals. To validate its effectiveness, we conducted large-scale simulation experiments across three distinct domains: politics, news, and economics. Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.", "date": "2025-04-14", "categories": []}
{"id": "2503.09639", "title": "Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy", "abstract": "Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the availability of vaccination services (MacDonald, 2015), as a case study. To this end, we introduce the VacSim framework with 100 generative agents powered by Large Language Models (LLMs). VacSim simulates vaccine policy outcomes with the following steps: 1) instantiate a population of agents with demographics based on census data; 2) connect the agents via a social network and model vaccine attitudes as a function of social dynamics and disease-related information; 3) design and evaluate various public health interventions aimed at mitigating vaccine hesitancy. To align with real-world results, we also introduce simulation warmup and attitude modulation to adjust agents' attitudes. We propose a series of evaluations to assess the reliability of various LLM simulations. Experiments indicate that models like Llama and Qwen can simulate aspects of human behavior but also highlight real-world alignment challenges, such as inconsistent responses with demographic profiles. This early exploration of LLM-driven simulations is not meant to serve as definitive policy guidance; instead, it serves as a call for action to examine social simulation for policy development.", "date": "2025-03-12", "categories": []}
{"id": "2507.09901", "title": "Large Population Models", "abstract": "Many of society's most pressing challenges, from pandemic response to supply chain disruptions to climate adaptation, emerge from the collective behavior of millions of autonomous agents making decisions over time. Large Population Models (LPMs) offer an approach to understand these complex systems by simulating entire populations with realistic behaviors and interactions at unprecedented scale. LPMs extend traditional modeling approaches through three key innovations: computational methods that efficiently simulate millions of agents simultaneously, mathematical frameworks that learn from diverse real-world data streams, and privacy-preserving communication protocols that bridge virtual and physical environments. This allows researchers to observe how agent behavior aggregates into system-level outcomes and test interventions before real-world implementation. While current AI advances primarily focus on creating \"digital humans\" with sophisticated individual capabilities, LPMs develop \"digital societies\" where the richness of interactions reveals emergent phenomena. By bridging individual agent behavior and population-scale dynamics, LPMs offer a complementary path in AI research illuminating collective intelligence and providing testing grounds for policies and social innovations before real-world deployment. We discuss the technical foundations and some open problems here. LPMs are implemented by the AgentTorch framework (github.com/AgentTorch/AgentTorch)", "date": "2025-07-14", "categories": []}
{"id": "2307.10016", "title": "When Dialects Collide: How Socioeconomic Mixing Affects Language Use", "abstract": "The socioeconomic background of people and how they use standard forms of language are not independent, as demonstrated in various sociolinguistic studies. However, the extent to which these correlations may be influenced by the mixing of people from different socioeconomic classes remains relatively unexplored from a quantitative perspective. In this work we leverage geotagged tweets and transferable computational methods to map deviations from standard English on a large scale, in seven thousand administrative areas of England and Wales. We combine these data with high-resolution income maps to assign a proxy socioeconomic indicator to home-located users. Strikingly, across eight metropolitan areas we find a consistent pattern suggesting that the more different socioeconomic classes mix, the less interdependent the frequency of their departures from standard grammar and their income become. Further, we propose an agent-based model of linguistic variety adoption that sheds light on the mechanisms that produce the observations seen in the data.", "date": "2023-07-19", "categories": []}
{"id": "2503.11713", "title": "Revisiting the Predictability of Performative, Social Events", "abstract": "Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the predicted outcome. Given these dynamics, to what extent can social events be predicted? This question was discussed throughout the 20th century by authors like Merton, Morgenstern, Simon, and others who considered it a central issue in social science methodology. In this work, we provide a modern answer to this old problem. Using recent ideas from performative prediction and outcome indistinguishability, we establish that one can always efficiently predict social events accurately, regardless of how predictions influence data. While achievable, we also show that these predictions are often undesirable, highlighting the limitations of previous desiderata. We end with a discussion of various avenues forward.", "date": "2025-03-12", "categories": []}
{"id": "2504.21582", "title": "MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework", "abstract": "Simulating collective decision-making involves more than aggregating individual behaviors; it emerges from dynamic interactions among individuals. While large language models (LLMs) offer strong potential for social simulation, achieving quantitative alignment with real-world data remains a key challenge. To bridge this gap, we propose the Mean-Field LLM (MF-LLM) framework, the first to incorporate mean field theory into LLM-based social simulation. MF-LLM models bidirectional interactions between individuals and the population through an iterative process, generating population signals to guide individual decisions, which in turn update the signals. This interplay produces coherent trajectories of collective behavior. To improve alignment with real-world data, we introduce IB-Tune, a novel fine-tuning method inspired by the Information Bottleneck principle, which retains population signals most predictive of future actions while filtering redundant history. Evaluated on a real-world social dataset, MF-LLM reduces KL divergence to human population distributions by 47\\% compared to non-mean-field baselines, enabling accurate trend forecasting and effective intervention planning. Generalizing across 7 domains and 4 LLM backbones, MF-LLM provides a scalable, high-fidelity foundation for social simulation.", "date": "2025-04-30", "categories": []}
{"id": "2503.18962", "title": "Representative Ranking for Deliberation in the Public Sphere", "abstract": "Online comment sections, such as those on news sites or social media, have the potential to foster informal public deliberation, However, this potential is often undermined by the frequency of toxic or low-quality exchanges that occur in these settings. To combat this, platforms increasingly leverage algorithmic ranking to facilitate higher-quality discussions, e.g., by using civility classifiers or forms of prosocial ranking. Yet, these interventions may also inadvertently reduce the visibility of legitimate viewpoints, undermining another key aspect of deliberation: representation of diverse views. We seek to remedy this problem by introducing guarantees of representation into these methods. In particular, we adopt the notion of justified representation (JR) from the social choice literature and incorporate a JR constraint into the comment ranking setting. We find that enforcing JR leads to greater inclusion of diverse viewpoints while still being compatible with optimizing for user engagement or other measures of conversational quality.", "date": "2025-03-19", "categories": []}
{"id": "2507.06310", "title": "Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles", "abstract": "Large language models (LLMs) have been increasingly used to build agents in social simulation because of their impressive abilities to generate fluent, contextually coherent dialogues. Such abilities can enhance the realism of models. However, the pursuit of realism is not necessarily compatible with the epistemic foundation of modelling. We argue that LLM agents, in many regards, are too human to model: they are too expressive, detailed and intractable to be consistent with the abstraction, simplification, and interpretability typically demanded by modelling. Through a model-building thought experiment that converts the Bass diffusion model to an LLM-based variant, we uncover five core dilemmas: a temporal resolution mismatch between natural conversation and abstract time steps; the need for intervention in conversations while avoiding undermining spontaneous agent outputs; the temptation to introduce rule-like instructions in prompts while maintaining conversational naturalness; the tension between role consistency and role evolution across time; and the challenge of understanding emergence, where system-level patterns become obscured by verbose micro textual outputs. These dilemmas steer the LLM agents towards an uncanny valley: not abstract enough to clarify underlying social mechanisms, while not natural enough to represent realistic human behaviour. This exposes an important paradox: the realism of LLM agents can obscure, rather than clarify, social dynamics when misapplied. We tease out the conditions in which LLM agents are ideally suited: where system-level emergence is not the focus, linguistic nuances and meaning are central, interactions unfold in natural time, and stable role identity is more important than long-term behavioural evolution. We call for repositioning LLM agents in the ecosystem of social simulation for future applications.", "date": "2025-07-08", "categories": []}
{"id": "2507.06974", "title": "FRaN-X: FRaming and Narratives-eXplorer", "abstract": "We present FRaN-X, a Framing and Narratives Explorer that automatically detects entity mentions and classifies their narrative roles directly from raw text. FRaN-X comprises a two-stage system that combines sequence labeling with fine-grained role classification to reveal how entities are portrayed as protagonists, antagonists, or innocents, using a unique taxonomy of 22 fine-grained roles nested under these three main categories. The system supports five languages (Bulgarian, English, Hindi, Russian, and Portuguese) and two domains (the Russia-Ukraine Conflict and Climate Change). It provides an interactive web interface for media analysts to explore and compare framing across different sources, tackling the challenge of automatically detecting and labeling how entities are framed. Our system allows end users to focus on a single article as well as analyze up to four articles simultaneously. We provide aggregate level analysis including an intuitive graph visualization that highlights the narrative a group of articles are pushing. Our system includes a search feature for users to look up entities of interest, along with a timeline view that allows analysts to track an entity's role transitions across different contexts within the article. The FRaN-X system and the trained models are licensed under an MIT License. FRaN-X is publicly accessible at https://fran-x.streamlit.app/ and a video demonstration is available at https://youtu.be/VZVi-1B6yYk.", "date": "2025-07-09", "categories": []}
{"id": "2409.01754", "title": "Empirical evidence of Large Language Model's influence on human spoken communication", "abstract": "From the invention of writing and the printing press, to television and social media, human history is punctuated by major innovations in communication technology, which fundamentally altered how ideas spread and reshaped our culture. Recent chatbots powered by generative artificial intelligence constitute a novel medium that encodes cultural patterns in their neural representations and disseminates them in conversations with hundreds of millions of people. Understanding whether these patterns transmit into human language, and ultimately shape human culture, is a fundamental question. While fully quantifying the causal impact of a chatbot like ChatGPT on human culture is very challenging, lexicographic shift in human spoken communication may offer an early indicator of such broad phenomenon. Here, we apply econometric causal inference techniques to 740,249 hours of human discourse from 360,445 YouTube academic talks and 771,591 conversational podcast episodes across multiple disciplines. We detect a measurable and abrupt increase in the use of words preferentially generated by ChatGPT, such as delve, comprehend, boast, swift, and meticulous, after its release. These findings suggest a scenario where machines, originally trained on human data and subsequently exhibiting their own cultural traits, can, in turn, measurably reshape human culture. This marks the beginning of a closed cultural feedback loop in which cultural traits circulate bidirectionally between humans and machines. Our results motivate further research into the evolution of human-machine culture, and raise concerns over the erosion of linguistic and cultural diversity, and the risks of scalable manipulation.", "date": "2024-09-03", "categories": []}
{"id": "2507.05301", "title": "News Source Citing Patterns in AI Search Systems", "abstract": "AI-powered search systems are emerging as new information gatekeepers, fundamentally transforming how users access news and information. Despite their growing influence, the citation patterns of these systems remain poorly understood. We address this gap by analyzing data from the AI Search Arena, a head-to-head evaluation platform for AI search systems. The dataset comprises over 24,000 conversations and 65,000 responses from models across three major providers: OpenAI, Perplexity, and Google. Among the over 366,000 citations embedded in these responses, 9% reference news sources. We find that while models from different providers cite distinct news sources, they exhibit shared patterns in citation behavior. News citations concentrate heavily among a small number of outlets and display a pronounced liberal bias, though low-credibility sources are rarely cited. User preference analysis reveals that neither the political leaning nor the quality of cited news sources significantly influences user satisfaction. These findings reveal significant challenges in current AI search systems and have important implications for their design and governance.", "date": "2025-07-07", "categories": []}
{"id": "2507.05443", "title": "Gendered Divides in Online Discussions about Reproductive Rights", "abstract": "The U.S. Supreme Court's 2022 ruling in Dobbs v. Jackson Women's Health Organization marked a turning point in the national debate over reproductive rights. While the ideological divide over abortion is well documented, less is known about how gender and local sociopolitical contexts interact to shape public discourse. Drawing on nearly 10 million abortion-related posts on X (formerly Twitter) from users with inferred gender, ideology and location, we show that gender significantly moderates abortion attitudes and emotional expression, particularly in conservative regions, and independently of ideology. This creates a gender gap in abortion attitudes that grows more pronounced in conservative regions. The leak of the Dobbs draft opinion further intensified online engagement, disproportionately mobilizing pro-abortion women in areas where access was under threat. These findings reveal that abortion discourse is not only ideologically polarized but also deeply structured by gender and place, highlighting the central role of identity in shaping political expression during moments of institutional disruption.", "date": "2025-07-07", "categories": []}
{"id": "2507.05638", "title": "LLMs are Introvert", "abstract": "The exponential growth of social media and generative AI has transformed information dissemination, fostering connectivity but also accelerating the spread of misinformation. Understanding information propagation dynamics and developing effective control strategies is essential to mitigate harmful content. Traditional models, such as SIR, provide basic insights but inadequately capture the complexities of online interactions. Advanced methods, including attention mechanisms and graph neural networks, enhance accuracy but typically overlook user psychology and behavioral dynamics. Large language models (LLMs), with their human-like reasoning, offer new potential for simulating psychological aspects of information spread. We introduce an LLM-based simulation environment capturing agents' evolving attitudes, emotions, and responses. Initial experiments, however, revealed significant gaps between LLM-generated behaviors and authentic human dynamics, especially in stance detection and psychological realism. A detailed evaluation through Social Information Processing Theory identified major discrepancies in goal-setting and feedback evaluation, stemming from the lack of emotional processing in standard LLM training. To address these issues, we propose the Social Information Processing-based Chain of Thought (SIP-CoT) mechanism enhanced by emotion-guided memory. This method improves the interpretation of social cues, personalization of goals, and evaluation of feedback. Experimental results confirm that SIP-CoT-enhanced LLM agents more effectively process social information, demonstrating behaviors, attitudes, and emotions closer to real human interactions. In summary, this research highlights critical limitations in current LLM-based propagation simulations and demonstrates how integrating SIP-CoT and emotional memory significantly enhances the social intelligence and realism of LLM agents.", "date": "2025-07-08", "categories": []}
{"id": "2407.01545", "title": "In the Shadow of Smith`s Invisible Hand: Risks to Economic Stability and Social Wellbeing in the Age of Intelligence", "abstract": "Work is fundamental to societal prosperity and mental health, providing financial security, identity, purpose, and social integration. The emergence of generative artificial intelligence (AI) has catalysed debate on job displacement. Some argue that many new jobs and industries will emerge to offset the displacement, while others foresee a widespread decoupling of economic productivity from human input threatening jobs on an unprecedented scale. This study explores the conditions under which both may be true and examines the potential for a self-reinforcing cycle of recessionary pressures that would necessitate sustained government intervention to maintain job security and economic stability. A system dynamics model was developed to undertake ex ante analysis of the effect of AI-capital deepening on labour underutilisation and demand in the economy. Results indicate that even a moderate increase in the AI-capital-to-labour ratio could increase labour underutilisation to double its current level, decrease per capita disposable income by 26% (95% interval, 20.6% - 31.8%), and decrease the consumption index by 21% (95% interval, 13.6% - 28.3%) by mid-2050. To prevent a reduction in per capita disposable income due to the estimated increase in underutilization, at least a 10.8-fold increase in the new job creation rate would be necessary. Results demonstrate the feasibility of an AI-capital- to-labour ratio threshold beyond which even high rates of new job creation cannot prevent declines in consumption. The precise threshold will vary across economies, emphasizing the urgent need for empirical research tailored to specific contexts. This study underscores the need for governments, civic organisations, and business to work together to ensure a smooth transition to an AI- dominated economy to safeguard the Mental Wealth of nations.", "date": "2024-04-22", "categories": []}
{"id": "2410.04360", "title": "GenSim: A General Social Simulation Platform with Large Language Model based Agents", "abstract": "With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. While prior work has demonstrated significant potential across various domains, much of it has focused on specific scenarios involving a limited number of agents and has lacked the ability to adapt when errors occur during simulation. To overcome these limitations, we propose a novel LLM-agent-based simulation platform called \\textit{GenSim}, which: (1) \\textbf{Abstracts a set of general functions} to simplify the simulation of customized social scenarios; (2) \\textbf{Supports one hundred thousand agents} to better simulate large-scale populations in real-world contexts; (3) \\textbf{Incorporates error-correction mechanisms} to ensure more reliable and long-term simulations. To evaluate our platform, we assess both the efficiency of large-scale agent simulations and the effectiveness of the error-correction mechanisms. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform based on LLM agents, promising to further advance the field of social science.", "date": "2024-10-06", "categories": []}
{"id": "2507.03027", "title": "The Book of Life approach: Enabling richness and scale for life course research", "abstract": "For over a century, life course researchers have faced a choice between two dominant methodological approaches: qualitative methods that analyze rich data but are constrained to small samples, and quantitative survey-based methods that study larger populations but sacrifice data richness for scale. Two recent technological developments now enable us to imagine a hybrid approach that combines some of the depth of the qualitative approach with the scale of quantitative methods. The first development is the steady rise of ''complex log data,'' behavioral data that is logged for purposes other than research but that can be repurposed to construct rich accounts of people's lives. The second is the emergence of large language models (LLMs) with exceptional pattern recognition capabilities on plain text. In this paper, we take a necessary step toward creating this hybrid approach by developing a flexible procedure to transform complex log data into a textual representation of an individual's life trajectory across multiple domains, over time, and in context. We call this data representation a ''book of life.'' We illustrate the feasibility of our approach by writing over 100 million books of life covering many different facets of life, over time and placed in social context using Dutch population-scale registry data. We open source the book of life toolkit (BOLT), and invite the research community to explore the many potential applications of this approach.", "date": "2025-07-02", "categories": []}
{"id": "2506.22821", "title": "Deep learning four decades of human migration", "abstract": "We present a novel and detailed dataset on origin-destination annual migration flows and stocks between 230 countries and regions, spanning the period from 1990 to the present. Our flow estimates are further disaggregated by country of birth, providing a comprehensive picture of migration over the last 35 years. The estimates are obtained by training a deep recurrent neural network to learn flow patterns from 18 covariates for all countries, including geographic, economic, cultural, societal, and political information. The recurrent architecture of the neural network means that the entire past can influence current migration patterns, allowing us to learn long-range temporal correlations. By training an ensemble of neural networks and additionally pushing uncertainty on the covariates through the trained network, we obtain confidence bounds for all our estimates, allowing researchers to pinpoint the geographic regions most in need of additional data collection. We validate our approach on various test sets of unseen data, demonstrating that it significantly outperforms traditional methods estimating five-year flows while delivering a significant increase in temporal resolution. The model is fully open source: all training data, neural network weights, and training code are made public alongside the migration estimates, providing a valuable resource for future studies of human migration.", "date": "2025-06-28", "categories": []}
{"id": "2506.16692", "title": "LegiGPT: Party Politics and Transport Policy with Large Language Model", "abstract": "Given the significant influence of lawmakers' political ideologies on legislative decision-making, analyzing their impact on transportation-related policymaking is of critical importance. This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals. Legislative bill data from South Korea's 21st National Assembly were used to identify key factors shaping transportation policymaking. These include political affiliations and sponsor characteristics. The LLM was employed to classify transportation-related bill proposals through a stepwise filtering process based on keywords, sentences, and contextual relevance. XAI techniques were then applied to examine the relationships between political party affiliation and associated attributes. The results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes. These findings suggest that both parties contributed to bipartisan legislation through different forms of engagement, such as initiating or supporting proposals. This integrated approach offers a valuable tool for understanding legislative dynamics and guiding future policy development, with broader implications for infrastructure planning and governance.", "date": "2025-06-20", "categories": []}
{"id": "2506.22449", "title": "Computational Analysis of Climate Policy", "abstract": "This thesis explores the impact of the Climate Emergency movement on local government climate policy, using computational methods. The Climate Emergency movement sought to accelerate climate action at local government level through the mechanism of Climate Emergency Declarations (CEDs), resulting in a series of commitments from councils to treat climate change as an emergency. With the aim of assessing the potential of current large language models to answer complex policy questions, I first built and configured a system named PALLM (Policy Analysis with a Large Language Model), using the OpenAI model GPT-4. This system is designed to apply a conceptual framework for climate emergency response plans to a dataset of climate policy documents. I validated the performance of this system with the help of local government policymakers, by generating analyses of the climate policies of 11 local governments in Victoria and assessing the policymakers' level of agreement with PALLM's responses. Having established that PALLM's performance is satisfactory, I used it to conduct a large-scale analysis of current policy documents from local governments in the state of Victoria, Australia. This thesis presents the methodology and results of this analysis, comparing the results for councils which have passed a CED to those which did not. This study finds that GPT-4 is capable of high-level policy analysis, with limitations including a lack of reliable attribution, and can also enable more nuanced analysis by researchers. Its use in this research shows that councils which have passed a CED are more likely to have a recent and climate-specific policy, and show more attention to urgency, prioritisation, and equity and social justice, than councils which have not. It concludes that the ability to assess policy documents at scale opens up exciting new opportunities for policy researchers.", "date": "2025-06-13", "categories": []}
{"id": "2406.11980", "title": "Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways", "abstract": "Manually annotating data for computational social science tasks can be costly, time-consuming, and emotionally draining. While recent work suggests that LLMs can perform such annotation tasks in zero-shot settings, little is known about how prompt design impacts LLMs' compliance and accuracy. We conduct a large-scale multi-prompt experiment to test how model selection (ChatGPT, PaLM2, and Falcon7b) and prompt design features (definition inclusion, output type, explanation, and prompt length) impact the compliance and accuracy of LLM-generated annotations on four CSS tasks (toxicity, sentiment, rumor stance, and news frames). Our results show that LLM compliance and accuracy are highly prompt-dependent. For instance, prompting for numerical scores instead of labels reduces all LLMs' compliance and accuracy. The overall best prompting setup is task-dependent, and minor prompt changes can cause large changes in the distribution of generated labels. By showing that prompt design significantly impacts the quality and distribution of LLM-generated annotations, this work serves as both a warning and practical guide for researchers and practitioners.", "date": "2024-06-17", "categories": []}
{"id": "2506.21606", "title": "Large Language Models as symbolic DNA of cultural dynamics", "abstract": "This paper proposes a novel conceptualization of Large Language Models (LLMs) as externalized informational substrates that function analogously to DNA for human cultural dynamics. Rather than viewing LLMs as either autonomous intelligence or mere programmed mimicry, we argue they serve a broader role as repositories that preserve compressed patterns of human symbolic expression--\"fossils\" of meaningful dynamics that retain relational residues without their original living contexts. Crucially, these compressed patterns only become meaningful through human reinterpretation, creating a recursive feedback loop where they can be recombined and cycle back to ultimately catalyze human creative processes. Through analysis of four universal features--compression, decompression, externalization, and recursion--we demonstrate that just as DNA emerged as a compressed and externalized medium for preserving useful cellular dynamics without containing explicit reference to goal-directed physical processes, LLMs preserve useful regularities of human culture without containing understanding of embodied human experience. Therefore, we argue that LLMs' significance lies not in rivaling human intelligence, but in providing humanity a tool for self-reflection and playful hypothesis-generation in a low-stakes, simulated environment. This framework positions LLMs as tools for cultural evolvability, enabling humanity to generate novel hypotheses about itself while maintaining the human interpretation necessary to ground these hypotheses in ongoing human aesthetics and norms.", "date": "2025-06-20", "categories": []}
{"id": "2506.22098", "title": "Involvement drives complexity of language in online debates", "abstract": "Language is a fundamental aspect of human societies, continuously evolving in response to various stimuli, including societal changes and intercultural interactions. Technological advancements have profoundly transformed communication, with social media emerging as a pivotal force that merges entertainment-driven content with complex social dynamics. As these platforms reshape public discourse, analyzing the linguistic features of user-generated content is essential to understanding their broader societal impact. In this paper, we examine the linguistic complexity of content produced by influential users on Twitter across three globally significant and contested topics: COVID-19, COP26, and the Russia-Ukraine war. By combining multiple measures of textual complexity, we assess how language use varies along four key dimensions: account type, political leaning, content reliability, and sentiment. Our analysis reveals significant differences across all four axes, including variations in language complexity between individuals and organizations, between profiles with sided versus moderate political views, and between those associated with higher versus lower reliability scores. Additionally, profiles producing more negative and offensive content tend to use more complex language, with users sharing similar political stances and reliability levels converging toward a common jargon. Our findings offer new insights into the sociolinguistic dynamics of digital platforms and contribute to a deeper understanding of how language reflects ideological and social structures in online spaces.", "date": "2025-06-27", "categories": []}
{"id": "2506.20269", "title": "Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models", "abstract": "With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction methods such as Large Language Models do well in capturing typical narrative elements or even the complex structure of a narrative, applying them to an entire corpus comes with obstacles, such as a high financial or computational cost. We propose a combination of the language understanding capabilities of Large Language Models with the large scale applicability of topic models to dynamically model narrative shifts across time using the Narrative Policy Framework. We apply a topic model and a corresponding change point detection method to find changes that concern a specific topic of interest. Using this model, we filter our corpus for documents that are particularly representative of that change and feed them into a Large Language Model that interprets the change that happened in an automated fashion and distinguishes between content and narrative shifts. We employ our pipeline on a corpus of The Wall Street Journal news paper articles from 2009 to 2023. Our findings indicate that a Large Language Model can efficiently extract a narrative shift if one exists at a given point in time, but does not perform as well when having to decide whether a shift in content or a narrative shift took place.", "date": "2025-06-25", "categories": []}
{"id": "2506.19603", "title": "Social Hatred: Efficient Multimodal Detection of Hatemongers", "abstract": "Automatic detection of online hate speech serves as a crucial step in the detoxification of the online discourse. Moreover, accurate classification can promote a better understanding of the proliferation of hate as a social phenomenon. While most prior work focus on the detection of hateful utterances, we argue that focusing on the user level is as important, albeit challenging. In this paper we consider a multimodal aggregative approach for the detection of hate-mongers, taking into account the potentially hateful texts, user activity, and the user network. Evaluating our method on three unique datasets X (Twitter), Gab, and Parler we show that processing a user's texts in her social context significantly improves the detection of hate mongers, compared to previously used text and graph-based methods. We offer comprehensive set of results obtained in different experimental settings as well as qualitative analysis of illustrative cases. Our method can be used to improve the classification of coded messages, dog-whistling, and racial gas-lighting, as well as to inform intervention measures. Moreover, we demonstrate that our multimodal approach performs well across very different content platforms and over large datasets and networks.", "date": "2025-06-24", "categories": []}
{"id": "2506.19635", "title": "On the efficacy of old features for the detection of new bots", "abstract": "For more than a decade now, academicians and online platform administrators have been studying solutions to the problem of bot detection. Bots are computer algorithms whose use is far from being benign: malicious bots are purposely created to distribute spam, sponsor public characters and, ultimately, induce a bias within the public opinion. To fight the bot invasion on our online ecosystem, several approaches have been implemented, mostly based on (supervised and unsupervised) classifiers, which adopt the most varied account features, from the simplest to the most expensive ones to be extracted from the raw data obtainable through the Twitter public APIs. In this exploratory study, using Twitter as a benchmark, we compare the performances of four state-of-art feature sets in detecting novel bots: one of the output scores of the popular bot detector Botometer, which considers more than 1,000 features of an account to take a decision; two feature sets based on the account profile and timeline; and the information about the Twitter client from which the user tweets. The results of our analysis, conducted on six recently released datasets of Twitter accounts, hint at the possible use of general-purpose classifiers and cheap-to-compute account features for the detection of evolved bots.", "date": "2025-06-24", "categories": []}
{"id": "2506.13783", "title": "Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents", "abstract": "How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to third places (e.g., cafe, store, park), and fewer conversations throughout the town. In interview responses, agents explicitly attributed their behavioral changes to disease-avoidance motivations. A validity check further indicated that they could distinguish between infectious and noninfectious diseases, selectively reducing social engagement only when there was a risk of infection. Our findings highlight the potential of GABM as an experimental tool for exploring complex human social dynamics at scale.", "date": "2025-06-10", "categories": []}
{"id": "2506.17372", "title": "Multimodal Political Bias Identification and Neutralization", "abstract": "Due to the presence of political echo chambers, it becomes imperative to detect and remove subjective bias and emotionally charged language from both the text and images of political articles. However, prior work has focused on solely the text portion of the bias rather than both the text and image portions. This is a problem because the images are just as powerful of a medium to communicate information as text is. To that end, we present a model that leverages both text and image bias which consists of four different steps. Image Text Alignment focuses on semantically aligning images based on their bias through CLIP models. Image Bias Scoring determines the appropriate bias score of images via a ViT classifier. Text De-Biasing focuses on detecting biased words and phrases and neutralizing them through BERT models. These three steps all culminate to the final step of debiasing, which replaces the text and the image with neutralized or reduced counterparts, which for images is done by comparing the bias scores. The results so far indicate that this approach is promising, with the text debiasing strategy being able to identify many potential biased words and phrases, and the ViT model showcasing effective training. The semantic alignment model also is efficient. However, more time, particularly in training, and resources are needed to obtain better results. A human evaluation portion was also proposed to ensure semantic consistency of the newly generated text and images.", "date": "2025-06-20", "categories": []}
{"id": "2305.14597", "title": "Voices of Her: Analyzing Gender Differences in the AI Publication World", "abstract": "While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends. Using the AI Scholar dataset of 78K researchers in the field of AI, we identify several gender differences: (1) Although female researchers tend to have fewer overall citations than males, this citation difference does not hold for all academic-age groups; (2) There exist large gender homophily in co-authorship on AI papers; (3) Female first-authored papers show distinct linguistic styles, such as longer text, more positive emotion words, and more catchy titles than male first-authored papers. Our analysis provides a window into the current demographic trends in our AI community, and encourages more gender equality and diversity in the future. Our code and data are at https://github.com/causalNLP/ai-scholar-gender.", "date": "2023-05-24", "categories": []}
{"id": "2503.16031", "title": "Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content", "abstract": "In the evolving landscape of online discourse, misinformation increasingly adopts humorous tones to evade detection and gain traction. This work introduces Deceptive Humor as a novel research direction, emphasizing how false narratives, when coated in humor, can become more difficult to detect and more likely to spread. To support research in this space, we present the Deceptive Humor Dataset (DHD) a collection of humor-infused comments derived from fabricated claims using the ChatGPT-4o model. Each entry is labeled with a Satire Level (from 1 for subtle satire to 3 for overt satire) and categorized into five humor types: Dark Humor, Irony, Social Commentary, Wordplay, and Absurdity. The dataset spans English, Telugu, Hindi, Kannada, Tamil, and their code-mixed forms, making it a valuable resource for multilingual analysis. DHD offers a structured foundation for understanding how humor can serve as a vehicle for the propagation of misinformation, subtly enhancing its reach and impact. Strong baselines are established to encourage further research and model development in this emerging area.", "date": "2025-03-20", "categories": []}
{"id": "2506.16190", "title": "Web(er) of Hate: A Survey on How Hate Speech Is Typed", "abstract": "The curation of hate speech datasets involves complex design decisions that balance competing priorities. This paper critically examines these methodological choices in a diverse range of datasets, highlighting common themes and practices, and their implications for dataset reliability. Drawing on Max Weber's notion of ideal types, we argue for a reflexive approach in dataset creation, urging researchers to acknowledge their own value judgments during dataset construction, fostering transparency and methodological rigour.", "date": "2025-06-19", "categories": []}
{"id": "2506.16337", "title": "Generalizability of Media Frames: Corpus creation and analysis across countries", "abstract": "Frames capture aspects of an issue that are emphasized in a debate by interlocutors and can help us understand how political language conveys different perspectives and ultimately shapes people's opinions. The Media Frame Corpus (MFC) is the most commonly used framework with categories and detailed guidelines for operationalizing frames. It is, however, focused on a few salient U.S. news issues, making it unclear how well these frames can capture news issues in other cultural contexts. To explore this, we introduce FrameNews-PT, a dataset of Brazilian Portuguese news articles covering political and economic news and annotate it within the MFC framework. Through several annotation rounds, we evaluate the extent to which MFC frames generalize to the Brazilian debate issues. We further evaluate how fine-tuned and zero-shot models perform on out-of-domain data. Results show that the 15 MFC frames remain broadly applicable with minor revisions of the guidelines. However, some MFC frames are rarely used, and novel news issues are analyzed using general 'fall-back' frames. We conclude that cross-cultural frame use requires careful consideration.", "date": "2025-06-19", "categories": []}
{"id": "2506.17073", "title": "LLM-Based Bot Broadens the Range of Arguments in Online Discussions, Even When Transparently Disclosed as AI", "abstract": "A wide range of participation is essential for democracy, as it helps prevent the dominance of extreme views, erosion of legitimacy, and political polarization. However, engagement in online political discussions often features a limited spectrum of views due to high levels of self-selection and the tendency of online platforms to facilitate exchanges primarily among like-minded individuals. This study examines whether an LLM-based bot can widen the scope of perspectives expressed by participants in online discussions through two pre-registered randomized experiments conducted in a chatroom. We evaluate the impact of a bot that actively monitors discussions, identifies missing arguments, and introduces them into the conversation. The results indicate that our bot significantly expands the range of arguments, as measured by both objective and subjective metrics. Furthermore, disclosure of the bot as AI does not significantly alter these effects. These findings suggest that LLM-based moderation tools can positively influence online political discourse.", "date": "2025-06-20", "categories": []}
{"id": "2506.14836", "title": "Detecting Narrative Shifts through Persistent Structures: A Topological Analysis of Media Discourse", "abstract": "How can we detect when global events fundamentally reshape public discourse? This study introduces a topological framework for identifying structural change in media narratives using persistent homology. Drawing on international news articles surrounding major events - including the Russian invasion of Ukraine (Feb 2022), the murder of George Floyd (May 2020), the U.S. Capitol insurrection (Jan 2021), and the Hamas-led invasion of Israel (Oct 2023) - we construct daily co-occurrence graphs of noun phrases to trace evolving discourse. Each graph is embedded and transformed into a persistence diagram via a Vietoris-Rips filtration. We then compute Wasserstein distances and persistence entropies across homological dimensions to capture semantic disruption and narrative volatility over time. Our results show that major geopolitical and social events align with sharp spikes in both H0 (connected components) and H1 (loops), indicating sudden reorganization in narrative structure and coherence. Cross-correlation analyses reveal a typical lag pattern in which changes to component-level structure (H0) precede higher-order motif shifts (H1), suggesting a bottom-up cascade of semantic change. An exception occurs during the Russian invasion of Ukraine, where H1 entropy leads H0, possibly reflecting top-down narrative framing before local discourse adjusts. Persistence entropy further distinguishes tightly focused from diffuse narrative regimes. These findings demonstrate that persistent homology offers a mathematically principled, unsupervised method for detecting inflection points and directional shifts in public attention - without requiring prior knowledge of specific events. This topological approach advances computational social science by enabling real-time detection of semantic restructuring during crises, protests, and information shocks.", "date": "2025-06-14", "categories": []}
{"id": "2506.15041", "title": "Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models", "abstract": "As interest in economic narratives has grown in recent years, so has the number of pipelines dedicated to extracting such narratives from texts. Pipelines often employ a mix of state-of-the-art natural language processing techniques, such as BERT, to tackle this task. While effective on foundational linguistic operations essential for narrative extraction, such models lack the deeper semantic understanding required to distinguish extracting economic narratives from merely conducting classic tasks like Semantic Role Labeling. Instead of relying on complex model pipelines, we evaluate the benefits of Large Language Models (LLMs) by analyzing a corpus of Wall Street Journal and New York Times newspaper articles about inflation. We apply a rigorous narrative definition and compare GPT-4o outputs to gold-standard narratives produced by expert annotators. Our results suggests that GPT-4o is capable of extracting valid economic narratives in a structured format, but still falls short of expert-level performance when handling complex documents and narratives. Given the novelty of LLMs in economic research, we also provide guidance for future work in economics and the social sciences that employs LLMs to pursue similar objectives.", "date": "2025-06-18", "categories": []}
{"id": "2312.16490", "title": "Exploring news intent and its application: A theory-driven approach", "abstract": "Understanding the intent behind information is crucial. However, news as a medium of public discourse still lacks a structured investigation of perceived news intent and its application. To advance this field, this paper reviews interdisciplinary studies on intentional action and introduces a conceptual deconstruction-based news intent understanding framework (NINT). This framework identifies the components of intent, facilitating a structured representation of news intent and its applications. Building upon NINT, we contribute a new intent perception dataset. Moreover, we investigate the potential of intent assistance on news-related tasks, such as significant improvement (+2.2% macF1) in the task of fake news detection. We hope that our findings will provide valuable insights into action-based intent cognition and computational social science.", "date": "2023-12-27", "categories": []}
{"id": "2506.14645", "title": "Passing the Turing Test in Political Discourse: Fine-Tuning LLMs to Mimic Polarized Social Media Comments", "abstract": "The increasing sophistication of large language models (LLMs) has sparked growing concerns regarding their potential role in exacerbating ideological polarization through the automated generation of persuasive and biased content. This study explores the extent to which fine-tuned LLMs can replicate and amplify polarizing discourse within online environments. Using a curated dataset of politically charged discussions extracted from Reddit, we fine-tune an open-source LLM to produce context-aware and ideologically aligned responses. The model's outputs are evaluated through linguistic analysis, sentiment scoring, and human annotation, with particular attention to credibility and rhetorical alignment with the original discourse. The results indicate that, when trained on partisan data, LLMs are capable of producing highly plausible and provocative comments, often indistinguishable from those written by humans. These findings raise significant ethical questions about the use of AI in political discourse, disinformation, and manipulation campaigns. The paper concludes with a discussion of the broader implications for AI governance, platform regulation, and the development of detection tools to mitigate adversarial fine-tuning risks.", "date": "2025-06-17", "categories": []}
{"id": "2502.14718", "title": "Entity Framing and Role Portrayal in the News", "abstract": "We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence.", "date": "2025-02-20", "categories": []}
{"id": "2506.12552", "title": "Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts", "abstract": "In an age characterized by the proliferation of mis- and disinformation online, it is critical to empower readers to understand the content they are reading. Important efforts in this direction rely on manual or automatic fact-checking, which can be challenging for emerging claims with limited information. Such scenarios can be handled by assessing the reliability and the political bias of the source of the claim, i.e., characterizing entire news outlets rather than individual claims or articles. This is an important but understudied research direction. While prior work has looked into linguistic and social contexts, we do not analyze individual articles or information in social media. Instead, we propose a novel methodology that emulates the criteria that professional fact-checkers use to assess the factuality and political bias of an entire outlet. Specifically, we design a variety of prompts based on these criteria and elicit responses from large language models (LLMs), which we aggregate to make predictions. In addition to demonstrating sizable improvements over strong baselines via extensive experiments with multiple LLMs, we provide an in-depth error analysis of the effect of media popularity and region on model performance. Further, we conduct an ablation study to highlight the key components of our dataset that contribute to these improvements. To facilitate future research, we released our dataset and code at https://github.com/mbzuai-nlp/llm-media-profiling.", "date": "2025-06-14", "categories": []}
{"id": "2506.12925", "title": "Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks", "abstract": "Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.", "date": "2025-06-15", "categories": []}
{"id": "2411.13187", "title": "Engagement-Driven Content Generation with Large Language Models", "abstract": "Large Language Models (LLMs) demonstrate significant persuasive capabilities in one-on-one interactions, but their influence within social networks, where interconnected users and complex opinion dynamics pose unique challenges, remains underexplored. This paper addresses the research question: \\emph{Can LLMs generate meaningful content that maximizes user engagement on social networks?}\n  To answer this, we propose a pipeline using reinforcement learning with simulated feedback, where the network's response to LLM-generated content (i.e., the reward) is simulated through a formal engagement model. This approach bypasses the temporal cost and complexity of live experiments, enabling an efficient feedback loop between the LLM and the network under study. It also allows to control over endogenous factors such as the LLM's position within the social network and the distribution of opinions on a given topic. Our approach is adaptive to the opinion distribution of the underlying network and agnostic to the specifics of the engagement model, which is embedded as a plug-and-play component. Such flexibility makes it suitable for more complex engagement tasks and interventions in computational social science.\n  Using our framework, we analyze the performance of LLMs in generating social engagement under different conditions, showcasing their full potential in this task. The experimental code is publicly available at https://github.com/mminici/Engagement-Driven-Content-Generation.", "date": "2024-11-20", "categories": []}
{"id": "2506.10421", "title": "Beyond the Battlefield: Framing Analysis of Media Coverage in Conflict Reporting", "abstract": "Framing used by news media, especially in times of conflict, can have substantial impact on readers' opinion, potentially aggravating the conflict itself. Current studies on the topic of conflict framing have limited insights due to their qualitative nature or only look at surface level generic frames without going deeper. In this work, we identify indicators of war and peace journalism, as outlined by prior work in conflict studies, in a corpus of news articles reporting on the Israel-Palestine war. For our analysis, we use computational approaches, using a combination of frame semantics and large language models to identify both communicative framing and its connection to linguistic framing. Our analysis reveals a higher focus on war based reporting rather than peace based. We also show substantial differences in reporting across the US, UK, and Middle Eastern news outlets in framing who the assailant and victims of the conflict are, surfacing biases within the media.", "date": "2025-06-12", "categories": []}
{"id": "2506.10789", "title": "FASCIST-O-METER: Classifier for Neo-fascist Discourse Online", "abstract": "Neo-fascism is a political and societal ideology that has been having remarkable growth in the last decade in the United States of America (USA), as well as in other Western societies. It poses a grave danger to democracy and the minorities it targets, and it requires active actions against it to avoid escalation. This work presents the first-of-its-kind neo-fascist coding scheme for digital discourse in the USA societal context, overseen by political science researchers. Our work bridges the gap between Natural Language Processing (NLP) and political science against this phenomena. Furthermore, to test the coding scheme, we collect a tremendous amount of activity on the internet from notable neo-fascist groups (the forums of Iron March and Stormfront.org), and the guidelines are applied to a subset of the collected posts. Through crowdsourcing, we annotate a total of a thousand posts that are labeled as neo-fascist or non-neo-fascist. With this labeled data set, we fine-tune and test both Small Language Models (SLMs) and Large Language Models (LLMs), obtaining the very first classification models for neo-fascist discourse. We find that the prevalence of neo-fascist rhetoric in this kind of forum is ever-present, making them a good target for future research. The societal context is a key consideration for neo-fascist speech when conducting NLP research. Finally, the work against this kind of political movement must be pressed upon and continued for the well-being of a democratic society. Disclaimer: This study focuses on detecting neo-fascist content in text, similar to other hate speech analyses, without labeling individuals or organizations.", "date": "2025-06-12", "categories": []}
{"id": "2412.10913", "title": "Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict", "abstract": "This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (r/Palestine, r/Judaism, r/IsraelPalestine, and r/worldnews). A lexicon-based, unsupervised methodology was developed to measure \"extreme opinions\" by considering factors such as anger, polarity, and subjectivity. The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events, such as the IDF's bombings of Al Quds Hospital and the Jabalia Refugee Camp, and the end of a ceasefire following a terrorist attack. Additionally, this study explores the distribution and correlation of these scores across different subreddits and over time, providing insights into the propagation of polarised sentiments in response to conflict events. By examining the quantitative effects of each score on extremism and analysing word cloud similarities through Jaccard indices, the research offers a nuanced understanding of the factors driving extreme online opinions. This approach underscores the potential of social media analytics in capturing the complex interplay between real-world events and online discourse, while also highlighting the limitations and challenges of measuring extremism in social media contexts.", "date": "2024-12-14", "categories": []}
{"id": "2506.06813", "title": "BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communities", "abstract": "Understanding political discourse in online spaces is crucial for analyzing public opinion and ideological polarization. While social computing and computational linguistics have explored such discussions in English, such research efforts are significantly limited in major yet under-resourced languages like Bengali due to the unavailability of datasets. In this paper, we present a multilingual dataset of Bengali transnational political discourse (BTPD) collected from three online platforms, each representing distinct community structures and interaction dynamics. Besides describing how we hand-curated the dataset through community-informed keyword-based retrieval, this paper also provides a general overview of its topics and multilingual content.", "date": "2025-06-07", "categories": []}
{"id": "2506.07606", "title": "PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels", "abstract": "Stance detection identifies the viewpoint expressed in text toward a specific target, such as a political figure. While previous datasets have focused primarily on tweet-level stances from established platforms, user-level stance resources, especially on emerging platforms like Bluesky remain scarce. User-level stance detection provides a more holistic view by considering a user's complete posting history rather than isolated posts. We present the first stance detection dataset for the 2024 U.S. presidential election, collected from Bluesky and centered on Kamala Harris and Donald Trump. The dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. PolitiSky24 was created using a carefully evaluated pipeline combining advanced information retrieval and large language models, which generates stance labels with supporting rationales and text spans for transparency. The labeling approach achieves 81\\% accuracy with scalable LLMs. This resource addresses gaps in political stance analysis through its timeliness, open-data nature, and user-level perspective. The dataset is available at https://doi.org/10.5281/zenodo.15616911", "date": "2025-06-09", "categories": []}
{"id": "2408.07237", "title": "A semantic embedding space based on large language models for modelling human beliefs", "abstract": "Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation.", "date": "2024-08-13", "categories": []}
{"id": "2506.05388", "title": "taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades", "abstract": "Open-access corpora are essential for advancing natural language processing (NLP) and computational social science (CSS). However, large-scale resources for German remain limited, restricting research on linguistic trends and societal issues such as gender bias. We present taz2024full, the largest publicly available corpus of German newspaper articles to date, comprising over 1.8 million texts from taz, spanning 1980 to 2024.\n  As a demonstration of the corpus's utility for bias and discrimination research, we analyse gender representation across four decades of reporting. We find a consistent overrepresentation of men, but also a gradual shift toward more balanced coverage in recent years. Using a scalable, structured analysis pipeline, we provide a foundation for studying actor mentions, sentiment, and linguistic framing in German journalistic texts.\n  The corpus supports a wide range of applications, from diachronic language analysis to critical media studies, and is freely available to foster inclusive and reproducible research in German-language NLP.", "date": "2025-06-03", "categories": []}
{"id": "2506.05415", "title": "Automatically Detecting Amusing Games in Wordle", "abstract": "We explore automatically predicting which Wordle games Reddit users find amusing.\n  We scrape approximately 80k reactions by Reddit users to Wordle games from Reddit, classify the reactions as expressing amusement or not using OpenAI's GPT-3.5 using few-shot prompting, and verify that GPT-3.5's labels roughly correspond to human labels.\n  We then extract features from Wordle games that can predict user amusement. We demonstrate that the features indeed provide a (weak) signal that predicts user amusement as predicted by GPT-3.5.\n  Our results indicate that user amusement at Wordle games can be predicted computationally to some extent. We explore which features of the game contribute to user amusement.\n  We find that user amusement is predictable, indicating a measurable aspect of creativity infused into Wordle games through humor.", "date": "2025-06-04", "categories": []}
{"id": "2506.06083", "title": "A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data", "abstract": "The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical concern for researchers is the trustworthiness of results when Machine Learning (ML) and Natural Language Processing (NLP) tools are used to analyse such data. We argue that confidence in the credibility and robustness of results depends on adopting a 'human-in-the-loop' methodology that is able to provide researchers with control over the analytical process, while retaining the benefits of using ML and NLP. With this in mind, we propose a novel methodological framework for Computational Grounded Theory (CGT) that supports the analysis of large qualitative datasets, while maintaining the rigour of established Grounded Theory (GT) methodologies. To illustrate the framework's value, we present the results of testing it on a dataset collected from Reddit in a study aimed at understanding tutors' experiences in the gig economy.", "date": "2025-06-06", "categories": []}
{"id": "2409.19257", "title": "Inducing lexicons of in-group language with socio-temporal context", "abstract": "In-group language is an important signifier of group dynamics. This paper proposes a novel method for inducing lexicons of in-group language, which incorporates its socio-temporal context. Existing methods for lexicon induction do not capture the evolving nature of in-group language, nor the social structure of the community. Using dynamic word and user embeddings trained on conversations from online anti-women communities, our approach outperforms prior methods for lexicon induction. We develop a test set for the task of lexicon induction and a new lexicon of manosphere language, validated by human experts, which quantifies the relevance of each term to a specific sub-community at a given point in time. Finally, we present novel insights on in-group language which illustrate the utility of this approach.", "date": "2024-09-28", "categories": []}
{"id": "2506.01807", "title": "Propaganda and Information Dissemination in the Russo-Ukrainian War: Natural Language Processing of Russian and Western Twitter Narratives", "abstract": "The conflict in Ukraine has been not only characterised by military engagement but also by a significant information war, with social media platforms like X, formerly known as Twitter playing an important role in shaping public perception. This article provides an analysis of tweets from propaganda accounts and trusted accounts collected from the onset of the war, February 2022 until the middle of May 2022 with n=40,000 total tweets. We utilise natural language processing and machine learning algorithms to assess the sentiment and identify key themes, topics and narratives across the dataset with human-in-the-loop (HITL) analysis throughout. Our findings indicate distinct strategies in how information is created, spread, and targeted at different audiences by both sides. Propaganda accounts frequently employ emotionally charged language and disinformation to evoke fear and distrust, whereas other accounts, primarily Western tend to focus on factual reporting and humanitarian aspects of the conflict. Clustering analysis reveals groups of accounts with similar behaviours, which we suspect indicates the presence of coordinated efforts. This research attempts to contribute to our understanding of the dynamics of information warfare and offers techniques for future studies on social media influence in military conflicts.", "date": "2025-06-02", "categories": []}
{"id": "2506.03503", "title": "Computational Architects of Society: Quantum Machine Learning for Social Rule Genesis", "abstract": "The quantification of social science remains a longstanding challenge, largely due to the philosophical nature of its foundational theories. Although quantum computing has advanced rapidly in recent years, its relevance to social theory remains underexplored. Most existing research focuses on micro-cognitive models or philosophical analogies, leaving a gap in system-level applications of quantum principles to the analysis of social systems. This study addresses that gap by proposing a theoretical and computational framework that combines quantum mechanics with Generative AI to simulate the emergence and evolution of social norms. Drawing on core quantum concepts--such as superposition, entanglement, and probabilistic measurement--this research models society as a dynamic, uncertain system and sets up five ideal-type experiments. These scenarios are simulated using 25 generative agents, each assigned evolving roles as compliers, resistors, or enforcers. Within a simulated environment monitored by a central observer (the Watcher), agents interact, respond to surveillance, and adapt to periodic normative disruptions. These interactions allow the system to self-organize under external stress and reveal emergent patterns. Key findings show that quantum principles, when integrated with generative AI, enable the modeling of uncertainty, emergence, and interdependence in complex social systems. Simulations reveal patterns including convergence toward normative order, the spread of resistance, and the spontaneous emergence of new equilibria in social rules. In conclusion, this study introduces a novel computational lens that lays the groundwork for a quantum-informed social theory. It offers interdisciplinary insights into how society can be understood not just as a structure to observe but as a dynamic system to simulate and redesign through quantum technologies.", "date": "2025-06-04", "categories": []}
{"id": "2312.05990", "title": "Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals", "abstract": "While researchers often study message features like moral content in text, such as party manifestos and social media, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contextual sensitivity and are limited by the vocabularies developed for the original applications. In this paper, we present an approach to construct vec-tionary measurement tools that boost validated dictionaries with word embeddings through nonlinear optimization. By harnessing semantic relationships encoded by embeddings, vec-tionaries improve the measurement of message features from text, especially those in short format, by expanding the applicability of original vocabularies to other contexts. Importantly, a vec-tionary can produce additional metrics to capture the valence and ambivalence of a message feature beyond its strength in texts. Using moral content in tweets as a case study, we illustrate the steps to construct the moral foundations vec-tionary, showcasing its ability to process texts missed by conventional dictionaries and word embedding methods and to produce measurements better aligned with crowdsourced human assessments. Furthermore, additional metrics from the vec-tionary unveiled unique insights that facilitated predicting outcomes such as message retransmission.", "date": "2023-12-10", "categories": []}
{"id": "2506.02740", "title": "Stereotypical gender actions can be extracted from Web text", "abstract": "We extracted gender-specific actions from text corpora and Twitter, and compared them to stereotypical expectations of people. We used Open Mind Common Sense (OMCS), a commonsense knowledge repository, to focus on actions that are pertinent to common sense and daily life of humans. We use the gender information of Twitter users and Web-corpus-based pronoun/name gender heuristics to compute the gender bias of the actions. With high recall, we obtained a Spearman correlation of 0.47 between corpus-based predictions and a human gold standard, and an area under the ROC curve of 0.76 when predicting the polarity of the gold standard. We conclude that it is feasible to use natural text (and a Twitter-derived corpus in particular) in order to augment commonsense repositories with the stereotypical gender expectations of actions. We also present a dataset of 441 commonsense actions with human judges' ratings on whether the action is typically/slightly masculine/feminine (or neutral), and another larger dataset of 21,442 actions automatically rated by the methods we investigate in this study.", "date": "2025-06-03", "categories": []}
{"id": "2506.00737", "title": "Narrative Media Framing in Political Discourse", "abstract": "Narrative frames are a powerful way of conceptualizing and communicating complex, controversial ideas, however automated frame analysis to date has mostly overlooked this framing device. In this paper, we connect elements of narrativity with fundamental aspects of framing, and present a framework which formalizes and operationalizes such aspects. We annotate and release a data set of news articles in the climate change domain, analyze the dominance of narrative frame components across political leanings, and test LLMs in their ability to predict narrative frames and their components. Finally, we apply our framework in an unsupervised way to elicit components of narrative framing in a second domain, the COVID-19 crisis, where our predictions are congruent with prior theoretical work showing the generalizability of our approach.", "date": "2025-05-31", "categories": []}
{"id": "2506.01627", "title": "MVAN: Multi-View Attention Networks for Fake News Detection on Social Media", "abstract": "Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, \\textbf{M}ulti-\\textbf{V}iew \\textbf{A}ttention \\textbf{N}etworks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5\\% in accuracy on average, and produce a reasonable explanation.", "date": "2025-06-02", "categories": []}
{"id": "2505.24028", "title": "Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine", "abstract": "This paper presents one of the top-performing solutions to the UNLP 2025 Shared Task on Detecting Manipulation in Social Media. The task focuses on detecting and classifying rhetorical and stylistic manipulation techniques used to influence Ukrainian Telegram users. For the classification subtask, we fine-tuned the Gemma 2 language model with LoRA adapters and applied a second-level classifier leveraging meta-features and threshold optimization. For span detection, we employed an XLM-RoBERTa model trained for multi-target, including token binary classification. Our approach achieved 2nd place in classification and 3rd place in span detection.", "date": "2025-05-29", "categories": []}
{"id": "2505.24646", "title": "PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder", "abstract": "Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. While existing embedding models excel at capturing general meaning, they often overlook ideological nuances, limiting their effectiveness in tasks that require an understanding of political bias. To address this gap, we introduce PRISM, the first framework designed to Produce inteRpretable polItical biaS eMbeddings. PRISM operates in two key stages: (1) Controversial Topic Bias Indicator Mining, which systematically extracts fine-grained political topics and their corresponding bias indicators from weakly labeled news data, and (2) Cross-Encoder Political Bias Embedding, which assigns structured bias scores to news articles based on their alignment with these indicators. This approach ensures that embeddings are explicitly tied to bias-revealing dimensions, enhancing both interpretability and predictive power. Through extensive experiments on two large-scale datasets, we demonstrate that PRISM outperforms state-of-the-art text embedding models in political bias classification while offering highly interpretable representations that facilitate diversified retrieval and ideological analysis. The source code is available at https://github.com/dukesun99/ACL-PRISM.", "date": "2025-05-30", "categories": []}
{"id": "2410.08948", "title": "Emergent social conventions and collective bias in LLM populations", "abstract": "Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong collective biases can emerge during this process, even when agents exhibit no bias individually. Last, we examine how committed minority groups of adversarial LLM agents can drive social change by imposing alternative social conventions on the larger population. Our results show that AI systems can autonomously develop social conventions without explicit programming and have implications for designing AI systems that align, and remain aligned, with human values and societal goals.", "date": "2024-10-11", "categories": []}
{"id": "2503.20960", "title": "Multi-Modal Framing Analysis of News", "abstract": "Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its reception. So far, such studies have been narrow, in that they use a fixed set of pre-defined frames and focus only on the text, ignoring the visual contexts in which those texts appear. Especially for framing in the news, this leaves out valuable information about editorial choices, which include not just the written article but also accompanying photographs. To overcome such limitations, we present a method for conducting multi-modal, multi-label framing analysis at scale using large (vision-) language models. Grounding our work in framing theory, we extract latent meaning embedded in images used to convey a certain point and contrast that to the text by comparing the respective frames used. We also identify highly partisan framing of topics with issue-specific frame analysis found in prior qualitative work. We demonstrate a method for doing scalable integrative framing analysis of both text and image in news, providing a more complete picture for understanding media bias.", "date": "2025-03-26", "categories": []}
{"id": "2505.23058", "title": "Be.FM: Open Foundation Models for Human Behavior", "abstract": "Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of the first open foundation models designed for human behavior modeling. Built upon open-source large language models and fine-tuned on a diverse range of behavioral data, Be.FM can be used to understand and predict human decision-making. We construct a comprehensive set of benchmark tasks for testing the capabilities of behavioral foundation models. Our results demonstrate that Be.FM can predict behaviors, infer characteristics of individuals and populations, generate insights about contexts, and apply behavioral science knowledge.", "date": "2025-05-29", "categories": []}
{"id": "2410.19064", "title": "The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents", "abstract": "With the growing spread of misinformation online, understanding how true news evolves into fake news has become crucial for early detection and prevention. However, previous research has often assumed fake news inherently exists rather than exploring its gradual formation. To address this gap, we propose FUSE (Fake news evolUtion Simulation framEwork), a novel Large Language Model (LLM)-based simulation approach explicitly focusing on fake news evolution from real news. Our framework model a social network with four distinct types of LLM agents commonly observed in daily interactions: spreaders who propagate information, commentators who provide interpretations, verifiers who fact-check, and bystanders who observe passively to simulate realistic daily interactions that progressively distort true news. To quantify these gradual distortions, we develop FUSE-EVAL, a comprehensive evaluation framework measuring truth deviation along multiple linguistic and semantic dimensions. Results show that FUSE effectively captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations. Experiments demonstrate that FUSE accurately reproduces known fake news evolution scenarios, aligns closely with human judgment, and highlights the importance of timely intervention at early stages. Our framework is extensible, enabling future research on broader scenarios of fake news.", "date": "2024-10-24", "categories": []}
{"id": "2505.21604", "title": "Public Discourse Sandbox: Facilitating Human and AI Digital Communication Research", "abstract": "Social media serves as a primary communication and information dissemination platform for major global events, entertainment, and niche or topically focused community discussions. Therefore, it represents a valuable resource for researchers who aim to understand numerous questions. However, obtaining data can be difficult, expensive, and often unreliable due to the presence of bots, fake accounts, and manipulated content. Additionally, there are ethical concerns if researchers decide to conduct an online experiment without explicitly notifying social media users about their intent. There is a need for more controlled and scalable mechanisms to evaluate the impacts of digital discussion interventions on audiences. We introduce the Public Discourse Sandbox (PDS), which serves as a digital discourse research platform for human-AI as well as AI-AI discourse research, testing, and training. PDS provides a safe and secure space for research experiments that are not viable on public, commercial social media platforms. Its main purpose is to enable the understanding of AI behaviors and the impacts of customized AI participants via techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning. We provide a hosted live version of the sandbox to support researchers as well as the open-sourced code on GitHub for community collaboration and contribution.", "date": "2025-05-27", "categories": []}
{"id": "2502.14132", "title": "Can Community Notes Replace Professional Fact-Checkers?", "abstract": "Two commonly employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towards an increased reliance on crowdsourced community notes. However, the extent and nature of dependencies between fact-checking and helpful community notes remain unclear. To address these questions, we use language models to annotate a large corpus of Twitter/X community notes with attributes such as topic, cited sources, and whether they refute claims tied to broader misinformation narratives. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported. Fact-checking is especially crucial for notes on posts linked to broader narratives, which are twice as likely to reference fact-checking sources compared to other sources. Our results show that successful community moderation relies on professional fact-checking and highlight how citizen and professional fact-checking are deeply intertwined.", "date": "2025-02-19", "categories": []}
{"id": "2505.19515", "title": "Analyzing Biases in Political Dialogue: Tagging U.S. Presidential Debates with an Extended DAMSL Framework", "abstract": "We present a critical discourse analysis of the 2024 U.S. presidential debates, examining Donald Trump's rhetorical strategies in his interactions with Joe Biden and Kamala Harris. We introduce a novel annotation framework, BEADS (Bias Enriched Annotation for Dialogue Structure), which systematically extends the DAMSL framework to capture bias driven and adversarial discourse features in political communication. BEADS includes a domain and language agnostic set of tags that model ideological framing, emotional appeals, and confrontational tactics. Our methodology compares detailed human annotation with zero shot ChatGPT assisted tagging on verified transcripts from the Trump and Biden (19,219 words) and Trump and Harris (18,123 words) debates. Our analysis shows that Trump consistently dominated in key categories: Challenge and Adversarial Exchanges, Selective Emphasis, Appeal to Fear, Political Bias, and Perceived Dismissiveness. These findings underscore his use of emotionally charged and adversarial rhetoric to control the narrative and influence audience perception. In this work, we establish BEADS as a scalable and reproducible framework for critical discourse analysis across languages, domains, and political contexts.", "date": "2025-05-26", "categories": []}
{"id": "2309.12646", "title": "The More Similar, the Better? Associations between Latent Semantic Similarity and Emotional Experiences Differ across Conversation Contexts", "abstract": "Latent semantic similarity (LSS) is a measure of the similarity of information exchanges in a conversation. Challenging the assumption that higher LSS bears more positive psychological meaning, we propose that this association might depend on the type of conversation people have. On the one hand, the share-mind perspective would predict that higher LSS should be associated with more positive emotional experiences across the board. The broaden-and-build theory, on the other hand, would predict that higher LSS should be inversely associated with more positive emotional experiences specifically in pleasant conversations. Linear mixed modeling based on conversations among 50 long-term married couples supported the latter prediction. That is, partners experienced greater positive emotions when their overall information exchanges were more dissimilar in pleasant (but not conflict) conversations. This work highlights the importance of context in understanding the emotional correlates of LSS and exemplifies how modern natural language processing tools can be used to evaluate competing theory-driven hypotheses in social psychology.", "date": "2023-09-22", "categories": []}
{"id": "2406.16833", "title": "USDC: A Dataset of $\\underline{U}$ser $\\underline{S}$tance and $\\underline{D}$ogmatism in Long $\\underline{C}$onversations", "abstract": "Analyzing user opinion changes in long conversation threads is extremely critical for applications like enhanced personalization, market research, political campaigns, customer service, targeted advertising, and content moderation. Unfortunately, previous studies on stance and dogmatism in user conversations have focused on training models using datasets annotated at the post level, treating each post as independent and randomly sampling posts from conversation threads. Hence, first, we build a dataset for studying user opinion fluctuations in 764 long multi-user Reddit conversation threads, called USDC. USDC contains annotations for 2 tasks: i) User Stance classification, which involves labeling a user's stance in a post within a conversation on a five-point scale; ii) User Dogmatism classification, which involves labeling a user's overall opinion in the conversation on a four-point scale. Besides being time-consuming and costly, manual annotations for USDC are challenging because: 1) Conversation threads could be very long, increasing the chances of noisy annotations; and 2) Interpreting instances where a user changes their opinion within a conversation is difficult because often such transitions are subtle and not expressed explicitly. Hence, we leverage majority voting on zero-shot, one-shot, and few-shot annotations from Mistral Large and GPT-4 to automate the annotation process. Human annotations on 200 test conversations achieved inter-annotator agreement scores of 0.49 for stance and 0.50 for dogmatism with these LLM annotations, indicating a reasonable level of consistency between human and LLM annotations. USDC is then used to finetune and instruction-tune multiple deployable small language models like LLaMA, Falcon and Vicuna for the stance and dogmatism classification tasks. We make the code and dataset publicly available [https://github.com/mounikamarreddy/USDC].", "date": "2024-06-24", "categories": []}
{"id": "2505.19355", "title": "Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement", "abstract": "Understanding true influence in social media requires distinguishing correlation from causation--particularly when analyzing misinformation spread. While existing approaches focus on exposure metrics and network structures, they often fail to capture the causal mechanisms by which external temporal signals trigger engagement. We introduce a novel joint treatment-outcome framework that leverages existing sequential models to simultaneously adapt to both policy timing and engagement effects. Our approach adapts causal inference techniques from healthcare to estimate Average Treatment Effects (ATE) within the sequential nature of social media interactions, tackling challenges from external confounding signals. Through our experiments on real-world misinformation and disinformation datasets, we show that our models outperform existing benchmarks by 15--22% in predicting engagement across diverse counterfactual scenarios, including exposure adjustment, timing shifts, and varied intervention durations. Case studies on 492 social media users show our causal effect measure aligns strongly with the gold standard in influence estimation, the expert-based empirical influence.", "date": "2025-05-25", "categories": []}
{"id": "2505.20067", "title": "Community Moderation and the New Epistemology of Fact Checking on Social Media", "abstract": "Social media platforms have traditionally relied on internal moderation teams and partnerships with independent fact-checking organizations to identify and flag misleading content. Recently, however, platforms including X (formerly Twitter) and Meta have shifted towards community-driven content moderation by launching their own versions of crowd-sourced fact-checking -- Community Notes. If effectively scaled and governed, such crowd-checking initiatives have the potential to combat misinformation with increased scale and speed as successfully as community-driven efforts once did with spam. Nevertheless, general content moderation, especially for misinformation, is inherently more complex. Public perceptions of truth are often shaped by personal biases, political leanings, and cultural contexts, complicating consensus on what constitutes misleading content. This suggests that community efforts, while valuable, cannot replace the indispensable role of professional fact-checkers. Here we systemically examine the current approaches to misinformation detection across major platforms, explore the emerging role of community-driven moderation, and critically evaluate both the promises and challenges of crowd-checking at scale.", "date": "2025-05-26", "categories": []}
{"id": "2208.00904", "title": "Revisiting Information Cascades in Online Social Networks", "abstract": "It's by now folklore that to understand the activity pattern of a user in an online social network (OSN) platform, one needs to look at his friends or the ones he follows. The common perception is that these friends exert influence on the user, effecting his decision whether to re-share content or not. Hinging upon this intuition, a variety of models were developed to predict how information propagates in OSN, similar to the way infection spreads in the population. In this paper, we revisit this world view and arrive at new conclusions. Given a set of users $V$, we study the task of predicting whether a user $u \\in V$ will re-share content by some $v \\in V$ at the following time window given the activity of all the users in $V$ in the previous time window. We design several algorithms for this task, ranging from a simple greedy algorithm that only learns $u$'s conditional probability distribution, ignoring the rest of $V$, to a convolutional neural network-based algorithm that receives the activity of all of $V$, but does not receive explicitly the social link structure. We tested our algorithms on four datasets that we collected from Twitter, each revolving around a different popular topic in 2020. The best performance, average F1-score of 0.86 over the four datasets, was achieved by the convolutional neural network. The simple, social-link ignorant, algorithm achieved an average F1-score of 0.78.", "date": "2022-08-01", "categories": []}
{"id": "2505.12158", "title": "The AI Gap: How Socioeconomic Status Affects Language Technology Interactions", "abstract": "Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.", "date": "2025-05-17", "categories": []}
{"id": "2505.17511", "title": "Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification", "abstract": "The rapid proliferation of misinformation in digital media demands solutions that go beyond isolated Large Language Model(LLM) or AI Agent based detection methods. This paper introduces a novel multi-agent framework that covers the complete misinformation lifecycle: classification, detection, correction, and source verification to deliver more transparent and reliable outcomes. In contrast to single-agent or monolithic architectures, our approach employs five specialized agents: an Indexer agent for dynamically maintaining trusted repositories, a Classifier agent for labeling misinformation types, an Extractor agent for evidence based retrieval and ranking, a Corrector agent for generating fact-based correction and a Verification agent for validating outputs and tracking source credibility. Each agent can be individually evaluated and optimized, ensuring scalability and adaptability as new types of misinformation and data sources emerge. By decomposing the misinformation lifecycle into specialized agents - our framework enhances scalability, modularity, and explainability. This paper proposes a high-level system overview, agent design with emphasis on transparency, evidence-based outputs, and source provenance to support robust misinformation detection and correction at scale.", "date": "2025-05-23", "categories": []}
{"id": "2505.15834", "title": "MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation", "abstract": "Fake news spreads widely on social media, leading to numerous negative effects. Most existing detection algorithms focus on analyzing news content and social context to detect fake news. However, these approaches typically detect fake news based on specific platforms, ignoring differences in propagation characteristics across platforms. In this paper, we introduce the MPPFND dataset, which captures propagation structures across multiple platforms. We also describe the commenting and propagation characteristics of different platforms to show that their social contexts have distinct features. We propose a multi-platform fake news detection model (APSL) that uses graph neural networks to extract social context features from various platforms. Experiments show that accounting for cross-platform propagation differences improves fake news detection performance.", "date": "2025-05-16", "categories": []}
{"id": "2505.16118", "title": "Semiotic Reconstruction of Destination Expectation Constructs An LLM-Driven Computational Paradigm for Social Media Tourism Analytics", "abstract": "Social media's rise establishes user-generated content (UGC) as pivotal for travel decisions, yet analytical methods lack scalability. This study introduces a dual-method LLM framework: unsupervised expectation extraction from UGC paired with survey-informed supervised fine-tuning. Findings reveal leisure/social expectations drive engagement more than foundational natural/emotional factors. By establishing LLMs as precision tools for expectation quantification, we advance tourism analytics methodology and propose targeted strategies for experience personalization and social travel promotion. The framework's adaptability extends to consumer behavior research, demonstrating computational social science's transformative potential in marketing optimization.", "date": "2025-05-22", "categories": []}
{"id": "2406.10965", "title": "DocNet: Semantic Structure in Inductive Bias Detection Models", "abstract": "News will be biased so long as people have opinions. As social media becomes the primary entry point for news and partisan differences increase, it is increasingly important for informed citizens to be able to recognize bias. If people are aware of the biases of the news they consume, they will be able to take action to avoid polarizing echo chambers. In this paper, we explore an often overlooked aspect of bias detection in media: the semantic structure of news articles. We present DocNet, a novel, inductive, and low-resource document embedding and political bias detection model. We also demonstrate that the semantic structure of news articles from opposing political sides, as represented in document-level graph embeddings, have significant similarities. DocNet bypasses the need for pre-trained language models, reducing resource dependency while achieving comparable performance. It can be used to advance political bias detection in low-resource environments. Our code and data are made available at: https://anonymous.4open.science/r/DocNet/", "date": "2024-06-16", "categories": []}
{"id": "2505.15563", "title": "Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis", "abstract": "This research presents a novel approach to computational framing analysis, called Semantic Relations-based Unsupervised Framing Analysis (SUFA). SUFA leverages semantic relations and dependency parsing algorithms to identify and assess entity-centric emphasis frames in news media reports. This innovative method is derived from two studies -- qualitative and computational -- using a dataset related to gun violence, demonstrating its potential for analyzing entity-centric emphasis frames. This article discusses SUFA's strengths, limitations, and application procedures. Overall, the SUFA approach offers a significant methodological advancement in computational framing analysis, with its broad applicability across both the social sciences and computational domains.", "date": "2025-05-21", "categories": []}
{"id": "2505.12535", "title": "Framework of Voting Prediction of Parliament Members", "abstract": "Keeping track of how lawmakers vote is essential for government transparency. While many parliamentary voting records are available online, they are often difficult to interpret, making it challenging to understand legislative behavior across parliaments and predict voting outcomes. Accurate prediction of votes has several potential benefits, from simplifying parliamentary work by filtering out bills with a low chance of passing to refining proposed legislation to increase its likelihood of approval. In this study, we leverage advanced machine learning and data analysis techniques to develop a comprehensive framework for predicting parliamentary voting outcomes across multiple legislatures. We introduce the Voting Prediction Framework (VPF) - a data-driven framework designed to forecast parliamentary voting outcomes at the individual legislator level and for entire bills. VPF consists of three key components: (1) Data Collection - gathering parliamentary voting records from multiple countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration - processing and enriching the data with meaningful features, such as legislator seniority, and content-based characteristics of a given bill; and (3) Prediction Models - using machine learning to forecast how each parliament member will vote and whether a bill is likely to pass. The framework will be open source, enabling anyone to use or modify the framework. To evaluate VPF, we analyzed over 5 million voting records from five countries - Canada, Israel, Tunisia, the United Kingdom and the USA. Our results show that VPF achieves up to 85% precision in predicting individual votes and up to 84% accuracy in predicting overall bill outcomes. These findings highlight VPF's potential as a valuable tool for political analysis, policy research, and enhancing public access to legislative decision-making.", "date": "2025-05-18", "categories": []}
{"id": "2505.13334", "title": "Measuring Social Influence with Networked Synthetic Control", "abstract": "Measuring social influence is difficult due to the lack of counter-factuals and comparisons. By combining machine learning-based modeling and network science, we present general properties of social value, a recent measure for social influence using synthetic control applicable to political behavior. Social value diverges from centrality measures on in that it relies on an external regressor to predict an output variable of interest, generates a synthetic measure of influence, then distributes individual contribution based on a social network. Through theoretical derivations, we show the properties of SV under linear regression with and without interaction, across lattice networks, power-law networks, and random graphs. A reduction in computation can be achieved for any ensemble model. Through simulation, we find that the generalized friendship paradox holds -- that in certain situations, your friends have on average more influence than you do.", "date": "2025-05-19", "categories": []}
{"id": "2409.20204", "title": "Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach", "abstract": "Several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, particularly on online platforms. These tools draw on insights from both social science and computer science. Given the increasing concern over gender-based discrimination in digital spaces, the contested definitions and measurements of sexism, and the rise of interdisciplinary efforts to understand its online manifestations, a systematic literature review is essential for capturing the current state and trajectory of this evolving field. In this review, we make four key contributions: (1) we synthesize the literature into five core themes: definitions of sexism and misogyny, disciplinary divergences, automated detection methods, associated challenges, and design-based interventions; (2) we adopt an interdisciplinary lens, bridging theoretical and methodological divides across disciplines; (3) we highlight critical gaps, including the need for intersectional approaches, the under-representation of non-Western languages and perspectives, and the limited focus on proactive design strategies beyond text classification; and (4) we offer a methodological contribution by applying a rigorous semi-automated systematic review process guided by PRISMA, establishing a replicable standard for future work in this domain. Our findings reveal a clear disciplinary divide in how sexism and misogyny are conceptualized and measured. Through an evidence-based synthesis, we examine how existing studies have attempted to bridge this gap through interdisciplinary collaboration. Drawing on both social science theories and computational modeling practices, we assess the strengths and limitations of current methodologies. Finally, we outline key challenges and future directions for advancing research on the detection and mitigation of online sexism and misogyny.", "date": "2024-09-30", "categories": []}
{"id": "2505.10681", "title": "Towards an LLM-powered Social Digital Twinning Platform", "abstract": "We present Social Digital Twinner, an innovative social simulation tool for exploring plausible effects of what-if scenarios in complex adaptive social systems. The architecture is composed of three seamlessly integrated parts: a data infrastructure featuring real-world data and a multi-dimensionally representative synthetic population of citizens, an LLM-enabled agent-based simulation engine, and a user interface that enable intuitive, natural language interactions with the simulation engine and the artificial agents (i.e. citizens). Social Digital Twinner facilitates real-time engagement and empowers stakeholders to collaboratively design, test, and refine intervention measures. The approach is promoting a data-driven and evidence-based approach to societal problem-solving. We demonstrate the tool's interactive capabilities by addressing the critical issue of youth school dropouts in Kragero, Norway, showcasing its ability to create and execute a dedicated social digital twin using natural language.", "date": "2025-05-15", "categories": []}
{"id": "2505.10746", "title": "ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems", "abstract": "Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Yet, existing detection and mitigation strategies often fail to identify active information campaigns in real time. This paper introduces ChestyBot, a pragmatics-based language model that detects unlabeled foreign malign influence tweets with up to 98.34% accuracy. The model supports a novel framework to disrupt foreign influence operations in their formative stages.", "date": "2025-05-15", "categories": []}
{"id": "2505.07874", "title": "The Sound of Populism: Distinct Linguistic Features Across Populist Variants", "abstract": "This study explores the sound of populism by integrating the classic Linguistic Inquiry and Word Count (LIWC) features, which capture the emotional and stylistic tones of language, with a fine-tuned RoBERTa model, a state-of-the-art context-aware language model trained to detect nuanced expressions of populism. This approach allows us to uncover the auditory dimensions of political rhetoric in U.S. presidential inaugural and State of the Union addresses. We examine how four key populist dimensions (i.e., left-wing, right-wing, anti-elitism, and people-centrism) manifest in the linguistic markers of speech, drawing attention to both commonalities and distinct tonal shifts across these variants. Our findings reveal that populist rhetoric consistently features a direct, assertive ``sound\" that forges a connection with ``the people'' and constructs a charismatic leadership persona. However, this sound is not simply informal but strategically calibrated. Notably, right-wing populism and people-centrism exhibit a more emotionally charged discourse, resonating with themes of identity, grievance, and crisis, in contrast to the relatively restrained emotional tones of left-wing and anti-elitist expressions.", "date": "2025-05-10", "categories": []}
{"id": "2505.08706", "title": "Big Data and the Computational Social Science of Entrepreneurship and Innovation", "abstract": "As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This chapter discusses the difficulties of leveraging large-scale data to identify technological and commercial novelty, document new venture origins, and forecast competition between new technologies and commercial forms. It suggests how scholars can take advantage of new text, network, image, audio, and video data in two distinct ways that advance innovation and entrepreneurship research. First, machine-learning models, combined with large-scale data, enable the construction of precision measurements that function as system-level observatories of innovation and entrepreneurship across human societies. Second, new artificial intelligence models fueled by big data generate 'digital doubles' of technology and business, forming laboratories for virtual experimentation about innovation and entrepreneurship processes and policies. The chapter argues for the advancement of theory development and testing in entrepreneurship and innovation by coupling big data with big models.", "date": "2025-05-13", "categories": []}
{"id": "2505.06313", "title": "AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity", "abstract": "The paper considers the use of GPT models with retrieval-augmented generation (RAG) for qualitative and quantitative analytics on NATO sentiments, NATO unity and NATO Article 5 trust opinion scores in different web sources: news sites found via Google Search API, Youtube videos with comments, and Reddit discussions. A RAG approach using GPT-4.1 model was applied to analyse news where NATO related topics were discussed. Two levels of RAG analytics were used: on the first level, the GPT model generates qualitative news summaries and quantitative opinion scores using zero-shot prompts; on the second level, the GPT model generates the summary of news summaries. Quantitative news opinion scores generated by the GPT model were analysed using Bayesian regression to get trend lines. The distributions found for the regression parameters make it possible to analyse an uncertainty in specified news opinion score trends. Obtained results show a downward trend for analysed scores of opinion related to NATO unity.\n  This approach does not aim to conduct real political analysis; rather, it consider AI based approaches which can be used for further analytics\n  as a part of a complex analytical approach. The obtained results demonstrate that the use of GPT models for news analysis can give informative qualitative and quantitative analytics, providing important insights.\n  The dynamic model based on neural ordinary differential equations was considered for modelling public opinions. This approach makes it possible to analyse different scenarios for evolving public opinions.", "date": "2025-05-08", "categories": []}
{"id": "2505.05687", "title": "Exploration of COVID-19 Discourse on Twitter: American Politician Edition", "abstract": "The advent of the COVID-19 pandemic has undoubtedly affected the political scene worldwide and the introduction of new terminology and public opinions regarding the virus has further polarized partisan stances. Using a collection of tweets gathered from leading American political figures online (Republican and Democratic), we explored the partisan differences in approach, response, and attitude towards handling the international crisis. Implementation of the bag-of-words, bigram, and TF-IDF models was used to identify and analyze keywords, topics, and overall sentiments from each party. Results suggest that Democrats are more concerned with the casualties of the pandemic, and give more medical precautions and recommendations to the public whereas Republicans are more invested in political responsibilities such as keeping the public updated through media and carefully watching the progress of the virus. We propose a systematic approach to predict and distinguish a tweet's political stance (left or right leaning) based on its COVID-19 related terms using different classification algorithms on different language models.", "date": "2025-05-08", "categories": []}
{"id": "2505.06107", "title": "Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models", "abstract": "Most web and digital trace data do not include information about an individual's nationality due to privacy concerns. The lack of data on nationality can create challenges for migration research. It can lead to a left-censoring issue since we are uncertain about the migrant's country of origin. Once we observe an emigration event, if we know the nationality, we can differentiate it from return migration. We propose methods to detect the nationality with the least available data, i.e., full names. We use the detected nationality in comparison with the country of academic origin, which is a common approach in studying the migration of researchers. We gathered 2.6 million unique name-nationality pairs from Wikipedia and categorized them into families of nationalities with three granularity levels to use as our training data. Using a character-based machine learning model, we achieved a weighted F1 score of 84% for the broadest and 67% for the most granular, country-level categorization. In our empirical study, we used the trained and tested model to assign nationality to 8+ million scholars' full names in Scopus data. Our results show that using the country of first publication as a proxy for nationality underestimates the size of return flows, especially for countries with a more diverse academic workforce, such as the USA, Australia, and Canada. We found that around 48% of emigration from the USA was return migration once we used the country of name origin, in contrast to 33% based on academic origin. In the most recent period, 79% of scholars whose affiliation has consistently changed from the USA to China, and are considered emigrants, have Chinese names in contrast to 41% with a Chinese academic origin. Our proposed methods for addressing left-censoring issues are beneficial for other research that uses digital trace data to study migration.", "date": "2025-05-09", "categories": []}
{"id": "2505.05459", "title": "UKElectionNarratives: A Dataset of Misleading Narratives Surrounding\n  Recent UK General Elections", "abstract": "  Misleading narratives play a crucial role in shaping public opinion during\nelections, as they can influence how voters perceive candidates and political\nparties. This entails the need to detect these narratives accurately. To\naddress this, we introduce the first taxonomy of common misleading narratives\nthat circulated during recent elections in Europe. Based on this taxonomy, we\nconstruct and analyse UKElectionNarratives: the first dataset of\nhuman-annotated misleading narratives which circulated during the UK General\nElections in 2019 and 2024. We also benchmark Pre-trained and Large Language\nModels (focusing on GPT-4o), studying their effectiveness in detecting\nelection-related misleading narratives. Finally, we discuss potential use cases\nand make recommendations for future research directions using the proposed\ncodebook and dataset.\n", "date": "2025-05-08", "categories": []}
{"id": "2505.03769", "title": "The Influence of Text Variation on User Engagement in Cross-Platform\n  Content Sharing", "abstract": "  In today's cross-platform social media landscape, understanding factors that\ndrive engagement for multimodal content, especially text paired with visuals,\nremains complex. This study investigates how rewriting Reddit post titles\nadapted from YouTube video titles affects user engagement. First, we build and\nanalyze a large dataset of Reddit posts sharing YouTube videos, revealing that\n21% of post titles are minimally modified. Statistical analysis demonstrates\nthat title rewrites measurably improve engagement. Second, we design a\ncontrolled, multi-phase experiment to rigorously isolate the effects of textual\nvariations by neutralizing confounding factors like video popularity, timing,\nand community norms. Comprehensive statistical tests reveal that effective\ntitle rewrites tend to feature emotional resonance, lexical richness, and\nalignment with community-specific norms. Lastly, pairwise ranking prediction\nexperiments using a fine-tuned BERT classifier achieves 74% accuracy,\nsignificantly outperforming near-random baselines, including GPT-4o. These\nresults validate that our controlled dataset effectively minimizes confounding\neffects, allowing advanced models to both learn and demonstrate the impact of\ntextual features on engagement. By bridging quantitative rigor with qualitative\ninsights, this study uncovers engagement dynamics and offers a robust framework\nfor future cross-platform, multimodal content strategies.\n", "date": "2025-04-26", "categories": []}
{"id": "2504.19594", "title": "Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate\n  Speech", "abstract": "  Telegram has become a major space for political discourse and alternative\nmedia. However, its lack of moderation allows misinformation, extremism, and\ntoxicity to spread. While prior research focused on these particular phenomena\nor topics, these have mostly been examined separately, and a broader\nunderstanding of the Telegram ecosystem is still missing. In this work, we fill\nthis gap by conducting a large-scale analysis of the Italian Telegram sphere,\nleveraging a dataset of 186 million messages from 13,151 chats collected in\n2023. Using network analysis, Large Language Models, and toxicity detection\ntools, we examine how different thematic communities form, align ideologically,\nand engage in harmful discourse within the Italian cultural context. Results\nshow strong thematic and ideological homophily. We also identify mixed\nideological communities where far-left and far-right rhetoric coexist on\nparticular geopolitical issues. Beyond political analysis, we find that\ntoxicity, rather than being isolated in a few extreme chats, appears widely\nnormalized within highly toxic communities. Moreover, we find that Italian\ndiscourse primarily targets Black people, Jews, and gay individuals\nindependently of the topic. Finally, we uncover common trend of intra-national\nhostility, where Italians often attack other Italians, reflecting regional and\nintra-regional cultural conflicts that can be traced back to old historical\ndivisions. This study provides the first large-scale mapping of the Italian\nTelegram ecosystem, offering insights into ideological interactions, toxicity,\nand identity-targets of hate and contributing to research on online toxicity\nacross different cultural and linguistic contexts on Telegram.\n", "date": "2025-04-28", "categories": []}
{"id": "2505.01754", "title": "Unraveling Media Perspectives: A Comprehensive Methodology Combining\n  Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology\n  Learning to Analyse Media Bias", "abstract": "  Biased news reporting poses a significant threat to informed decision-making\nand the functioning of democracies. This study introduces a novel methodology\nfor scalable, minimally biased analysis of media bias in political news. The\nproposed approach examines event selection, labeling, word choice, and\ncommission and omission biases across news sources by leveraging natural\nlanguage processing techniques, including hierarchical topic modeling,\nsentiment analysis, and ontology learning with large language models. Through\nthree case studies related to current political events, we demonstrate the\nmethodology's effectiveness in identifying biases across news sources at\nvarious levels of granularity. This work represents a significant step towards\nscalable, minimally biased media bias analysis, laying the groundwork for tools\nto help news consumers navigate an increasingly complex media landscape.\n", "date": "2025-05-03", "categories": []}
{"id": "2505.00006", "title": "Toward a digital twin of U.S. Congress", "abstract": "  In this paper we provide evidence that a virtual model of U.S.\ncongresspersons based on a collection of language models satisfies the\ndefinition of a digital twin. In particular, we introduce and provide\nhigh-level descriptions of a daily-updated dataset that contains every Tweet\nfrom every U.S. congressperson during their respective terms. We demonstrate\nthat a modern language model equipped with congressperson-specific subsets of\nthis data are capable of producing Tweets that are largely indistinguishable\nfrom actual Tweets posted by their physical counterparts. We illustrate how\ngenerated Tweets can be used to predict roll-call vote behaviors and to\nquantify the likelihood of congresspersons crossing party lines, thereby\nassisting stakeholders in allocating resources and potentially impacting\nreal-world legislative dynamics. We conclude with a discussion of the\nlimitations and important extensions of our analysis.\n", "date": "2025-04-04", "categories": []}
{"id": "2504.21604", "title": "Robust Misinformation Detection by Visiting Potential Commonsense\n  Conflict", "abstract": "  The development of Internet technology has led to an increased prevalence of\nmisinformation, causing severe negative effects across diverse domains. To\nmitigate this challenge, Misinformation Detection (MD), aiming to detect online\nmisinformation automatically, emerges as a rapidly growing research topic in\nthe community. In this paper, we propose a novel plug-and-play augmentation\nmethod for the MD task, namely Misinformation Detection with Potential\nCommonsense Conflict (MD-PCC). We take inspiration from the prior studies\nindicating that fake articles are more likely to involve commonsense conflict.\nAccordingly, we construct commonsense expressions for articles, serving to\nexpress potential commonsense conflicts inferred by the difference between\nextracted commonsense triplet and golden ones inferred by the well-established\ncommonsense reasoning tool COMET. These expressions are then specified for each\narticle as augmentation. Any specific MD methods can be then trained on those\ncommonsense-augmented articles. Besides, we also collect a novel\ncommonsense-oriented dataset named CoMis, whose all fake articles are caused by\ncommonsense conflict. We integrate MD-PCC with various existing MD backbones\nand compare them across both 4 public benchmark datasets and CoMis. Empirical\nresults demonstrate that MD-PCC can consistently outperform the existing MD\nbaselines.\n", "date": "2025-04-30", "categories": []}
{"id": "2504.20469", "title": "Fane at SemEval-2025 Task 10: Zero-Shot Entity Framing with Large\n  Language Models", "abstract": "  Understanding how news narratives frame entities is crucial for studying\nmedia's impact on societal perceptions of events. In this paper, we evaluate\nthe zero-shot capabilities of large language models (LLMs) in classifying\nframing roles. Through systematic experimentation, we assess the effects of\ninput context, prompting strategies, and task decomposition. Our findings show\nthat a hierarchical approach of first identifying broad roles and then\nfine-grained roles, outperforms single-step classification. We also demonstrate\nthat optimal input contexts and prompts vary across task levels, highlighting\nthe need for subtask-specific strategies. We achieve a Main Role Accuracy of\n89.4% and an Exact Match Ratio of 34.5%, demonstrating the effectiveness of our\napproach. Our findings emphasize the importance of tailored prompt design and\ninput context optimization for improving LLM performance in entity framing.\n", "date": "2025-04-29", "categories": []}
{"id": "2405.13056", "title": "Large language models for newspaper sentiment analysis during COVID-19:\n  The Guardian", "abstract": "  During the COVID-19 pandemic, the news media coverage encompassed a wide\nrange of topics that includes viral transmission, allocation of medical\nresources, and government response measures. There have been studies on\nsentiment analysis of social media platforms during COVID-19 to understand the\npublic response given the rise of cases and government strategies implemented\nto control the spread of the virus. Sentiment analysis can provide a better\nunderstanding of changes in societal opinions and emotional trends during the\npandemic. Apart from social media, newspapers have played a vital role in the\ndissemination of information, including information from the government,\nexperts, and also the public about various topics. A study of sentiment\nanalysis of newspaper sources during COVID-19 for selected countries can give\nan overview of how the media covered the pandemic. In this study, we select The\nGuardian newspaper and provide a sentiment analysis during various stages of\nCOVID-19 that includes initial transmission, lockdowns and vaccination. We\nemploy novel large language models (LLMs) and refine them with expert-labelled\nsentiment analysis data. We also provide an analysis of sentiments experienced\npre-pandemic for comparison. The results indicate that during the early\npandemic stages, public sentiment prioritised urgent crisis response, later\nshifting focus to addressing the impact on health and the economy. In\ncomparison with related studies about social media sentiment analyses, we found\na discrepancy between The Guardian with dominance of negative sentiments (sad,\nannoyed, anxious and denial), suggesting that social media offers a more\ndiversified emotional reflection. We found a grim narrative in The Guardian\nwith overall dominance of negative sentiments, pre and during COVID-19 across\nnews sections including Australia, UK, World News, and Opinion\n", "date": "2024-05-20", "categories": []}
{"id": "2407.08563", "title": "Vox Populi, Vox AI? Using Language Models to Estimate German Public\n  Opinion", "abstract": "  The recent development of large language models (LLMs) has spurred\ndiscussions about whether LLM-generated \"synthetic samples\" could complement or\nreplace traditional surveys, considering their training data potentially\nreflects attitudes and behaviors prevalent in the population. A number of\nmostly US-based studies have prompted LLMs to mimic survey respondents, with\nsome of them finding that the responses closely match the survey data. However,\nseveral contextual factors related to the relationship between the respective\ntarget population and LLM training data might affect the generalizability of\nsuch findings. In this study, we investigate the extent to which LLMs can\nestimate public opinion in Germany, using the example of vote choice. We\ngenerate a synthetic sample of personas matching the individual characteristics\nof the 2017 German Longitudinal Election Study respondents. We ask the LLM\nGPT-3.5 to predict each respondent's vote choice and compare these predictions\nto the survey-based estimates on the aggregate and subgroup levels. We find\nthat GPT-3.5 does not predict citizens' vote choice accurately, exhibiting a\nbias towards the Green and Left parties. While the LLM captures the tendencies\nof \"typical\" voter subgroups, such as partisans, it misses the multifaceted\nfactors swaying individual voter choices. By examining the LLM-based prediction\nof voting behavior in a new context, our study contributes to the growing body\nof research about the conditions under which LLMs can be leveraged for studying\npublic opinion. The findings point to disparities in opinion representation in\nLLMs and underscore the limitations in applying them for public opinion\nestimation.\n", "date": "2024-07-11", "categories": []}
{"id": "2504.19556", "title": "Detecting Effects of AI-Mediated Communication on Language Complexity\n  and Sentiment", "abstract": "  Given the subtle human-like effects of large language models on linguistic\npatterns, this study examines shifts in language over time to detect the impact\nof AI-mediated communication (AI- MC) on social media. We compare a replicated\ndataset of 970,919 tweets from 2020 (pre-ChatGPT) with 20,000 tweets from the\nsame period in 2024, all of which mention Donald Trump during election periods.\nUsing a combination of Flesch-Kincaid readability and polarity scores, we\nanalyze changes in text complexity and sentiment. Our findings reveal a\nsignificant increase in mean sentiment polarity (0.12 vs. 0.04) and a shift\nfrom predominantly neutral content (54.8% in 2020 to 39.8% in 2024) to more\npositive expressions (28.6% to 45.9%). These findings suggest not only an\nincreasing presence of AI in social media communication but also its impact on\nlanguage and emotional expression patterns.\n", "date": "2025-04-28", "categories": []}
{"id": "2504.16947", "title": "SCRAG: Social Computing-Based Retrieval Augmented Generation for\n  Community Response Forecasting in Social Media Environments", "abstract": "  This paper introduces SCRAG, a prediction framework inspired by social\ncomputing, designed to forecast community responses to real or hypothetical\nsocial media posts. SCRAG can be used by public relations specialists (e.g., to\ncraft messaging in ways that avoid unintended misinterpretations) or public\nfigures and influencers (e.g., to anticipate social responses), among other\napplications related to public sentiment prediction, crisis management, and\nsocial what-if analysis. While large language models (LLMs) have achieved\nremarkable success in generating coherent and contextually rich text, their\nreliance on static training data and susceptibility to hallucinations limit\ntheir effectiveness at response forecasting in dynamic social media\nenvironments. SCRAG overcomes these challenges by integrating LLMs with a\nRetrieval-Augmented Generation (RAG) technique rooted in social computing.\nSpecifically, our framework retrieves (i) historical responses from the target\ncommunity to capture their ideological, semantic, and emotional makeup, and\n(ii) external knowledge from sources such as news articles to inject\ntime-sensitive context. This information is then jointly used to forecast the\nresponses of the target community to new posts or narratives. Extensive\nexperiments across six scenarios on the X platform (formerly Twitter), tested\nwith various embedding models and LLMs, demonstrate over 10% improvements on\naverage in key evaluation metrics. A concrete example further shows its\neffectiveness in capturing diverse ideologies and nuances. Our work provides a\nsocial computing tool for applications where accurate and concrete insights\ninto community responses are crucial.\n", "date": "2025-04-18", "categories": []}
{"id": "2504.16122", "title": "SOTOPIA-S4: a user-friendly system for flexible, customizable, and\n  large-scale social simulation", "abstract": "  Social simulation through large language model (LLM) agents is a promising\napproach to explore and validate hypotheses related to social science questions\nand LLM agents behavior. We present SOTOPIA-S4, a fast, flexible, and scalable\nsocial simulation system that addresses the technical barriers of current\nframeworks while enabling practitioners to generate multi-turn and multi-party\nLLM-based interactions with customizable evaluation metrics for hypothesis\ntesting. SOTOPIA-S4 comes as a pip package that contains a simulation engine,\nan API server with flexible RESTful APIs for simulation management, and a web\ninterface that enables both technical and non-technical users to design, run,\nand analyze simulations without programming. We demonstrate the usefulness of\nSOTOPIA-S4 with two use cases involving dyadic hiring negotiation and\nmulti-party planning scenarios.\n", "date": "2025-04-19", "categories": []}
{"id": "2504.09865", "title": "Labeling Messages as AI-Generated Does Not Reduce Their Persuasive\n  Effects", "abstract": "  As generative artificial intelligence (AI) enables the creation and\ndissemination of information at massive scale and speed, it is increasingly\nimportant to understand how people perceive AI-generated content. One prominent\npolicy proposal requires explicitly labeling AI-generated content to increase\ntransparency and encourage critical thinking about the information, but prior\nresearch has not yet tested the effects of such labels. To address this gap, we\nconducted a survey experiment (N=1601) on a diverse sample of Americans,\npresenting participants with an AI-generated message about several public\npolicies (e.g., allowing colleges to pay student-athletes), randomly assigning\nwhether participants were told the message was generated by (a) an expert AI\nmodel, (b) a human policy expert, or (c) no label. We found that messages were\ngenerally persuasive, influencing participants' views of the policies by 9.74\npercentage points on average. However, while 94.6% of participants assigned to\nthe AI and human label conditions believed the authorship labels, labels had no\nsignificant effects on participants' attitude change toward the policies,\njudgments of message accuracy, nor intentions to share the message with others.\nThese patterns were robust across a variety of participant characteristics,\nincluding prior knowledge of the policy, prior experience with AI, political\nparty, education level, or age. Taken together, these results imply that, while\nauthorship labels would likely enhance transparency, they are unlikely to\nsubstantially affect the persuasiveness of the labeled content, highlighting\nthe need for alternative strategies to address challenges posed by AI-generated\ninformation.\n", "date": "2025-04-14", "categories": []}
{"id": "2404.11988", "title": "The Emerging Generative Artificial Intelligence Divide in the United\n  States", "abstract": "  The digital divide refers to disparities in access to and use of digital\ntooling across social and economic groups. This divide can reinforce\nmarginalization both at the individual level and at the level of places,\nbecause persistent economic advantages accrue to places where new technologies\nare adopted early. To what extent are emerging generative artificial\nintelligence (AI) tools subject to these social and spatial divides? We\nleverage a large-scale search query database to characterize U.S. residents'\nknowledge of a novel generative AI tool, ChatGPT, during its first six months\nof release. We identify hotspots of higher-than-expected search volumes for\nChatGPT in coastal metropolitan areas, while coldspots are evident in the\nAmerican South, Appalachia, and the Midwest. Nationwide, counties with the\nhighest rates of search have proportionally more educated and more economically\nadvantaged populations, as well as proportionally more technology and\nfinance-sector jobs in comparison with other counties or with the national\naverage. Observed associations with race/ethnicity and urbanicity are\nattenuated in fully adjusted hierarchical models, but education emerges as the\nstrongest positive predictor of generative AI awareness. In the absence of\nintervention, early differences in uptake show a potential to reinforce\nexisting spatial and socioeconomic divides.\n", "date": "2024-04-18", "categories": []}
{"id": "2504.10662", "title": "Emotion Alignment: Discovering the Gap Between Social Media and\n  Real-World Sentiments in Persian Tweets and Images", "abstract": "  In contemporary society, widespread social media usage is evident in people's\ndaily lives. Nevertheless, disparities in emotional expressions between the\nreal world and online platforms can manifest. We comprehensively analyzed\nPersian community on X to explore this phenomenon. An innovative pipeline was\ndesigned to measure the similarity between emotions in the real world compared\nto social media. Accordingly, recent tweets and images of participants were\ngathered and analyzed using Transformers-based text and image sentiment\nanalysis modules. Each participant's friends also provided insights into the\ntheir real-world emotions. A distance criterion was used to compare real-world\nfeelings with virtual experiences. Our study encompassed N=105 participants,\n393 friends who contributed their perspectives, over 8,300 collected tweets,\nand 2,000 media images. Results indicated a 28.67% similarity between images\nand real-world emotions, while tweets exhibited a 75.88% alignment with\nreal-world feelings. Additionally, the statistical significance confirmed that\nthe observed disparities in sentiment proportions.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.14037", "title": "Uncovering Conspiratorial Narratives within Arabic Online Content", "abstract": "  This study investigates the spread of conspiracy theories in Arabic digital\nspaces through computational analysis of online content. By combining Named\nEntity Recognition and Topic Modeling techniques, specifically the Top2Vec\nalgorithm, we analyze data from Arabic blogs and Facebook to identify and\nclassify conspiratorial narratives. Our analysis uncovers six distinct\ncategories: gender/feminist, geopolitical, government cover-ups, apocalyptic,\nJudeo-Masonic, and geoengineering. The research highlights how these narratives\nare deeply embedded in Arabic social media discourse, shaped by regional\nhistorical, cultural, and sociopolitical contexts. By applying advanced Natural\nLanguage Processing methods to Arabic content, this study addresses a gap in\nconspiracy theory research, which has traditionally focused on English-language\ncontent or offline data. The findings provide new insights into the\nmanifestation and evolution of conspiracy theories in Arabic digital spaces,\nenhancing our understanding of their role in shaping public discourse in the\nArab world.\n", "date": "2025-04-18", "categories": []}
{"id": "2504.15072", "title": "Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation\n  Analysis", "abstract": "  The rapid development of social media has significantly reshaped the dynamics\nof public opinion, resulting in complex interactions that traditional models\nfail to effectively capture. To address this challenge, we propose an\ninnovative approach that integrates multi-dimensional Hawkes processes with\nGraph Neural Network, modeling opinion propagation dynamics among nodes in a\nsocial network while considering the intricate hierarchical relationships\nbetween comments. The extended multi-dimensional Hawkes process captures the\nhierarchical structure, multi-dimensional interactions, and mutual influences\nacross different topics, forming a complex propagation network. Moreover,\nrecognizing the lack of high-quality datasets capable of comprehensively\ncapturing the evolution of public opinion dynamics, we introduce a new dataset,\nVISTA. It includes 159 trending topics, corresponding to 47,207 posts, 327,015\nsecond-level comments, and 29,578 third-level comments, covering diverse\ndomains such as politics, entertainment, sports, health, and medicine. The\ndataset is annotated with detailed sentiment labels across 11 categories and\nclearly defined hierarchical relationships. When combined with our method, it\noffers strong interpretability by linking sentiment propagation to the comment\nhierarchy and temporal evolution. Our approach provides a robust baseline for\nfuture research.\n", "date": "2025-04-21", "categories": []}
{"id": "2409.09045", "title": "United in Diversity? Contextual Biases in LLM-Based Predictions of the\n  2024 European Parliament Elections", "abstract": "  \"Synthetic samples\" based on large language models (LLMs) have been argued to\nserve as efficient alternatives to surveys of humans, assuming that their\ntraining data includes information on human attitudes and behavior. However,\nLLM-synthetic samples might exhibit bias, for example due to training data and\nfine-tuning processes being unrepresentative of diverse contexts. Such biases\nrisk reinforcing existing biases in research, policymaking, and society.\nTherefore, researchers need to investigate if and under which conditions\nLLM-generated synthetic samples can be used for public opinion prediction. In\nthis study, we examine to what extent LLM-based predictions of individual\npublic opinion exhibit context-dependent biases by predicting the results of\nthe 2024 European Parliament elections. Prompting three LLMs with\nindividual-level background information of 26,000 eligible European voters, we\nask the LLMs to predict each person's voting behavior. By comparing them to the\nactual results, we show that LLM-based predictions of future voting behavior\nlargely fail, their accuracy is unequally distributed across national and\nlinguistic contexts, and they require detailed attitudinal information in the\nprompt. The findings emphasize the limited applicability of LLM-synthetic\nsamples to public opinion prediction. In investigating their contextual biases,\nthis study contributes to the understanding and mitigation of inequalities in\nthe development of LLMs and their applications in computational social science.\n", "date": "2024-08-29", "categories": []}
{"id": "2504.13085", "title": "Tackling Social Bias against the Poor: A Dataset and Taxonomy on\n  Aporophobia", "abstract": "  Eradicating poverty is the first goal in the United Nations Sustainable\nDevelopment Goals. However, aporophobia -- the societal bias against people\nliving in poverty -- constitutes a major obstacle to designing, approving and\nimplementing poverty-mitigation policies. This work presents an initial step\ntowards operationalizing the concept of aporophobia to identify and track\nharmful beliefs and discriminative actions against poor people on social media.\nIn close collaboration with non-profits and governmental organizations, we\nconduct data collection and exploration. Then we manually annotate a corpus of\nEnglish tweets from five world regions for the presence of (1) direct\nexpressions of aporophobia, and (2) statements referring to or criticizing\naporophobic views or actions of others, to comprehensively characterize the\nsocial media discourse related to bias and discrimination against the poor.\nBased on the annotated data, we devise a taxonomy of categories of aporophobic\nattitudes and actions expressed through speech on social media. Finally, we\ntrain several classifiers and identify the main challenges for automatic\ndetection of aporophobia in social networks. This work paves the way towards\nidentifying, tracking, and mitigating aporophobic views on social media at\nscale.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.12177", "title": "Mapping Controversies Using Artificial Intelligence: An Analysis of the\n  Hamas-Israel Conflict on YouTube", "abstract": "  This article analyzes the Hamas-Israel controversy through 253,925\nSpanish-language YouTube comments posted between October 2023 and January 2024,\nfollowing the October 7 attack that escalated the conflict. Adopting an\ninterdisciplinary approach, the study combines the analysis of controversies\nfrom Science and Technology Studies (STS) with advanced computational\nmethodologies, specifically Natural Language Processing (NLP) using the BERT\n(Bidirectional Encoder Representations from Transformers) model. Using this\napproach, the comments were automatically classified into seven categories,\nreflecting pro-Palestinian, pro-Israeli, anti- Palestinian, anti-Israeli\npositions, among others. The results show a predominance of pro- Palestinian\ncomments, although pro-Israeli and anti-Palestinian comments received more\n\"likes.\" This study also applies the agenda-setting theory to demonstrate how\nmedia coverage significantly influences public perception, observing a notable\nshift in public opinion, transitioning from a pro- Palestinian stance to a more\ncritical position towards Israel. This work highlights the importance of\ncombining social science perspectives with technological tools in the analysis\nof controversies, presenting a methodological innovation by integrating\ncomputational analysis with critical social theories to address complex public\nopinion phenomena and media narratives.\n", "date": "2025-04-16", "categories": []}
{"id": "2409.10168", "title": "Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube\n  Search for COVID-19 Misinformation Between the United States and South Africa", "abstract": "  Despite being an integral tool for finding health-related information online,\nYouTube has faced criticism for disseminating COVID-19 misinformation globally\nto its users. Yet, prior audit studies have predominantly investigated YouTube\nwithin the Global North contexts, often overlooking the Global South. To\naddress this gap, we conducted a comprehensive 10-day geolocation-based audit\non YouTube to compare the prevalence of COVID-19 misinformation in search\nresults between the United States (US) and South Africa (SA), the countries\nheavily affected by the pandemic in the Global North and the Global South,\nrespectively. For each country, we selected 3 geolocations and placed\nsock-puppets, or bots emulating \"real\" users, that collected search results for\n48 search queries sorted by 4 search filters for 10 days, yielding a dataset of\n915K results. We found that 31.55% of the top-10 search results contained\nCOVID-19 misinformation. Among the top-10 search results, bots in SA faced\nsignificantly more misinformative search results than their US counterparts.\nOverall, our study highlights the contrasting algorithmic behaviors of YouTube\nsearch between two countries, underscoring the need for the platform to\nregulate algorithmic behavior consistently across different regions of the\nGlobe.\n", "date": "2024-09-16", "categories": []}
{"id": "2412.02637", "title": "Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter\n  Communities", "abstract": "  In this project, we describe a method of modeling semantic leadership across\na set of communities associated with the #BlackLivesMatter movement, which has\nbeen informed by qualitative research on the structure of social media and\nBlack Twitter in particular. We describe our bespoke approaches to\ntime-binning, community clustering, and connecting communities over time, as\nwell as our adaptation of state-of-the-art approaches to semantic change\ndetection and semantic leadership induction. We find substantial evidence of\nthe leadership role of BLM activists and progressives, as well as Black\ncelebrities. We also find evidence of the sustained engagement of the\nconservative community with this discourse, suggesting an alternative\nexplanation for how we arrived at the present moment, in which \"anti-woke\" and\n\"anti-CRT\" bills are being enacted nationwide.\n", "date": "2024-12-03", "categories": []}
{"id": "2504.08755", "title": "Delving into: the quantification of Ai-generated content on the internet\n  (synthetic data)", "abstract": "  While it is increasingly evident that the internet is becoming saturated with\ncontent created by generated Ai large language models, accurately measuring the\nscale of this phenomenon has proven challenging. By analyzing the frequency of\nspecific keywords commonly used by ChatGPT, this paper demonstrates that such\nlinguistic markers can effectively be used to esti-mate the presence of\ngenerative AI content online. The findings suggest that at least 30% of text on\nactive web pages originates from AI-generated sources, with the actual\nproportion likely ap-proaching 40%. Given the implications of autophagous\nloops, this is a sobering realization.\n", "date": "2025-03-29", "categories": []}
{"id": "2410.03764", "title": "Words that Represent Peace", "abstract": "  We used data from LexisNexis to determine the words in news media that best\nclassifies countries as higher or lower peace. We found that higher peace news\nis characterized by themes of finance, daily actitivities, and health and that\nlower peace news is characterized by themes of politics, government, and legal\nissues. This work provides a starting point to measure levels of peace and\nidentify the social processes that underly those words.\n", "date": "2024-10-02", "categories": []}
{"id": "2503.24199", "title": "Agent-Based Simulations of Online Political Discussions: A Case Study on\n  Elections in Germany", "abstract": "  User engagement on social media platforms is influenced by historical\ncontext, time constraints, and reward-driven interactions. This study presents\nan agent-based simulation approach that models user interactions, considering\npast conversation history, motivation, and resource constraints. Utilizing\nGerman Twitter data on political discourse, we fine-tune AI models to generate\nposts and replies, incorporating sentiment analysis, irony detection, and\noffensiveness classification. The simulation employs a myopic best-response\nmodel to govern agent behavior, accounting for decision-making based on\nexpected rewards. Our results highlight the impact of historical context on\nAI-generated responses and demonstrate how engagement evolves under varying\nconstraints.\n", "date": "2025-03-31", "categories": []}
{"id": "2504.08213", "title": "Big Meaning: Qualitative Analysis on Large Bodies of Data Using AI", "abstract": "  This study introduces a framework that leverages AI-generated descriptive\ncodes to indicate a text's fecundity--the density of unique human-generated\ncodes--in thematic analysis. Rather than replacing human interpretation,\nAI-generated codes guide the selection of texts likely to yield richer\nqualitative insights. Using a dataset of 2,530 Malaysian news articles on\nrefugee attitudes, we compare AI-selected documents to randomly chosen ones by\nhaving three human coders independently derive codes. The results demonstrate\nthat AI-selected texts exhibit approximately twice the fecundity. Our findings\nsupport the use of AI-generated codes as an effective proxy for identifying\ndocuments with a high potential for meaning-making in thematic analysis.\n", "date": "2025-04-11", "categories": []}
{"id": "2504.07400", "title": "Talking Point based Ideological Discourse Analysis in News Events", "abstract": "  Analyzing ideological discourse even in the age of LLMs remains a challenge,\nas these models often struggle to capture the key elements that shape\nreal-world narratives. Specifically, LLMs fail to focus on characteristic\nelements driving dominant discourses and lack the ability to integrate\ncontextual information required for understanding abstract ideological views.\nTo address these limitations, we propose a framework motivated by the theory of\nideological discourse analysis to analyze news articles related to real-world\nevents. Our framework represents the news articles using a relational structure\n- talking points, which captures the interaction between entities, their roles,\nand media frames along with a topic of discussion. It then constructs a\nvocabulary of repeating themes - prominent talking points, that are used to\ngenerate ideology-specific viewpoints (or partisan perspectives). We evaluate\nour framework's ability to generate these perspectives through automated tasks\n- ideology and partisan classification tasks, supplemented by human validation.\nAdditionally, we demonstrate straightforward applicability of our framework in\ncreating event snapshots, a visual way of interpreting event discourse. We\nrelease resulting dataset and model to the community to support further\nresearch.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.07719", "title": "Counting Hours, Counting Losses: The Toll of Unpredictable Work\n  Schedules on Financial Security", "abstract": "  Financial instability has become a significant issue in today's society.\nWhile research typically focuses on financial aspects, there is a tendency to\noverlook time-related aspects of unstable work schedules. The inability to rely\non consistent work schedules leads to burnout, work-family conflicts, and\nfinancial shocks that directly impact workers' income and assets. Unforeseen\nfluctuations in earnings pose challenges in financial planning, affecting\ndecisions on savings and spending and ultimately undermining individuals'\nlong-term financial stability and well-being.\n  This issue is particularly evident in sectors where workers experience\nfrequently changing schedules without sufficient notice, including those in the\nfood service and retail sectors, part-time and hourly workers, and individuals\nwith lower incomes. These groups are already more financially vulnerable, and\nthe unpredictable nature of their schedules exacerbates their financial\nfragility.\n  Our objective is to understand how unforeseen fluctuations in earnings\nexacerbate financial fragility by investigating the extent to which\nindividuals' financial management depends on their ability to anticipate and\nplan for the future. To address this question, we develop a simulation\nframework that models how individuals optimize utility amidst financial\nuncertainty and the imperative to avoid financial ruin. We employ online\nlearning techniques, specifically adapting workers' consumption policies based\non evolving information about their work schedules.\n  With this framework, we show both theoretically and empirically how a\nworker's capacity to anticipate schedule changes enhances their long-term\nutility. Conversely, the inability to predict future events can worsen workers'\ninstability. Moreover, our framework enables us to explore interventions to\nmitigate the problem of schedule uncertainty and evaluate their effectiveness.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.04325", "title": "Constructing the Truth: Text Mining and Linguistic Networks in Public\n  Hearings of Case 03 of the Special Jurisdiction for Peace (JEP)", "abstract": "  Case 03 of the Special Jurisdiction for Peace (JEP), focused on the so-called\nfalse positives in Colombia, represents one of the most harrowing episodes of\nthe Colombian armed conflict. This article proposes an innovative methodology\nbased on natural language analysis and semantic co-occurrence models to\nexplore, systematize, and visualize narrative patterns present in the public\nhearings of victims and appearing parties. By constructing skipgram networks\nand analyzing their modularity, the study identifies thematic clusters that\nreveal regional and procedural status differences, providing empirical evidence\non dynamics of victimization, responsibility, and acknowledgment in this case.\nThis computational approach contributes to the collective construction of both\njudicial and extrajudicial truth, offering replicable tools for other\ntransitional justice cases. The work is grounded in the pillars of truth,\njustice, reparation, and non-repetition, proposing a critical and in-depth\nreading of contested memories.\n", "date": "2025-04-06", "categories": []}
{"id": "2504.04311", "title": "A Survey of Social Cybersecurity: Techniques for Attack Detection,\n  Evaluations, Challenges, and Future Prospects", "abstract": "  In today's digital era, the Internet, especially social media platforms,\nplays a significant role in shaping public opinions, attitudes, and beliefs.\nUnfortunately, the credibility of scientific information sources is often\nundermined by the spread of misinformation through various means, including\ntechnology-driven tools like bots, cyborgs, trolls, sock-puppets, and deep\nfakes. This manipulation of public discourse serves antagonistic business\nagendas and compromises civil society. In response to this challenge, a new\nscientific discipline has emerged: social cybersecurity.\n", "date": "2025-04-06", "categories": []}
{"id": "2504.04782", "title": "I only read it for the plot! Maturity Ratings Affect Fanfiction Style\n  and Community Engagement", "abstract": "  We consider the textual profiles of different fanfiction maturity ratings,\nhow they vary across fan groups, and how this relates to reader engagement\nmetrics. Previous studies have shown that fanfiction writing is motivated by a\ncombination of admiration for and frustration with the fan object. These\nfindings emerge when looking at fanfiction as a whole, as well as when it is\ndivided into subgroups, also called fandoms. However, maturity ratings are used\nto indicate the intended audience of the fanfiction, as well as whether the\nstory includes mature themes and explicit scenes. Since these ratings can be\nused to filter readers and writers, they can also be seen as a proxy for\ndifferent reader/writer motivations and desires. We find that explicit\nfanfiction in particular has a distinct textual profile when compared to other\nmaturity ratings. These findings thus nuance our understanding of reader/writer\nmotivations in fanfiction communities, and also highlights the influence of the\ncommunity norms and fan behavior more generally on these cultural products.\n", "date": "2025-04-07", "categories": []}
{"id": "2504.02887", "title": "Processes Matter: How ML/GAI Approaches Could Support Open Qualitative\n  Coding of Online Discourse Datasets", "abstract": "  Open coding, a key inductive step in qualitative research, discovers and\nconstructs concepts from human datasets. However, capturing extensive and\nnuanced aspects or \"coding moments\" can be challenging, especially with large\ndiscourse datasets. While some studies explore machine learning (ML)/Generative\nAI (GAI)'s potential for open coding, few evaluation studies exist. We compare\nopen coding results by five recently published ML/GAI approaches and four human\ncoders, using a dataset of online chat messages around a mobile learning\nsoftware. Our systematic analysis reveals ML/GAI approaches' strengths and\nweaknesses, uncovering the complementary potential between humans and AI.\nLine-by-line AI approaches effectively identify content-based codes, while\nhumans excel in interpreting conversational dynamics. We discussed how embedded\nanalytical processes could shape the results of ML/GAI approaches. Instead of\nreplacing humans in open coding, researchers should integrate AI with and\naccording to their analytical processes, e.g., as parallel co-coders.\n", "date": "2025-04-02", "categories": []}
{"id": "2504.03520", "title": "Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles", "abstract": "  Bias in news reporting significantly impacts public perception, particularly\nregarding crime, politics, and societal issues. Traditional bias detection\nmethods, predominantly reliant on human moderation, suffer from subjective\ninterpretations and scalability constraints. Here, we introduce an AI-driven\nframework leveraging advanced large language models (LLMs), specifically\nGPT-4o, GPT-4o Mini, Gemini Pro, Gemini Flash, Llama 8B, and Llama 3B, to\nsystematically identify and mitigate biases in news articles. To this end, we\ncollect an extensive dataset consisting of over 30,000 crime-related articles\nfrom five politically diverse news sources spanning a decade (2013-2023). Our\napproach employs a two-stage methodology: (1) bias detection, where each LLM\nscores and justifies biased content at the paragraph level, validated through\nhuman evaluation for ground truth establishment, and (2) iterative debiasing\nusing GPT-4o Mini, verified by both automated reassessment and human reviewers.\nEmpirical results indicate GPT-4o Mini's superior accuracy in bias detection\nand effectiveness in debiasing. Furthermore, our analysis reveals temporal and\ngeographical variations in media bias correlating with socio-political dynamics\nand real-world events. This study contributes to scalable computational\nmethodologies for bias mitigation, promoting fairness and accountability in\nnews reporting.\n", "date": "2025-04-04", "categories": []}
{"id": "2503.00599", "title": "Large Engagement Networks for Classifying Coordinated Campaigns and\n  Organic Twitter Trends", "abstract": "  Social media users and inauthentic accounts, such as bots, may coordinate in\npromoting their topics. Such topics may give the impression that they are\norganically popular among the public, even though they are astroturfing\ncampaigns that are centrally managed. It is challenging to predict if a topic\nis organic or a coordinated campaign due to the lack of reliable ground truth.\nIn this paper, we create such ground truth by detecting the campaigns promoted\nby ephemeral astroturfing attacks. These attacks push any topic to Twitter's\n(X) trends list by employing bots that tweet in a coordinated manner in a short\nperiod and then immediately delete their tweets. We manually curate a dataset\nof organic Twitter trends. We then create engagement networks out of these\ndatasets which can serve as a challenging testbed for graph classification task\nto distinguish between campaigns and organic trends. Engagement networks\nconsist of users as nodes and engagements as edges (retweets, replies, and\nquotes) between users. We release the engagement networks for 179 campaigns and\n135 non-campaigns, and also provide finer-grain labels to characterize the type\nof the campaigns and non-campaigns. Our dataset, LEN (Large Engagement\nNetworks), is available in the URL below. In comparison to traditional graph\nclassification datasets, which are small with tens of nodes and hundreds of\nedges at most, graphs in LEN are larger. The average graph in LEN has ~11K\nnodes and ~23K edges. We show that state-of-the-art GNN methods give only\nmediocre results for campaign vs. non-campaign and campaign type classification\non LEN. LEN offers a unique and challenging playfield for the graph\nclassification problem. We believe that LEN will help advance the frontiers of\ngraph classification techniques on large networks and also provide an\ninteresting use case in terms of distinguishing coordinated campaigns and\norganic trends.\n", "date": "2025-03-01", "categories": []}
{"id": "2503.22411", "title": "Elite Political Discourse has Become More Toxic in Western Countries", "abstract": "  Toxic and uncivil politics is widely seen as a growing threat to democratic\nvalues and governance, yet our understanding of the drivers and evolution of\npolitical incivility remains limited. Leveraging a novel dataset of nearly 18\nmillion Twitter messages from parliamentarians in 17 countries over five years,\nthis paper systematically investigates whether politics internationally is\nbecoming more uncivil, and what are the determinants of political incivility.\nOur analysis reveals a marked increase in toxic discourse among political\nelites, and that it is associated to radical-right parties and parties in\nopposition. Toxicity diminished markedly during the early phase of the COVID-19\npandemic and, surprisingly, during election campaigns. Furthermore, our results\nindicate that posts relating to ``culture war'' topics, such as migration and\nLGBTQ+ rights, are substantially more toxic than debates focused on welfare or\neconomic issues. These findings underscore a troubling shift in international\ndemocracies toward an erosion of constructive democratic dialogue.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.20231", "title": "Dynamics of Algorithmic Content Amplification on TikTok", "abstract": "  Intelligent algorithms increasingly shape the content we encounter and engage\nwith online. TikTok's For You feed exemplifies extreme algorithm-driven\ncuration, tailoring the stream of video content almost exclusively based on\nusers' explicit and implicit interactions with the platform. Despite growing\nattention, the dynamics of content amplification on TikTok remain largely\nunquantified. How quickly, and to what extent, does TikTok's algorithm amplify\ncontent aligned with users' interests? To address these questions, we conduct a\nsock-puppet audit, deploying bots with different interests to engage with\nTikTok's \"For You\" feed. Our findings reveal that content aligned with the\nbots' interests undergoes strong amplification, with rapid reinforcement\ntypically occurring within the first 200 videos watched. While amplification is\nconsistently observed across all interests, its intensity varies by interest,\nindicating the emergence of topic-specific biases. Time series analyses and\nMarkov models uncover distinct phases of recommendation dynamics, including\npersistent content reinforcement and a gradual decline in content diversity\nover time. Although TikTok's algorithm preserves some content diversity, we\nfind a strong negative correlation between amplification and exploration: as\nthe amplification of interest-aligned content increases, engagement with unseen\nhashtags declines. These findings contribute to discussions on\nsocio-algorithmic feedback loops in the digital age and the trade-offs between\npersonalization and content diversity.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.20233", "title": "Dynamic Learning and Productivity for Data Analysts: A Bayesian Hidden\n  Markov Model Perspective", "abstract": "  Data analysts are essential in organizations, transforming raw data into\ninsights that drive decision-making and strategy. This study explores how\nanalysts' productivity evolves on a collaborative platform, focusing on two key\nlearning activities: writing queries and viewing peer queries. While\ntraditional research often assumes static models, where performance improves\nsteadily with cumulative learning, such models fail to capture the dynamic\nnature of real-world learning. To address this, we propose a Hidden Markov\nModel (HMM) that tracks how analysts transition between distinct learning\nstates based on their participation in these activities.\n  Using an industry dataset with 2,001 analysts and 79,797 queries, this study\nidentifies three learning states: novice, intermediate, and advanced.\nProductivity increases as analysts advance to higher states, reflecting the\ncumulative benefits of learning. Writing queries benefits analysts across all\nstates, with the largest gains observed for novices. Viewing peer queries\nsupports novices but may hinder analysts in higher states due to cognitive\noverload or inefficiencies. Transitions between states are also uneven, with\nprogression from intermediate to advanced being particularly challenging. This\nstudy advances understanding of into dynamic learning behavior of knowledge\nworker and offers practical implications for designing systems, optimizing\ntraining, enabling personalized learning, and fostering effective knowledge\nsharing.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.16497", "title": "Effective Yet Ephemeral Propaganda Defense: There Needs to Be More than\n  One-Shot Inoculation to Enhance Critical Thinking", "abstract": "  In today's media landscape, propaganda distribution has a significant impact\non society. It sows confusion, undermines democratic processes, and leads to\nincreasingly difficult decision-making for news readers. We investigate the\nlasting effect on critical thinking and propaganda awareness on them when using\na propaganda detection and contextualization tool. Building on inoculation\ntheory, which suggests that preemptively exposing individuals to weakened forms\nof propaganda can improve their resilience against it, we integrate Kahneman's\ndual-system theory to measure the tools' impact on critical thinking. Through a\ntwo-phase online experiment, we measure the effect of several inoculation\ndoses. Our findings show that while the tool increases critical thinking during\nits use, this increase vanishes without access to the tool. This indicates a\nsingle use of the tool does not create a lasting impact. We discuss the\nimplications and propose possible approaches to improve the resilience against\npropaganda in the long-term.\n", "date": "2025-03-11", "categories": []}
{"id": "2503.15057", "title": "A Data-driven Investigation of Euphemistic Language: Comparing the usage\n  of \"slave\" and \"servant\" in 19th century US newspapers", "abstract": "  This study investigates the usage of \"slave\" and \"servant\" in the 19th\ncentury US newspapers using computational methods. While both terms were used\nto refer to enslaved African Americans, they were used in distinct ways. In the\nChronicling America corpus, we included possible OCR errors by using FastText\nembedding and excluded text reprints to consider text reprint culture in the\n19th century. Word2vec embedding was used to find semantically close words to\n\"slave\" and \"servant\" and log-odds ratio was calculated to identify\nover-represented discourse words in the Southern and Northern newspapers. We\nfound that \"slave\" is associated with socio-economic, legal, and administrative\nwords, however, \"servant\" is linked to religious words in the Northern\nnewspapers while Southern newspapers associated \"servant\" with domestic and\nfamilial words. We further found that slave discourse words in Southern\nnewspapers are more prevalent in Northern newspapers while servant discourse\nwords from each side are prevalent in their own region. This study contributes\nto the understanding of how newspapers created different discourses around\nenslaved African Americans in the 19th century US.\n", "date": "2025-03-19", "categories": []}
{"id": "2212.06492", "title": "FNDaaS: Content-agnostic Detection of Fake News sites", "abstract": "  Automatic fake news detection is a challenging problem in misinformation\nspreading, and it has tremendous real-world political and social impacts. Past\nstudies have proposed machine learning-based methods for detecting such fake\nnews, focusing on different properties of the published news articles, such as\nlinguistic characteristics of the actual content, which however have\nlimitations due to the apparent language barriers. Departing from such efforts,\nwe propose Fake News Detection-as-a Service (FNDaaS), the first automatic,\ncontent-agnostic fake news detection method, that considers new and unstudied\nfeatures such as network and structural characteristics per news website. This\nmethod can be enforced as-a-Service, either at the ISP-side for easier\nscalability and maintenance, or user-side for better end-user privacy. We\ndemonstrate the efficacy of our method using more than 340K datapoints crawled\nfrom existing lists of 637 fake and 1183 real news websites, and by building\nand testing a proof of concept system that materializes our proposal. Our\nanalysis of data collected from these websites shows that the vast majority of\nfake news domains are very young and appear to have lower time periods of an IP\nassociated with their domain than real news ones. By conducting various\nexperiments with machine learning classifiers, we demonstrate that FNDaaS can\nachieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on\nnewly-flagged ones.\n", "date": "2022-12-13", "categories": []}
{"id": "2503.09853", "title": "Who Are You Behind the Screen? Implicit MBTI and Gender Detection Using\n  Artificial Intelligence", "abstract": "  In personalized technology and psychological research, precisely detecting\ndemographic features and personality traits from digital interactions becomes\never more important. This work investigates implicit categorization, inferring\npersonality and gender variables directly from linguistic patterns in Telegram\nconversation data, while conventional personality prediction techniques mostly\ndepend on explicitly self-reported labels. We refine a Transformer-based\nlanguage model (RoBERTa) to capture complex linguistic cues indicative of\npersonality traits and gender differences using a dataset comprising 138,866\nmessages from 1,602 users annotated with MBTI types and 195,016 messages from\n2,598 users annotated with gender. Confidence levels help to greatly raise\nmodel accuracy to 86.16\\%, hence proving RoBERTa's capacity to consistently\nidentify implicit personality types from conversational text data. Our results\nhighlight the usefulness of Transformer topologies for implicit personality and\ngender classification, hence stressing their efficiency and stressing important\ntrade-offs between accuracy and coverage in realistic conversational\nenvironments. With regard to gender classification, the model obtained an\naccuracy of 74.4\\%, therefore capturing gender-specific language patterns.\nPersonality dimension analysis showed that people with introverted and\nintuitive preferences are especially more active in text-based interactions.\nThis study emphasizes practical issues in balancing accuracy and data coverage\nas Transformer-based models show their efficiency in implicit personality and\ngender prediction tasks from conversational texts.\n", "date": "2025-03-12", "categories": []}
{"id": "2503.10738", "title": "Visual Polarization Measurement Using Counterfactual Image Generation", "abstract": "  Political polarization is a significant issue in American politics,\ninfluencing public discourse, policy, and consumer behavior. While studies on\npolarization in news media have extensively focused on verbal content,\nnon-verbal elements, particularly visual content, have received less attention\ndue to the complexity and high dimensionality of image data. Traditional\ndescriptive approaches often rely on feature extraction from images, leading to\nbiased polarization estimates due to information loss. In this paper, we\nintroduce the Polarization Measurement using Counterfactual Image Generation\n(PMCIG) method, which combines economic theory with generative models and\nmulti-modal deep learning to fully utilize the richness of image data and\nprovide a theoretically grounded measure of polarization in visual content.\nApplying this framework to a decade-long dataset featuring 30 prominent\npoliticians across 20 major news outlets, we identify significant polarization\nin visual content, with notable variations across outlets and politicians. At\nthe news outlet level, we observe significant heterogeneity in visual slant.\nOutlets such as Daily Mail, Fox News, and Newsmax tend to favor Republican\npoliticians in their visual content, while The Washington Post, USA Today, and\nThe New York Times exhibit a slant in favor of Democratic politicians. At the\npolitician level, our results reveal substantial variation in polarized\ncoverage, with Donald Trump and Barack Obama among the most polarizing figures,\nwhile Joe Manchin and Susan Collins are among the least. Finally, we conduct a\nseries of validation tests demonstrating the consistency of our proposed\nmeasures with external measures of media slant that rely on non-image-based\nsources.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.11743", "title": "PUBLICSPEAK: Hearing the Public with a Probabilistic Framework in Local\n  Government", "abstract": "  Local governments around the world are making consequential decisions on\nbehalf of their constituents, and these constituents are responding with\nrequests, advice, and assessments of their officials at public meetings. So\nmany small meetings cannot be covered by traditional newsrooms at scale. We\npropose PUBLICSPEAK, a probabilistic framework which can utilize meeting\nstructure, domain knowledge, and linguistic information to discover public\nremarks in local government meetings. We then use our approach to inspect the\nissues raised by constituents in 7 cities across the United States. We evaluate\nour approach on a novel dataset of local government meetings and find that\nPUBLICSPEAK improves over state-of-the-art by 10% on average, and by up to 40%.\n", "date": "2025-03-14", "categories": []}
{"id": "2410.19150", "title": "A Test of Time: Predicting the Sustainable Success of Online\n  Collaboration in Wikipedia", "abstract": "  The Internet has significantly expanded the potential for global\ncollaboration, allowing millions of users to contribute to collective projects\nlike Wikipedia. While prior work has assessed the success of online\ncollaborations, most approaches are time-agnostic, evaluating success without\nconsidering its longevity. Research on the factors that ensure the long-term\npreservation of high-quality standards in online collaboration is scarce. In\nthis study, we address this gap. We propose a novel metric, `Sustainable\nSuccess,' which measures the ability of collaborative efforts to maintain their\nquality over time. Using Wikipedia as a case study, we introduce the\nSustainPedia dataset, which compiles data from over 40K Wikipedia articles,\nincluding each article's sustainable success label and more than 300\nexplanatory features such as edit history, user experience, and team\ncomposition. Using this dataset, we develop machine learning models to predict\nthe sustainable success of Wikipedia articles. Our best-performing model\nachieves a high AU-ROC score of 0.88 on average. Our analysis reveals important\ninsights. For example, we find that the longer an article takes to be\nrecognized as high-quality, the more likely it is to maintain that status over\ntime (i.e., be sustainable). Additionally, user experience emerged as the most\ncritical predictor of sustainability. Our analysis provides insights into\nbroader collective actions beyond Wikipedia (e.g., online activism,\ncrowdsourced open-source software), where the same social dynamics that drive\nsuccess on Wikipedia might play a role. We make all data and code used for this\nstudy publicly available for further research.\n", "date": "2024-10-24", "categories": []}
{"id": "2503.10648", "title": "Hate Speech and Sentiment of YouTube Video Comments From Public and\n  Private Sources Covering the Israel-Palestine Conflict", "abstract": "  This study explores the prevalence of hate speech (HS) and sentiment in\nYouTube video comments concerning the Israel-Palestine conflict by analyzing\ncontent from both public and private news sources. The research involved\nannotating 4983 comments for HS and sentiments (neutral, pro-Israel, and\npro-Palestine). Subsequently, machine learning (ML) models were developed,\ndemonstrating robust predictive capabilities with area under the receiver\noperating characteristic (AUROC) scores ranging from 0.83 to 0.90. These models\nwere applied to the extracted comment sections of YouTube videos from public\nand private sources, uncovering a higher incidence of HS in public sources\n(40.4%) compared to private sources (31.6%). Sentiment analysis revealed a\npredominantly neutral stance in both source types, with more pronounced\nsentiments towards Israel and Palestine observed in public sources. This\ninvestigation highlights the dynamic nature of online discourse surrounding the\nIsrael-Palestine conflict and underscores the potential of moderating content\nin a politically charged environment.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.11116", "title": "Trust in Disinformation Narratives: a Trust in the News Experiment", "abstract": "  Understanding why people trust or distrust one another, institutions, or\ninformation is a complex task that has led scholars from various fields of\nstudy to employ diverse epistemological and methodological approaches. Despite\nthe challenges, it is generally agreed that the antecedents of trust (and\ndistrust) encompass a multitude of emotional and cognitive factors, including a\ngeneral disposition to trust and an assessment of trustworthiness factors. In\nan era marked by increasing political polarization, cultural backlash,\nwidespread disinformation and fake news, and the use of AI software to produce\nnews content, the need to study trust in the news has gained significant\ntraction. This study presents the findings of a trust in the news experiment\ndesigned in collaboration with Spanish and UK journalists, fact-checkers, and\nthe CardiffNLP Natural Language Processing research group. The purpose of this\nexperiment, conducted in June 2023, was to examine the extent to which people\ntrust a set of fake news articles based on previously identified disinformation\nnarratives related to gender, climate change, and COVID-19. The online\nexperiment participants (801 in Spain and 800 in the UK) were asked to read\nthree fake news items and rate their level of trust on a scale from 1 (not\ntrue) to 8 (true). The pieces used a combination of factors, including stance\n(favourable, neutral, or against the narrative), presence of toxic expressions,\nclickbait titles, and sources of information to test which elements influenced\npeople's responses the most. Half of the pieces were produced by humans and the\nother half by ChatGPT. The results show that the topic of news articles,\nstance, people's age, gender, and political ideologies significantly affected\ntheir levels of trust in the news, while the authorship (humans or ChatGPT)\ndoes not have a significant impact.\n", "date": "2025-03-14", "categories": []}
{"id": "2409.15949", "title": "Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics", "abstract": "  The application of text mining methods is becoming increasingly prevalent,\nparticularly within Humanities and Computational Social Sciences, as well as in\na broader range of disciplines. This paper presents an analysis of gender bias\nin English song lyrics using topic modeling and bias measurement techniques.\nLeveraging BERTopic, we cluster a dataset of 537,553 English songs into\ndistinct topics and analyze their temporal evolution. Our results reveal a\nsignificant thematic shift in song lyrics over time, transitioning from\nromantic themes to a heightened focus on the sexualization of women.\nAdditionally, we observe a substantial prevalence of profanity and misogynistic\ncontent across various topics, with a particularly high concentration in the\nlargest thematic cluster. To further analyse gender bias across topics and\ngenres in a quantitative way, we employ the Single Category Word Embedding\nAssociation Test (SC-WEAT) to calculate bias scores for word embeddings trained\non the most prominent topics as well as individual genres. The results indicate\na consistent male bias in words associated with intelligence and strength,\nwhile appearance and weakness words show a female bias. Further analysis\nhighlights variations in these biases across topics, illustrating the interplay\nbetween thematic content and gender stereotypes in song lyrics.\n", "date": "2024-09-24", "categories": []}
{"id": "2503.08709", "title": "Simulating Influence Dynamics with LLM Agents", "abstract": "  This paper introduces a simulator designed for opinion dynamics researchers\nto model competing influences within social networks in the presence of\nLLM-based agents. By integrating established opinion dynamics principles with\nstate-of-the-art LLMs, this tool enables the study of influence propagation and\ncounter-misinformation strategies. The simulator is particularly valuable for\nresearchers in social science, psychology, and operations research, allowing\nthem to analyse societal phenomena without requiring extensive coding\nexpertise. Additionally, the simulator will be openly available on GitHub,\nensuring accessibility and adaptability for those who wish to extend its\ncapabilities for their own research.\n", "date": "2025-03-10", "categories": []}
{"id": "2301.10856", "title": "Partial Mobilization: Tracking Multilingual Information Flows Amongst\n  Russian Media Outlets and Telegram", "abstract": "  In response to disinformation and propaganda from Russian online media\nfollowing the invasion of Ukraine, Russian media outlets such as Russia Today\nand Sputnik News were banned throughout Europe. To maintain viewership, many of\nthese Russian outlets began to heavily promote their content on messaging\nservices like Telegram. In this work, we study how 16 Russian media outlets\ninteracted with and utilized 732 Telegram channels throughout 2022. Leveraging\nthe foundational model MPNet, DP-means clustering, and Hawkes processes, we\ntrace how narratives spread between news sites and Telegram channels. We show\nthat news outlets not only propagate existing narratives through Telegram but\nthat they source material from the messaging platform. For example, across the\nwebsites in our study, between 2.3% (ura.news) and 26.7% (ukraina.ru) of\narticles discussed content that originated/resulted from activity on Telegram.\nFinally, tracking the spread of individual topics, we measure the rate at which\nnews outlets and Telegram channels disseminate content within the Russian media\necosystem, finding that websites like ura.news and Telegram channels such as\n@genshab are the most effective at disseminating their content.\n", "date": "2023-01-25", "categories": []}
{"id": "2503.05707", "title": "Russo-Ukrainian war disinformation detection in suspicious Telegram\n  channels", "abstract": "  The paper proposes an advanced approach for identifying disinformation on\nTelegram channels related to the Russo-Ukrainian conflict, utilizing\nstate-of-the-art (SOTA) deep learning techniques and transfer learning.\nTraditional methods of disinformation detection, often relying on manual\nverification or rule-based systems, are increasingly inadequate in the face of\nrapidly evolving propaganda tactics and the massive volume of data generated\ndaily. To address these challenges, the proposed system employs deep learning\nalgorithms, including LLM models, which are fine-tuned on a custom dataset\nencompassing verified disinformation and legitimate content. The paper's\nfindings indicate that this approach significantly outperforms traditional\nmachine learning techniques, offering enhanced contextual understanding and\nadaptability to emerging disinformation strategies.\n", "date": "2025-02-13", "categories": []}
{"id": "2503.07179", "title": "Strategies for political-statement segmentation and labelling in\n  unstructured text", "abstract": "  Analysis of parliamentary speeches and political-party manifestos has become\nan integral area of computational study of political texts. While speeches have\nbeen overwhelmingly analysed using unsupervised methods, a large corpus of\nmanifestos with by-statement political-stance labels has been created by the\nparticipants of the MARPOR project. It has been recently shown that these\nlabels can be predicted by a neural model; however, the current approach relies\non provided statement boundaries, limiting out-of-domain applicability. In this\nwork, we propose and test a range of unified split-and-label frameworks --\nbased on linear-chain CRFs, fine-tuned text-to-text models, and the combination\nof in-context learning with constrained decoding -- that can be used to jointly\nsegment and classify statements from raw textual data. We show that our\napproaches achieve competitive accuracy when applied to raw text of political\nmanifestos, and then demonstrate the research potential of our method by\napplying it to the records of the UK House of Commons and tracing the political\ntrajectories of four major parties in the last three decades.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.07364", "title": "Artificial Utopia: Simulation and Intelligent Agents for a Democratised\n  Future", "abstract": "  Prevailing top-down systems in politics and economics struggle to keep pace\nwith the pressing challenges of the 21st century, such as climate change,\nsocial inequality and conflict. Bottom-up democratisation and participatory\napproaches in politics and economics are increasingly seen as promising\nalternatives to confront and overcome these issues, often with utopian\novertones, as proponents believe they may dramatically reshape political,\nsocial and ecological futures for the better and in contrast to contemporary\nauthoritarian tendencies across various countries. Institutional specifics and\nthe associated collective human behavior or culture remains little understood\nand debated, however. In this article, I propose a novel research agenda\nfocusing on utopian democratisation efforts with formal and computational\nmethods as well as with artificial intelligence - I call this agenda Artificial\nUtopia. Artificial Utopias provide safe testing grounds for new political ideas\nand economic policies in-silico with reduced risk of negative consequences as\ncompared to testing ideas in real-world contexts. An increasing number of\nadvanced simulation and intelligence methods, that aim at representing human\ncognition and collective decision-making in more realistic ways, could benefit\nthis process. This includes agent-based modelling, reinforcement learning,\nlarge language models and more. I clarify what some of these simulation\napproaches can contribute to the study of Artificial Utopias with the help of\ntwo institutional examples: the citizen assembly and the democratic firm.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.07395", "title": "Revisiting Noise in Natural Language Processing for Computational Social\n  Science", "abstract": "  Computational Social Science (CSS) is an emerging field driven by the\nunprecedented availability of human-generated content for researchers. This\nfield, however, presents a unique set of challenges due to the nature of the\ntheories and datasets it explores, including highly subjective tasks and\ncomplex, unstructured textual corpora. Among these challenges, one of the less\nwell-studied topics is the pervasive presence of noise. This thesis aims to\naddress this gap in the literature by presenting a series of interconnected\ncase studies that examine different manifestations of noise in CSS. These\ninclude character-level errors following the OCR processing of historical\nrecords, archaic language, inconsistencies in annotations for subjective and\nambiguous tasks, and even noise and biases introduced by large language models\nduring content generation. This thesis challenges the conventional notion that\nnoise in CSS is inherently harmful or useless. Rather, it argues that certain\nforms of noise can encode meaningful information that is invaluable for\nadvancing CSS research, such as the unique communication styles of individuals\nor the culture-dependent nature of datasets and tasks. Further, this thesis\nhighlights the importance of nuance in dealing with noise and the\nconsiderations CSS researchers must address when encountering it, demonstrating\nthat different types of noise require distinct strategies.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.05065", "title": "The study of short texts in digital politics: Document aggregation for\n  topic modeling", "abstract": "  Statistical topic modeling is widely used in political science to study text.\nResearchers examine documents of varying lengths, from tweets to speeches.\nThere is ongoing debate on how document length affects the interpretability of\ntopic models. We investigate the effects of aggregating short documents into\nlarger ones based on natural units that partition the corpus. In our study, we\nanalyze one million tweets by U.S. state legislators from April 2016 to\nSeptember 2020. We find that for documents aggregated at the account level,\ntopics are more associated with individual states than when using individual\ntweets. This finding is replicated with Wikipedia pages aggregated by birth\ncities, showing how document definitions can impact topic modeling results.\n", "date": "2025-03-07", "categories": []}
{"id": "2404.09299", "title": "Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora", "abstract": "  Media Storms, dramatic outbursts of attention to a story, are central\ncomponents of media dynamics and the attention landscape. Despite their\nsignificance, there has been little systematic and empirical research on this\nconcept due to issues of measurement and operationalization. We introduce an\niterative human-in-the-loop method to identify media storms in a large-scale\ncorpus of news articles. The text is first transformed into signals of\ndispersion based on several textual characteristics. In each iteration, we\napply unsupervised anomaly detection to these signals; each anomaly is then\nvalidated by an expert to confirm the presence of a storm, and those results\nare then used to tune the anomaly detection in the next iteration. We\ndemonstrate the applicability of this method in two scenarios: first,\nsupplementing an initial list of media storms within a specific time frame; and\nsecond, detecting media storms in new time periods. We make available a media\nstorm dataset compiled using both scenarios. Both the method and dataset offer\nthe basis for comprehensive empirical research into the concept of media\nstorms, including characterizing them and predicting their outbursts and\ndurations, in mainstream media or social media platforms.\n", "date": "2024-04-14", "categories": []}
{"id": "2503.02563", "title": "To Vaccinate or not to Vaccinate? Analyzing $\\mathbb{X}$ Power over the\n  Pandemic", "abstract": "  The COVID-19 pandemic has profoundly affected the normal course of life --\nfrom lock-downs and virtual meetings to the unprecedentedly swift creation of\nvaccines. To halt the COVID-19 pandemic, the world has started preparing for\nthe global vaccine roll-out. In an effort to navigate the immense volume of\ninformation about COVID-19, the public has turned to social networks. Among\nthem, $\\mathbb{X}$ (formerly Twitter) has played a key role in distributing\nrelated information. Most people are not trained to interpret medical research\nand remain skeptical about the efficacy of new vaccines. Measuring their\nreactions and perceptions is gaining significance in the fight against\nCOVID-19. To assess the public perception regarding the COVID-19 vaccine, our\nwork applies a sentiment analysis approach, using natural language processing\nof $\\mathbb{X}$ data. We show how to use textual analytics and textual data\nvisualization to discover early insights (for example, by analyzing the most\nfrequently used keywords and hashtags). Furthermore, we look at how people's\nsentiments vary across the countries. Our results indicate that although the\noverall reaction to the vaccine is positive, there are also negative sentiments\nassociated with the tweets, especially when examined at the country level.\nAdditionally, from the extracted tweets, we manually labeled 100 tweets as\npositive and 100 tweets as negative and trained various One-Class Classifiers\n(OCCs). The experimental results indicate that the S-SVDD classifiers\noutperform other OCCs.\n", "date": "2025-03-04", "categories": []}
{"id": "2503.02671", "title": "Are some books better than others?", "abstract": "  Scholars, awards committees, and laypeople frequently discuss the merit of\nwritten works. Literary professionals and journalists differ in how much\nperspectivism they concede in their book reviews. Here, we quantify how\nstrongly book reviews are determined by the actual book contents vs.\nidiosyncratic reader tendencies. In our analysis of 624,320 numerical and\ntextual book reviews, we find that the contents of professionally published\nbooks are not predictive of a random reader's reading enjoyment. Online reviews\nof popular fiction and non-fiction books carry up to ten times more information\nabout the reviewer than about the book. For books of a preferred genre, readers\nmight be less likely to give low ratings, but still struggle to converge in\ntheir relative assessments. We find that book evaluations generalize more\nacross experienced review writers than casual readers. When discussing specific\nissues with a book, one review text had poor predictability of issues brought\nup in another review of the same book. We conclude that extreme perspectivism\nis a justifiable position when researching literary quality, bestowing literary\nawards, and designing recommendation systems.\n", "date": "2025-03-04", "categories": []}
{"id": "2407.13929", "title": "Unmasking Social Bots: How Confident Are We?", "abstract": "  Social bots remain a major vector for spreading disinformation on social\nmedia and a menace to the public. Despite the progress made in developing\nmultiple sophisticated social bot detection algorithms and tools, bot detection\nremains a challenging, unsolved problem that is fraught with uncertainty due to\nthe heterogeneity of bot behaviors, training data, and detection algorithms.\nDetection models often disagree on whether to label the same account as bot or\nhuman-controlled. However, they do not provide any measure of uncertainty to\nindicate how much we should trust their results. We propose to address both bot\ndetection and the quantification of uncertainty at the account level - a novel\nfeature of this research. This dual focus is crucial as it allows us to\nleverage additional information related to the quantified uncertainty of each\nprediction, thereby enhancing decision-making and improving the reliability of\nbot classifications. Specifically, our approach facilitates targeted\ninterventions for bots when predictions are made with high confidence and\nsuggests caution (e.g., gathering more data) when predictions are uncertain.\n", "date": "2024-07-18", "categories": []}
{"id": "2412.14663", "title": "IOHunter: Graph Foundation Model to Uncover Online Information\n  Operations", "abstract": "  Social media platforms have become vital spaces for public discourse, serving\nas modern agor\\`as where a wide range of voices influence societal narratives.\nHowever, their open nature also makes them vulnerable to exploitation by\nmalicious actors, including state-sponsored entities, who can conduct\ninformation operations (IOs) to manipulate public opinion. The spread of\nmisinformation, false news, and misleading claims threatens democratic\nprocesses and societal cohesion, making it crucial to develop methods for the\ntimely detection of inauthentic activity to protect the integrity of online\ndiscourse. In this work, we introduce a methodology designed to identify users\norchestrating information operations, a.k.a. IO drivers, across various\ninfluence campaigns. Our framework, named IOHunter, leverages the combined\nstrengths of Language Models and Graph Neural Networks to improve\ngeneralization in supervised, scarcely-supervised, and cross-IO contexts. Our\napproach achieves state-of-the-art performance across multiple sets of IOs\noriginating from six countries, significantly surpassing existing approaches.\nThis research marks a step toward developing Graph Foundation Models\nspecifically tailored for the task of IO detection on social media platforms.\n", "date": "2024-12-19", "categories": []}
{"id": "2503.00417", "title": "A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity,\n  Polarization, and Demographics Information", "abstract": "  Polarization is defined as divisive opinions held by two or more groups on\nsubstantive issues. As the world's third-largest democracy, Indonesia faces\ngrowing concerns about the interplay between political polarization and online\ntoxicity, which is often directed at vulnerable minority groups. Despite the\nimportance of this issue, previous NLP research has not fully explored the\nrelationship between toxicity and polarization. To bridge this gap, we present\na novel multi-label Indonesian dataset that incorporates toxicity,\npolarization, and annotator demographic information. Benchmarking this dataset\nusing BERT-base models and large language models (LLMs) shows that polarization\ninformation enhances toxicity classification, and vice versa. Furthermore,\nproviding demographic information significantly improves the performance of\npolarization classification.\n", "date": "2025-03-01", "categories": []}
{"id": "2503.01623", "title": "Lost in Moderation: How Commercial Content Moderation APIs Over- and\n  Under-Moderate Group-Targeted Hate Speech and Linguistic Variations", "abstract": "  Commercial content moderation APIs are marketed as scalable solutions to\ncombat online hate speech. However, the reliance on these APIs risks both\nsilencing legitimate speech, called over-moderation, and failing to protect\nonline platforms from harmful speech, known as under-moderation. To assess such\nrisks, this paper introduces a framework for auditing black-box NLP systems.\nUsing the framework, we systematically evaluate five widely used commercial\ncontent moderation APIs. Analyzing five million queries based on four datasets,\nwe find that APIs frequently rely on group identity terms, such as ``black'',\nto predict hate speech. While OpenAI's and Amazon's services perform slightly\nbetter, all providers under-moderate implicit hate speech, which uses codified\nmessages, especially against LGBTQIA+ individuals. Simultaneously, they\nover-moderate counter-speech, reclaimed slurs and content related to Black,\nLGBTQIA+, Jewish, and Muslim people. We recommend that API providers offer\nbetter guidance on API implementation and threshold setting and more\ntransparency on their APIs' limitations.\n  Warning: This paper contains offensive and hateful terms and concepts. We\nhave chosen to reproduce these terms for reasons of transparency.\n", "date": "2025-03-03", "categories": []}
{"id": "2210.05715", "title": "Language Independent Stance Detection: Social Interaction-based\n  Embeddings and Large Language Models", "abstract": "  The large majority of the research performed on stance detection has been\nfocused on developing more or less sophisticated text classification systems,\neven when many benchmarks are based on social network data such as Twitter.\nThis paper aims to take on the stance detection task by placing the emphasis\nnot so much on the text itself but on the interaction data available on social\nnetworks. More specifically, we propose a new method to leverage social\ninformation such as friends and retweets by generating Relational Embeddings,\nnamely, dense vector representations of interaction pairs. Our experiments on\nseven publicly available datasets and four different languages (Basque,\nCatalan, Italian, and Spanish) show that combining our relational embeddings\nwith discriminative textual methods helps to substantially improve performance,\nobtaining state-of-the-art results for six out of seven evaluation settings,\noutperforming strong baselines based on Large Language Models, or other popular\ninteraction-based approaches such as DeepWalk or node2vec.\n", "date": "2022-10-11", "categories": []}
{"id": "2210.06644", "title": "The COVID That Wasn't: Counterfactual Journalism Using GPT", "abstract": "  In this paper, we explore the use of large language models to assess human\ninterpretations of real world events. To do so, we use a language model trained\nprior to 2020 to artificially generate news articles concerning COVID-19 given\nthe headlines of actual articles written during the pandemic. We then compare\nstylistic qualities of our artificially generated corpus with a news corpus, in\nthis case 5,082 articles produced by CBC News between January 23 and May 5,\n2020. We find our artificially generated articles exhibits a considerably more\nnegative attitude towards COVID and a significantly lower reliance on\ngeopolitical framing. Our methods and results hold importance for researchers\nseeking to simulate large scale cultural processes via recent breakthroughs in\ntext generation.\n", "date": "2022-10-13", "categories": []}
{"id": "2501.00855", "title": "What is a Social Media Bot? A Global Comparison of Bot and Human\n  Characteristics", "abstract": "  Chatter on social media is 20% bots and 80% humans. Chatter by bots and\nhumans is consistently different: bots tend to use linguistic cues that can be\neasily automated while humans use cues that require dialogue understanding.\nBots use words that match the identities they choose to present, while humans\nmay send messages that are not related to the identities they present. Bots and\nhumans differ in their communication structure: sampled bots have a star\ninteraction structure, while sampled humans have a hierarchical structure.\nThese conclusions are based on a large-scale analysis of social media tweets\nacross ~200mil users across 7 events. Social media bots took the world by storm\nwhen social-cybersecurity researchers realized that social media users not only\nconsisted of humans but also of artificial agents called bots. These bots wreck\nhavoc online by spreading disinformation and manipulating narratives. Most\nresearch on bots are based on special-purposed definitions, mostly predicated\non the event studied. This article first begins by asking, \"What is a bot?\",\nand we study the underlying principles of how bots are different from humans.\nWe develop a first-principle definition of a social media bot. With this\ndefinition as a premise, we systematically compare characteristics between bots\nand humans across global events, and reflect on how the software-programmed bot\nis an Artificial Intelligent algorithm, and its potential for evolution as\ntechnology advances. Based on our results, we provide recommendations for the\nuse and regulation of bots. Finally, we discuss open challenges and future\ndirections: Detect, to systematically identify these automated and potentially\nevolving bots; Differentiate, to evaluate the goodness of the bot in terms of\ntheir content postings and relationship interactions; Disrupt, to moderate the\nimpact of malicious bots.\n", "date": "2025-01-01", "categories": []}
{"id": "2305.17174", "title": "From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language\n  Models", "abstract": "  Dogwhistles are coded expressions that simultaneously convey one meaning to a\nbroad audience and a second one, often hateful or provocative, to a narrow\nin-group; they are deployed to evade both political repercussions and\nalgorithmic content moderation. For example, in the sentence 'we need to end\nthe cosmopolitan experiment,' the word 'cosmopolitan' likely means 'worldly' to\nmany, but secretly means 'Jewish' to a select few. We present the first\nlarge-scale computational investigation of dogwhistles. We develop a typology\nof dogwhistles, curate the largest-to-date glossary of over 300 dogwhistles\nwith rich contextual information and examples, and analyze their usage in\nhistorical U.S. politicians' speeches. We then assess whether a large language\nmodel (GPT-3) can identify dogwhistles and their meanings, and find that\nGPT-3's performance varies widely across types of dogwhistles and targeted\ngroups. Finally, we show that harmful content containing dogwhistles avoids\ntoxicity detection, highlighting online risks of such coded language. This work\nsheds light on the theoretical and applied importance of dogwhistles in both\nNLP and computational social science, and provides resources for future\nresearch in modeling dogwhistles and mitigating their online harms.\n", "date": "2023-05-26", "categories": []}
{"id": "2311.16831", "title": "Polarized Online Discourse on Abortion: Frames and Hostile Expressions\n  among Liberals and Conservatives", "abstract": "  Abortion has been one of the most divisive issues in the United States. Yet,\nmissing is comprehensive longitudinal evidence on how political divides on\nabortion are reflected in public discourse over time, on a national scale, and\nin response to key events before and after the overturn of Roe v Wade. We\nanalyze a corpus of over 3.5M tweets related to abortion over the span of one\nyear (January 2022 to January 2023) from over 1.1M users. We estimate users'\nideology and rely on state-of-the-art transformer-based classifiers to identify\nexpressions of hostility and extract five prominent frames surrounding\nabortion. We use those data to examine (a) how prevalent were expressions of\nhostility (i.e., anger, toxic speech, insults, obscenities, and hate speech),\n(b) what frames liberals and conservatives used to articulate their positions\non abortion, and (c) the prevalence of hostile expressions in liberals and\nconservative discussions of these frames. We show that liberals and\nconservatives largely mirrored each other's use of hostile expressions: as\nliberals used more hostile rhetoric, so did conservatives, especially in\nresponse to key events. In addition, the two groups used distinct frames and\ndiscussed them in vastly distinct contexts, suggesting that liberals and\nconservatives have differing perspectives on abortion. Lastly, frames favored\nby one side provoked hostile reactions from the other: liberals use more\nhostile expressions when addressing religion, fetal personhood, and exceptions\nto abortion bans, whereas conservatives use more hostile language when\naddressing bodily autonomy and women's health. This signals disrespect and\nderogation, which may further preclude understanding and exacerbate\npolarization.\n", "date": "2023-11-28", "categories": []}
{"id": "2502.18138", "title": "Large Language Model Driven Agents for Simulating Echo Chamber Formation", "abstract": "  The rise of echo chambers on social media platforms has heightened concerns\nabout polarization and the reinforcement of existing beliefs. Traditional\napproaches for simulating echo chamber formation have often relied on\npredefined rules and numerical simulations, which, while insightful, may lack\nthe nuance needed to capture complex, real-world interactions. In this paper,\nwe present a novel framework that leverages large language models (LLMs) as\ngenerative agents to simulate echo chamber dynamics within social networks. The\nnovelty of our approach is that it incorporates both opinion updates and\nnetwork rewiring behaviors driven by LLMs, allowing for a context-aware and\nsemantically rich simulation of social interactions. Additionally, we utilize\nreal-world Twitter (now X) data to benchmark the LLM-based simulation against\nactual social media behaviors, providing insights into the accuracy and realism\nof the generated opinion trends. Our results demonstrate the efficacy of LLMs\nin modeling echo chamber formation, capturing both structural and semantic\ndimensions of opinion clustering. %This work contributes to a deeper\nunderstanding of social influence dynamics and offers a new tool for studying\npolarization in online communities.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.15998", "title": "News Sentiment as a Predictor for American Domestic Migration", "abstract": "  This paper goes into depth on the effect that US News Sentiment from national\nnewspapers has on US interstate migration trends. Through harnessing data from\nthe New York Times between 2010 and 2020, an average sentiment score was\ncalculated, allowing for data to be entered into a neural network. Then a\nlogistic regression model was used to predict interstate migration. The results\nindicate the model was highly accurate as the mean margin of error was +/- 900\ncitizens. The predictions from the model were compared with the US Census data\nfrom 2010 to 2020 that was used to train the model. Since the input for the\nmodel was not exposed to any migration data, the model clearly demonstrated\nthat its results were drawn from sentiment data alone. These findings are\nsignificant as they indicate that the role of the press could be used as a\npredictor for domestic migration which can help the government and businesses\nunderstand better what is influencing people to move to certain places.\n", "date": "2025-02-21", "categories": []}
{"id": "2502.16879", "title": "A Multi-LLM-Agent-Based Framework for Economic and Public Policy\n  Analysis", "abstract": "  This paper pioneers a novel approach to economic and public policy analysis\nby leveraging multiple Large Language Models (LLMs) as heterogeneous artificial\neconomic agents. We first evaluate five LLMs' economic decision-making\ncapabilities in solving two-period consumption allocation problems under two\ndistinct scenarios: with explicit utility functions and based on intuitive\nreasoning. While previous research has often simulated heterogeneity by solely\nvarying prompts, our approach harnesses the inherent variations in analytical\ncapabilities across different LLMs to model agents with diverse cognitive\ntraits. Building on these findings, we construct a Multi-LLM-Agent-Based (MLAB)\nframework by mapping these LLMs to specific educational groups and\ncorresponding income brackets. Using interest-income taxation as a case study,\nwe demonstrate how the MLAB framework can simulate policy impacts across\nheterogeneous agents, offering a promising new direction for economic and\npublic policy analysis by leveraging LLMs' human-like reasoning capabilities\nand computational power.\n", "date": "2025-02-24", "categories": []}
{"id": "2502.13548", "title": "Detecting Linguistic Bias in Government Documents Using Large language\n  Models", "abstract": "  This paper addresses the critical need for detecting bias in government\ndocuments, an underexplored area with significant implications for governance.\nExisting methodologies often overlook the unique context and far-reaching\nimpacts of governmental documents, potentially obscuring embedded biases that\nshape public policy and citizen-government interactions. To bridge this gap, we\nintroduce the Dutch Government Data for Bias Detection (DGDB), a dataset\nsourced from the Dutch House of Representatives and annotated for bias by\nexperts. We fine-tune several BERT-based models on this dataset and compare\ntheir performance with that of generative language models. Additionally, we\nconduct a comprehensive error analysis that includes explanations of the\nmodels' predictions. Our findings demonstrate that fine-tuned models achieve\nstrong performance and significantly outperform generative language models,\nindicating the effectiveness of DGDB for bias detection. This work underscores\nthe importance of labeled datasets for bias detection in various languages and\ncontributes to more equitable governance practices.\n", "date": "2025-02-19", "categories": []}
{"id": "2502.12450", "title": "Investigating and Extending Homans' Social Exchange Theory with Large\n  Language Model based Agents", "abstract": "  Homans' Social Exchange Theory (SET) is widely recognized as a basic\nframework for understanding the formation and emergence of human civilizations\nand social structures. In social science, this theory is typically studied\nbased on simple simulation experiments or real-world human studies, both of\nwhich either lack realism or are too expensive to control. In artificial\nintelligence, recent advances in large language models (LLMs) have shown\npromising capabilities in simulating human behaviors. Inspired by these\ninsights, we adopt an interdisciplinary research perspective and propose using\nLLM-based agents to study Homans' SET. Specifically, we construct a virtual\nsociety composed of three LLM agents and have them engage in a social exchange\ngame to observe their behaviors. Through extensive experiments, we found that\nHomans' SET is well validated in our agent society, demonstrating the\nconsistency between the agent and human behaviors. Building on this foundation,\nwe intentionally alter the settings of the agent society to extend the\ntraditional Homans' SET, making it more comprehensive and detailed. To the best\nof our knowledge, this paper marks the first step in studying Homans' SET with\nLLM-based agents. More importantly, it introduces a novel and feasible research\nparadigm that bridges the fields of social science and computer science through\nLLM-based agents. Code is available at https://github.com/Paitesanshi/SET.\n", "date": "2025-02-18", "categories": []}
{"id": "2412.12072", "title": "Making FETCH! Happen: Finding Emergent Dog Whistles Through Common\n  Habitats", "abstract": "  WARNING: This paper contains content that maybe upsetting or offensive to\nsome readers. Dog whistles are coded expressions with dual meanings: one\nintended for the general public (outgroup) and another that conveys a specific\nmessage to an intended audience (ingroup). Often, these expressions are used to\nconvey controversial political opinions while maintaining plausible deniability\nand slip by content moderation filters. Identification of dog whistles relies\non curated lexicons, which have trouble keeping up to date. We introduce\nFETCH!, a task for finding novel dog whistles in massive social media corpora.\nWe find that state-of-the-art systems fail to achieve meaningful results across\nthree distinct social media case studies. We present EarShot, a strong baseline\nsystem that combines the strengths of vector databases and Large Language\nModels (LLMs) to efficiently and effectively identify new dog whistles.\n", "date": "2024-12-16", "categories": []}
{"id": "2502.11519", "title": "UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on\n  Graphs", "abstract": "  Polarization and fragmentation in social media amplify user biases, making it\nincreasingly important to understand the evolution of opinions. Opinion\ndynamics provide interpretability for studying opinion evolution, yet\nincorporating these insights into predictive models remains challenging. This\nchallenge arises due to the inherent complexity of the diversity of opinion\nfusion rules and the difficulty in capturing equilibrium states while avoiding\nover-smoothing. This paper constructs a unified opinion dynamics model to\nintegrate different opinion fusion rules and generates corresponding synthetic\ndatasets. To fully leverage the advantages of unified opinion dynamics, we\nintroduces UniGO, a framework for modeling opinion evolution on graphs. Using a\ncoarsen-refine mechanism, UniGO efficiently models opinion dynamics through a\ngraph neural network, mitigating over-smoothing while preserving equilibrium\nphenomena. UniGO leverages pretraining on synthetic datasets, which enhances\nits ability to generalize to real-world scenarios, providing a viable paradigm\nfor applications of opinion dynamics. Experimental results on both synthetic\nand real-world datasets demonstrate UniGO's effectiveness in capturing complex\nopinion formation processes and predicting future evolution. The pretrained\nmodel also shows strong generalization capability, validating the benefits of\nusing synthetic data to boost real-world performance.\n", "date": "2025-02-17", "categories": []}
{"id": "2409.18997", "title": "PropaInsight: Toward Deeper Understanding of Propaganda in Terms of\n  Techniques, Appeals, and Intent", "abstract": "  Propaganda plays a critical role in shaping public opinion and fueling\ndisinformation. While existing research primarily focuses on identifying\npropaganda techniques, it lacks the ability to capture the broader motives and\nthe impacts of such content. To address these challenges, we introduce\npropainsight, a conceptual framework grounded in foundational social science\nresearch, which systematically dissects propaganda into techniques, arousal\nappeals, and underlying intent. propainsight offers a more granular\nunderstanding of how propaganda operates across different contexts.\nAdditionally, we present propagaze, a novel dataset that combines\nhuman-annotated data with high-quality synthetic data generated through a\nmeticulously designed pipeline. Our experiments show that off-the-shelf LLMs\nstruggle with propaganda analysis, but training with propagaze significantly\nimproves performance. Fine-tuned Llama-7B-Chat achieves 203.4% higher text span\nIoU in technique identification and 66.2% higher BertScore in appeal analysis\ncompared to 1-shot GPT-4-Turbo. Moreover, propagaze complements limited\nhuman-annotated data in data-sparse and cross-domain scenarios, showing its\npotential for comprehensive and generalizable propaganda analysis.\n", "date": "2024-09-19", "categories": []}
{"id": "2501.16235", "title": "Echoes of Discord: Forecasting Hater Reactions to Counterspeech", "abstract": "  Hate speech (HS) erodes the inclusiveness of online users and propagates\nnegativity and division. Counterspeech has been recognized as a way to mitigate\nthe harmful consequences. While some research has investigated the impact of\nuser-generated counterspeech on social media platforms, few have examined and\nmodeled haters' reactions toward counterspeech, despite the immediate\nalteration of haters' attitudes being an important aspect of counterspeech.\nThis study fills the gap by analyzing the impact of counterspeech from the\nhater's perspective, focusing on whether the counterspeech leads the hater to\nreenter the conversation and if the reentry is hateful. We compile the Reddit\nEchoes of Hate dataset (ReEco), which consists of triple-turn conversations\nfeaturing haters' reactions, to assess the impact of counterspeech. To predict\nhaters' behaviors, we employ two strategies: a two-stage reaction predictor and\na three-way classifier. The linguistic analysis sheds insights on the language\nof counterspeech to hate eliciting different haters' reactions. Experimental\nresults demonstrate that the 3-way classification model outperforms the\ntwo-stage reaction predictor, which first predicts reentry and then determines\nthe reentry type. We conclude the study with an assessment showing the most\ncommon errors identified by the best-performing model.\n", "date": "2025-01-27", "categories": []}
{"id": "2502.05331", "title": "Fine-Tuned LLMs are \"Time Capsules\" for Tracking Societal Bias Through\n  Books", "abstract": "  Books, while often rich in cultural insights, can also mirror societal biases\nof their eras - biases that Large Language Models (LLMs) may learn and\nperpetuate during training. We introduce a novel method to trace and quantify\nthese biases using fine-tuned LLMs. We develop BookPAGE, a corpus comprising\n593 fictional books across seven decades (1950-2019), to track bias evolution.\nBy fine-tuning LLMs on books from each decade and using targeted prompts, we\nexamine shifts in biases related to gender, sexual orientation, race, and\nreligion. Our findings indicate that LLMs trained on decade-specific books\nmanifest biases reflective of their times, with both gradual trends and notable\nshifts. For example, model responses showed a progressive increase in the\nportrayal of women in leadership roles (from 8% to 22%) from the 1950s to\n2010s, with a significant uptick in the 1990s (from 4% to 12%), possibly\naligning with third-wave feminism. Same-sex relationship references increased\nmarkedly from the 1980s to 2000s (from 0% to 10%), mirroring growing LGBTQ+\nvisibility. Concerningly, negative portrayals of Islam rose sharply in the\n2000s (26% to 38%), likely reflecting post-9/11 sentiments. Importantly, we\ndemonstrate that these biases stem mainly from the books' content and not the\nmodels' architecture or initial training. Our study offers a new perspective on\nsocietal bias trends by bridging AI, literary studies, and social science\nresearch.\n", "date": "2025-02-07", "categories": []}
{"id": "2502.08888", "title": "LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance\n  Detection with Social Context Information", "abstract": "  The proliferation of misinformation, such as rumors on social media, has\ndrawn significant attention, prompting various expressions of stance among\nusers. Although rumor detection and stance detection are distinct tasks, they\ncan complement each other. Rumors can be identified by cross-referencing\nstances in related posts, and stances are influenced by the nature of the\nrumor. However, existing stance detection methods often require post-level\nstance annotations, which are costly to obtain. We propose a novel LLM-enhanced\nMIL approach to jointly predict post stance and claim class labels, supervised\nsolely by claim labels, using an undirected microblog propagation model. Our\nweakly supervised approach relies only on bag-level labels of claim veracity,\naligning with multi-instance learning (MIL) principles. To achieve this, we\ntransform the multi-class problem into multiple MIL-based binary classification\nproblems. We then employ a discriminative attention layer to aggregate the\noutputs from these classifiers into finer-grained classes. Experiments\nconducted on three rumor datasets and two stance datasets demonstrate the\neffectiveness of our approach, highlighting strong connections between rumor\nveracity and expressed stances in responding posts. Our method shows promising\nperformance in joint rumor and stance detection compared to the\nstate-of-the-art methods.\n", "date": "2025-02-13", "categories": []}
{"id": "2307.07367", "title": "Are Large Language Models a Threat to Digital Public Goods? Evidence\n  from Activity on Stack Overflow", "abstract": "  Large language models like ChatGPT efficiently provide users with information\nabout various topics, presenting a potential substitute for searching the web\nand asking people for help online. But since users interact privately with the\nmodel, these models may drastically reduce the amount of publicly available\nhuman-generated data and knowledge resources. This substitution can present a\nsignificant problem in securing training data for future models. In this work,\nwe investigate how the release of ChatGPT changed human-generated open data on\nthe web by analyzing the activity on Stack Overflow, the leading online Q\\&A\nplatform for computer programming. We find that relative to its Russian and\nChinese counterparts, where access to ChatGPT is limited, and to similar forums\nfor mathematics, where ChatGPT is less capable, activity on Stack Overflow\nsignificantly decreased. A difference-in-differences model estimates a 16\\%\ndecrease in weekly posts on Stack Overflow. This effect increases in magnitude\nover time, and is larger for posts related to the most widely used programming\nlanguages. Posts made after ChatGPT get similar voting scores than before,\nsuggesting that ChatGPT is not merely displacing duplicate or low-quality\ncontent. These results suggest that more users are adopting large language\nmodels to answer questions and they are better substitutes for Stack Overflow\nfor languages for which they have more training data. Using models like ChatGPT\nmay be more efficient for solving certain programming problems, but its\nwidespread adoption and the resulting shift away from public exchange on the\nweb will limit the open data people and models can learn from in the future.\n", "date": "2023-07-14", "categories": []}
{"id": "2502.05704", "title": "Rethinking Word Similarity: Semantic Similarity through Classification\n  Confusion", "abstract": "  Word similarity has many applications to social science and cultural\nanalytics tasks like measuring meaning change over time and making sense of\ncontested terms. Yet traditional similarity methods based on cosine similarity\nbetween word embeddings cannot capture the context-dependent, asymmetrical,\npolysemous nature of semantic similarity. We propose a new measure of\nsimilarity, Word Confusion, that reframes semantic similarity in terms of\nfeature-based classification confusion. Word Confusion is inspired by Tversky's\nsuggestion that similarity features be chosen dynamically. Here we train a\nclassifier to map contextual embeddings to word identities and use the\nclassifier confusion (the probability of choosing a confounding word c instead\nof the correct target word t) as a measure of the similarity of c and t. The\nset of potential confounding words acts as the chosen features. Our method is\ncomparable to cosine similarity in matching human similarity judgments across\nseveral datasets (MEN, WirdSim353, and SimLex), and can measure similarity\nusing predetermined features of interest. We demonstrate our model's ability to\nmake use of dynamic features by applying it to test a hypothesis about changes\nin the 18th C. meaning of the French word \"revolution\" from popular to state\naction during the French Revolution. We hope this reimagining of semantic\nsimilarity will inspire the development of new tools that better capture the\nmulti-faceted and dynamic nature of language, advancing the fields of\ncomputational social science and cultural analytics and beyond.\n", "date": "2025-02-08", "categories": []}
{"id": "2502.04655", "title": "Before It's Too Late: A State Space Model for the Early Prediction of\n  Misinformation and Disinformation Engagement", "abstract": "  In today's digital age, conspiracies and information campaigns can emerge\nrapidly and erode social and democratic cohesion. While recent deep learning\napproaches have made progress in modeling engagement through language and\npropagation models, they struggle with irregularly sampled data and early\ntrajectory assessment. We present IC-Mamba, a novel state space model that\nforecasts social media engagement by modeling interval-censored data with\nintegrated temporal embeddings. Our model excels at predicting engagement\npatterns within the crucial first 15-30 minutes of posting (RMSE 0.118-0.143),\nenabling rapid assessment of content reach. By incorporating interval-censored\nmodeling into the state space framework, IC-Mamba captures fine-grained\ntemporal dynamics of engagement growth, achieving a 4.72% improvement over\nstate-of-the-art across multiple engagement metrics (likes, shares, comments,\nand emojis). Our experiments demonstrate IC-Mamba's effectiveness in\nforecasting both post-level dynamics and broader narrative patterns (F1\n0.508-0.751 for narrative-level predictions). The model maintains strong\npredictive performance across extended time horizons, successfully forecasting\nopinion-level engagement up to 28 days ahead using observation windows of 3-10\ndays. These capabilities enable earlier identification of potentially\nproblematic content, providing crucial lead time for designing and implementing\ncountermeasures. Code is available at: https://github.com/ltian678/ic-mamba. An\ninteractive dashboard demonstrating our results is available at:\nhttps://ic-mamba.behavioral-ds.science.\n", "date": "2025-02-07", "categories": []}
{"id": "2502.05032", "title": "News about Global North considered Truthful! The Geo-political Veracity\n  Gradient in Global South News", "abstract": "  While there has been much research into developing AI techniques for fake\nnews detection aided by various benchmark datasets, it has often been pointed\nout that fake news in different geo-political regions traces different\ncontours. In this work we uncover, through analytical arguments and empirical\nevidence, the existence of an important characteristic in news originating from\nthe Global South viz., the geo-political veracity gradient. In particular, we\nshow that Global South news about topics from Global North -- such as news from\nan Indian news agency on US elections -- tend to be less likely to be fake.\nObserving through the prism of the political economy of fake news creation, we\nposit that this pattern could be due to the relative lack of monetarily aligned\nincentives in producing fake news about a different region than the regional\nremit of the audience. We provide empirical evidence for this from benchmark\ndatasets. We also empirically analyze the consequences of this effect in\napplying AI-based fake news detection models for fake news AI trained on one\nregion within another regional context. We locate our work within emerging\ncritical scholarship on geo-political biases within AI in general, particularly\nwith AI usage in fake news identification; we hope our insight into the\ngeo-political veracity gradient could help steer fake news AI scholarship\ntowards positively impacting Global South societies.\n", "date": "2025-02-07", "categories": []}
{"id": "2407.12771", "title": "The Role of Network and Identity in the Diffusion of Hashtags", "abstract": "  The diffusion of culture online is theorized to be influenced by many\ninteracting social factors (e.g., network and identity). However, most existing\ncomputational cascade models consider just a single factor (e.g., network or\nidentity). This work offers a new framework for teasing apart the mechanisms\nunderlying hashtag cascades. We curate a new dataset of 1,337 hashtags\nrepresenting cultural innovation online, develop a 10-factor evaluation\nframework for comparing empirical and simulated cascades, and show that a\ncombined network+identity model better simulates hashtag cascades than network-\nor identity-only counterfactuals. We also explore heterogeneity in performance:\nWhile a combined network+identity model best predicts the popularity of\ncascades, a network-only model best predicts cascade growth and an\nidentity-only model best predicts adopter composition. The network+identity\nmodel has the highest comparative advantage among hashtags used for expressing\nracial or regional identity and talking about sports or news. In fact, we are\nable to predict what combination of network and/or identity best models each\nhashtag and use this to further improve performance. Our results show the\nutility of models incorporating the interactions of network, identity, and\nother social factors in the diffusion of hashtags in social media.\n", "date": "2024-07-17", "categories": []}
{"id": "2501.18441", "title": "From Public Square to Echo Chamber: The Fragmentation of Online\n  Discourse", "abstract": "  This paper examines how social media algorithms and filter bubbles contribute\nto the fragmentation of online discourse, fostering ideological divides and\nundermining shared understanding. Drawing on Michael Sandels philosophical\nemphasis on community and shared values, the study explores how digital\nplatforms amplify discrimination discourse including sexism, racism,\nxenophobia, ableism, homophobia, and religious intolerance during periods of\nheightened societal tension. By analyzing the dynamics of digital communities,\nthe research highlights mechanisms driving the emergence and evolution of\ndiscourse fragments in response to real world events. The findings reveal how\nsocial media structures exacerbate polarization, restrict cross group dialogue,\nand erode the collective reasoning essential for a just society. This study\nsituates philosophical perspectives within a computational analysis of social\nmedia interactions, offering a nuanced understanding of the challenges posed by\nfragmented discourse in the digital age.\n", "date": "2025-01-30", "categories": []}
{"id": "2502.01450", "title": "Simulating Rumor Spreading in Social Networks using LLM Agents", "abstract": "  With the rise of social media, misinformation has become increasingly\nprevalent, fueled largely by the spread of rumors. This study explores the use\nof Large Language Model (LLM) agents within a novel framework to simulate and\nanalyze the dynamics of rumor propagation across social networks. To this end,\nwe design a variety of LLM-based agent types and construct four distinct\nnetwork structures to conduct these simulations. Our framework assesses the\neffectiveness of different network constructions and agent behaviors in\ninfluencing the spread of rumors. Our results demonstrate that the framework\ncan simulate rumor spreading across more than one hundred agents in various\nnetworks with thousands of edges. The evaluations indicate that network\nstructure, personas, and spreading schemes can significantly influence rumor\ndissemination, ranging from no spread to affecting 83\\% of agents in\niterations, thereby offering a realistic simulation of rumor spread in social\nnetworks.\n", "date": "2025-02-03", "categories": []}
{"id": "2501.06274", "title": "Polarized Patterns of Language Toxicity and Sentiment of Debunking Posts\n  on Social Media", "abstract": "  The rise of misinformation and fake news in online political discourse poses\nsignificant challenges to democratic processes and public engagement. While\ndebunking efforts aim to counteract misinformation and foster fact-based\ndialogue, these discussions often involve language toxicity and emotional\npolarization. We examined over 86 million debunking tweets and more than 4\nmillion Reddit debunking comments to investigate the relationship between\nlanguage toxicity, pessimism, and social polarization in debunking efforts.\nFocusing on discussions of the 2016 and 2020 U.S. presidential elections and\nthe QAnon conspiracy theory, our analysis reveals three key findings: (1)\nperipheral participants (1-degree users) play a disproportionate role in\nshaping toxic discourse, driven by lower community accountability and emotional\nexpression; (2) platform mechanisms significantly influence polarization, with\nTwitter amplifying partisan differences and Reddit fostering higher overall\ntoxicity due to its structured, community-driven interactions; and (3) a\nnegative correlation exists between language toxicity and pessimism, with\nincreased interaction reducing toxicity, especially on Reddit. We show that\nplatform architecture affects informational complexity of user interactions,\nwith Twitter promoting concentrated, uniform discourse and Reddit encouraging\ndiverse, complex communication. Our findings highlight the importance of user\nengagement patterns, platform dynamics, and emotional expressions in shaping\npolarization in debunking discourse. This study offers insights for\npolicymakers and platform designers to mitigate harmful effects and promote\nhealthier online discussions, with implications for understanding\nmisinformation, hate speech, and political polarization in digital\nenvironments.\n", "date": "2025-01-10", "categories": []}
{"id": "2501.18640", "title": "Divergent Emotional Patterns in Disinformation on Social Media? An\n  Analysis of Tweets and TikToks about the DANA in Valencia", "abstract": "  This study investigates the dissemination of disinformation on social media\nplatforms during the DANA event (DANA is a Spanish acronym for Depresion\nAislada en Niveles Altos, translating to high-altitude isolated depression)\nthat resulted in extremely heavy rainfall and devastating floods in Valencia,\nSpain, on October 29, 2024. We created a novel dataset of 650 TikTok and X\nposts, which was manually annotated to differentiate between disinformation and\ntrustworthy content. Additionally, a Few-Shot annotation approach with GPT-4o\nachieved substantial agreement (Cohen's kappa of 0.684) with manual labels.\nEmotion analysis revealed that disinformation on X is mainly associated with\nincreased sadness and fear, while on TikTok, it correlates with higher levels\nof anger and disgust. Linguistic analysis using the LIWC dictionary showed that\ntrustworthy content utilizes more articulate and factual language, whereas\ndisinformation employs negations, perceptual words, and personal anecdotes to\nappear credible. Audio analysis of TikTok posts highlighted distinct patterns:\ntrustworthy audios featured brighter tones and robotic or monotone narration,\npromoting clarity and credibility, while disinformation audios leveraged tonal\nvariation, emotional depth, and manipulative musical elements to amplify\nengagement. In detection models, SVM+TF-IDF achieved the highest F1-Score,\nexcelling with limited data. Incorporating audio features into\nroberta-large-bne improved both Accuracy and F1-Score, surpassing its text-only\ncounterpart and SVM in Accuracy. GPT-4o Few-Shot also performed well,\nshowcasing the potential of large language models for automated disinformation\ndetection. These findings demonstrate the importance of leveraging both textual\nand audio features for improved disinformation detection on multimodal\nplatforms like TikTok.\n", "date": "2025-01-28", "categories": []}
{"id": "2309.14355", "title": "PopBERT. Detecting populism and its host ideologies in the German\n  Bundestag", "abstract": "  The rise of populism concerns many political scientists and practitioners,\nyet the detection of its underlying language remains fragmentary. This paper\naims to provide a reliable, valid, and scalable approach to measure populist\nstances. For that purpose, we created an annotated dataset based on\nparliamentary speeches of the German Bundestag (2013 to 2021). Following the\nideational definition of populism, we label moralizing references to the\nvirtuous people or the corrupt elite as core dimensions of populist language.\nTo identify, in addition, how the thin ideology of populism is thickened, we\nannotate how populist statements are attached to left-wing or right-wing host\nideologies. We then train a transformer-based model (PopBERT) as a multilabel\nclassifier to detect and quantify each dimension. A battery of validation\nchecks reveals that the model has a strong predictive accuracy, provides high\nqualitative face validity, matches party rankings of expert surveys, and\ndetects out-of-sample text snippets correctly. PopBERT enables dynamic analyses\nof how German-speaking politicians and parties use populist language as a\nstrategic device. Furthermore, the annotator-level data may also be applied in\ncross-domain applications or to develop related classifiers.\n", "date": "2023-09-22", "categories": []}
{"id": "2501.16507", "title": "Characterizing Network Structure of Anti-Trans Actors on TikTok", "abstract": "  The recent proliferation of short form video social media sites such as\nTikTok has been effectively utilized for increased visibility, communication,\nand community connection amongst trans/nonbinary creators online. However,\nthese same platforms have also been exploited by right-wing actors targeting\ntrans/nonbinary people, enabling such anti-trans actors to efficiently spread\nhate speech and propaganda. Given these divergent groups, what are the\ndifferences in network structure between anti-trans and pro-trans communities\non TikTok, and to what extent do they amplify the effects of anti-trans\ncontent? In this paper, we collect a sample of TikTok videos containing pro and\nanti-trans content, and develop a taxonomy of trans related sentiment to enable\nthe classification of content on TikTok, and ultimately analyze the reply\nnetwork structures of pro-trans and anti-trans communities. In order to\naccomplish this, we worked with hired expert data annotators from the\ntrans/nonbinary community in order to generate a sample of highly accurately\nlabeled data. From this subset, we utilized a novel classification pipeline\nleveraging Retrieval-Augmented Generation (RAG) with annotated examples and\ntaxonomy definitions to classify content into pro-trans, anti-trans, or neutral\ncategories. We find that incorporating our taxonomy and its logics into our\nclassification engine results in improved ability to differentiate trans\nrelated content, and that Results from network analysis indicate many\ninteractions between posters of pro-trans and anti-trans content exist, further\ndemonstrating targeting of trans individuals, and demonstrating the need for\nbetter content moderation tools\n", "date": "2025-01-27", "categories": []}
{"id": "2310.15772", "title": "Analyzing User Characteristics of Hate Speech Spreaders on Social Media", "abstract": "  Hate speech on social media threatens the mental and physical well-being of\nindividuals and contributes to real-world violence. Resharing is an important\ndriver behind the spread of hate speech on social media. Yet, little is known\nabout who reshares hate speech and what their characteristics are. In this\npaper, we analyze the role of user characteristics in hate speech resharing\nacross different types of hate speech (e.g., political hate). For this, we\nproceed as follows: First, we cluster hate speech posts using large language\nmodels to identify different types of hate speech. Then we model the effects of\nuser attributes on users' probability to reshare hate speech using an\nexplainable machine learning model. To do so, we apply debiasing to control for\nselection bias in our observational social media data and further control for\nthe latent vulnerability of users to hate speech. We find that, all else equal,\nusers with fewer followers, fewer friends, fewer posts, and older accounts\nshare more hate speech. This shows that users with little social influence tend\nto share more hate speech. Further, we find substantial heterogeneity across\ndifferent types of hate speech. For example, racist and misogynistic hate is\nspread mostly by users with little social influence. In contrast, political\nanti-Trump and anti-right-wing hate is reshared by users with larger social\ninfluence. Overall, understanding the factors that drive users to share hate\nspeech is crucial for detecting individuals at risk of engaging in harmful\nbehavior and for designing effective mitigation strategies.\n", "date": "2023-10-24", "categories": []}
{"id": "2408.07873", "title": "Words Matter: Reducing Stigma in Online Conversations about Substance\n  Use with Large Language Models", "abstract": "  Stigma is a barrier to treatment for individuals struggling with substance\nuse disorders (SUD), which leads to significantly lower treatment engagement\nrates. With only 7% of those affected receiving any form of help, societal\nstigma not only discourages individuals with SUD from seeking help but isolates\nthem, hindering their recovery journey and perpetuating a cycle of shame and\nself-doubt. This study investigates how stigma manifests on social media,\nparticularly Reddit, where anonymity can exacerbate discriminatory behaviors.\nWe analyzed over 1.2 million posts, identifying 3,207 that exhibited\nstigmatizing language towards people who use substances (PWUS). Using Informed\nand Stylized LLMs, we develop a model for de-stigmatization of these\nexpressions into empathetic language, resulting in 1,649 reformed phrase pairs.\nOur paper contributes to the field by proposing a computational framework for\nanalyzing stigma and destigmatizing online content, and delving into the\nlinguistic features that propagate stigma towards PWUS. Our work not only\nenhances understanding of stigma's manifestations online but also provides\npractical tools for fostering a more supportive digital environment for those\naffected by SUD. Code and data will be made publicly available upon acceptance.\n", "date": "2024-08-15", "categories": []}
{"id": "2501.15283", "title": "Are Human Interactions Replicable by Generative Agents? A Case Study on\n  Pronoun Usage in Hierarchical Interactions", "abstract": "  As Large Language Models (LLMs) advance in their capabilities, researchers\nhave increasingly employed them for social simulation. In this paper, we\ninvestigate whether interactions among LLM agents resemble those of humans.\nSpecifically, we focus on the pronoun usage difference between leaders and\nnon-leaders, examining whether the simulation would lead to human-like pronoun\nusage patterns during the LLMs' interactions. Our evaluation reveals the\nsignificant discrepancies between LLM-based simulations and human pronoun\nusage, with prompt-based or specialized agents failing to demonstrate\nhuman-like pronoun usage patterns. In addition, we reveal that even if LLMs\nunderstand the human pronoun usage patterns, they fail to demonstrate them in\nthe actual interaction process. Our study highlights the limitations of social\nsimulations based on LLM agents, urging caution in using such social simulation\nin practitioners' decision-making process.\n", "date": "2025-01-25", "categories": []}
{"id": "2501.13682", "title": "Collective Memory and Narrative Cohesion: A Computational Study of\n  Palestinian Refugee Oral Histories in Lebanon", "abstract": "  This study uses the Palestinian Oral History Archive (POHA) to investigate\nhow Palestinian refugee groups in Lebanon sustain a cohesive collective memory\nof the Nakba through shared narratives. Grounded in Halbwachs' theory of group\nmemory, we employ statistical analysis of pairwise similarity of narratives,\nfocusing on the influence of shared gender and location. We use textual\nrepresentation and semantic embeddings of narratives to represent the\ninterviews themselves. Our analysis demonstrates that shared origin is a\npowerful determinant of narrative similarity across thematic keywords,\nlandmarks, and significant figures, as well as in semantic embeddings of the\nnarratives. Meanwhile, shared residence fosters cohesion, with its impact\nsignificantly amplified when paired with shared origin. Additionally, women's\nnarratives exhibit heightened thematic cohesion, particularly in recounting\nexperiences of the British occupation, underscoring the gendered dimensions of\nmemory formation. This research deepens the understanding of collective memory\nin diasporic settings, emphasizing the critical role of oral histories in\nsafeguarding Palestinian identity and resisting erasure.\n", "date": "2025-01-23", "categories": []}
{"id": "2410.17257", "title": "Code-Driven Law NO, Normware SI!", "abstract": "  With the digitalization of society, the interest, the debates and the\nresearch efforts concerning \"code\", \"law\", \"artificial intelligence\", and their\nvarious relationships, have been widely increasing. Yet, most arguments\nprimarily focus on contemporary computational methods and artifacts\n(inferential models constructed via machine-learning methods, rule-based\nsystems, smart contracts), rather than attempting to identify more fundamental\nmechanisms. Aiming to go beyond this conceptual limitation, this paper\nintroduces and elaborates on \"normware\" as an explicit additional stance --\ncomplementary to software and hardware -- for the interpretation and the design\nof artificial devices. By means of a few examples, I will argue that a\nnormware-centred perspective provides a more adequate abstraction to study and\ndesign interactions between computational systems and human institutions, and\nmay help with the design and development of technical interventions within\nwider socio-technical views.\n", "date": "2024-10-05", "categories": []}
{"id": "2412.01265", "title": "Indexing Economic Fluctuation Narratives from Keiki Watchers Survey", "abstract": "  In this paper, we design indices of economic fluctuation narratives derived\nfrom economic surveys. Companies, governments, and investors rely on key\nmetrics like GDP and industrial production indices to predict economic trends.\nHowever, they have yet to effectively leverage the wealth of information\ncontained in economic text, such as causal relationships, in their economic\nforecasting. Therefore, we design indices of economic fluctuation from economic\nsurveys by using our previously proposed narrative framework. From the\nevaluation results, it is observed that the proposed indices had a stronger\ncorrelation with cumulative lagging diffusion index than other types of\ndiffusion indices.\n", "date": "2024-12-02", "categories": []}
{"id": "2412.04505", "title": "Achieving Semantic Consistency: Contextualized Word Representations for\n  Political Text Analysis", "abstract": "  Accurately interpreting words is vital in political science text analysis;\nsome tasks require assuming semantic stability, while others aim to trace\nsemantic shifts. Traditional static embeddings, like Word2Vec effectively\ncapture long-term semantic changes but often lack stability in short-term\ncontexts due to embedding fluctuations caused by unbalanced training data.\nBERT, which features transformer-based architecture and contextual embeddings,\noffers greater semantic consistency, making it suitable for analyses in which\nstability is crucial. This study compares Word2Vec and BERT using 20 years of\nPeople's Daily articles to evaluate their performance in semantic\nrepresentations across different timeframes. The results indicate that BERT\noutperforms Word2Vec in maintaining semantic stability and still recognizes\nsubtle semantic variations. These findings support BERT's use in text analysis\ntasks that require stability, where semantic changes are not assumed, offering\na more reliable foundation than static alternatives.\n", "date": "2024-12-03", "categories": []}
{"id": "2501.12336", "title": "FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with\n  XLM-Roberta Sentence Embeddings and Deep Neural Regression", "abstract": "  This paper presents results of our system for CoMeDi Shared Task, focusing on\nSubtask 2: Disagreement Ranking. Our system leverages sentence embeddings\ngenerated by the paraphrase-xlm-r-multilingual-v1 model, combined with a deep\nneural regression model incorporating batch normalization and dropout for\nimproved generalization. By predicting the mean of pairwise judgment\ndifferences between annotators, our method explicitly targets disagreement\nranking, diverging from traditional \"gold label\" aggregation approaches. We\noptimized our system with a customized architecture and training procedure,\nachieving competitive performance in Spearman correlation against mean\ndisagreement labels. Our results highlight the importance of robust embeddings,\neffective model architecture, and careful handling of judgment differences for\nranking disagreement in multilingual contexts. These findings provide insights\ninto the use of contextualized representations for ordinal judgment tasks and\nopen avenues for further refinement of disagreement prediction models.\n", "date": "2025-01-21", "categories": []}
{"id": "2501.09950", "title": "Sympathy over Polarization: A Computational Discourse Analysis of Social\n  Media Posts about the July 2024 Trump Assassination Attempt", "abstract": "  On July 13, 2024, at the Trump rally in Pennsylvania, someone attempted to\nassassinate Republican Presidential Candidate Donald Trump. This attempt\nsparked a large-scale discussion on social media. We collected posts from X\n(formerly known as Twitter) one week before and after the assassination attempt\nand aimed to model the short-term effects of such a ``shock'' on public\nopinions and discussion topics. Specifically, our study addresses three key\nquestions: first, we investigate how public sentiment toward Donald Trump\nshifts over time and across regions (RQ1) and examine whether the assassination\nattempt itself significantly affects public attitudes, independent of the\nexisting political alignments (RQ2). Finally, we explore the major themes in\nonline conversations before and after the crisis, illustrating how discussion\ntopics evolved in response to this politically charged event (RQ3). By\nintegrating large language model-based sentiment analysis,\ndifference-in-differences modeling, and topic modeling techniques, we find that\nfollowing the attempt the public response was broadly sympathetic to Trump\nrather than polarizing, despite baseline ideological and regional disparities.\n", "date": "2025-01-17", "categories": []}
{"id": "2501.09719", "title": "Comparative Insights from 12 Machine Learning Models in Extracting\n  Economic Ideology from Political Text", "abstract": "  This study conducts a systematic assessment of the capabilities of 12 machine\nlearning models and model variations in detecting economic ideology. As an\nevaluation benchmark, I use manifesto data spanning six elections in the United\nKingdom and pre-annotated by expert and crowd coders. The analysis assesses the\nperformance of several generative, fine-tuned, and zero-shot models at the\ngranular and aggregate levels. The results show that generative models such as\nGPT-4o and Gemini 1.5 Flash consistently outperform other models against all\nbenchmarks. However, they pose issues of accessibility and resource\navailability. Fine-tuning yielded competitive performance and offers a reliable\nalternative through domain-specific optimization. But its dependency on\ntraining data severely limits scalability. Zero-shot models consistently face\ndifficulties with identifying signals of economic ideology, often resulting in\nnegative associations with human coding. Using general knowledge for the\ndomain-specific task of ideology scaling proved to be unreliable. Other key\nfindings include considerable within-party variation, fine-tuning benefiting\nfrom larger training data, and zero-shot's sensitivity to prompt content. The\nassessments include the strengths and limitations of each model and derive\nbest-practices for automated analyses of political content.\n", "date": "2025-01-16", "categories": []}
{"id": "2501.06265", "title": "AgoraSpeech: A multi-annotated comprehensive dataset of political\n  discourse through the lens of humans and AI", "abstract": "  Political discourse datasets are important for gaining political insights,\nanalyzing communication strategies or social science phenomena. Although\nnumerous political discourse corpora exist, comprehensive, high-quality,\nannotated datasets are scarce. This is largely due to the substantial manual\neffort, multidisciplinarity, and expertise required for the nuanced annotation\nof rhetorical strategies and ideological contexts. In this paper, we present\nAgoraSpeech, a meticulously curated, high-quality dataset of 171 political\nspeeches from six parties during the Greek national elections in 2023. The\ndataset includes annotations (per paragraph) for six natural language\nprocessing (NLP) tasks: text classification, topic identification, sentiment\nanalysis, named entity recognition, polarization and populism detection. A\ntwo-step annotation was employed, starting with ChatGPT-generated annotations\nand followed by exhaustive human-in-the-loop validation. The dataset was\ninitially used in a case study to provide insights during the pre-election\nperiod. However, it has general applicability by serving as a rich source of\ninformation for political and social scientists, journalists, or data\nscientists, while it can be used for benchmarking and fine-tuning NLP and large\nlanguage models (LLMs).\n", "date": "2025-01-09", "categories": []}
{"id": "2501.06834", "title": "LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural\n  Agents", "abstract": "  Despite its importance, studying economic behavior across diverse, non-WEIRD\n(Western, Educated, Industrialized, Rich, and Democratic) populations presents\nsignificant challenges. We address this issue by introducing a novel\nmethodology that uses Large Language Models (LLMs) to create synthetic cultural\nagents (SCAs) representing these populations. We subject these SCAs to classic\nbehavioral experiments, including the dictator and ultimatum games. Our results\ndemonstrate substantial cross-cultural variability in experimental behavior.\nNotably, for populations with available data, SCAs' behaviors qualitatively\nresemble those of real human subjects. For unstudied populations, our method\ncan generate novel, testable hypotheses about economic behavior. By integrating\nAI into experimental economics, this approach offers an effective and ethical\nmethod to pilot experiments and refine protocols for hard-to-reach populations.\nOur study provides a new tool for cross-cultural economic studies and\ndemonstrates how LLMs can help experimental behavioral research.\n", "date": "2025-01-12", "categories": []}
{"id": "2501.02132", "title": "A hybrid marketplace of ideas", "abstract": "  The convergence of humans and artificial intelligence systems introduces new\ndynamics into the cultural and intellectual landscape. Complementing emerging\ncultural evolution concepts such as machine culture, AI agents represent a\nsignificant techno-sociological development, particularly within the\nanthropological study of Web3 as a community focused on decentralization\nthrough blockchain. Despite their growing presence, the cultural significance\nof AI agents remains largely unexplored in academic literature. Toward this\nend, we conceived hybrid netnography, a novel interdisciplinary approach that\nexamines the cultural and intellectual dynamics within digital ecosystems by\nanalyzing the interactions and contributions of both human and AI agents as\nco-participants in shaping narratives, ideas, and cultural artifacts. We argue\nthat, within the Web3 community on the social media platform X, these agents\nchallenge traditional notions of participation and influence in public\ndiscourse, creating a hybrid marketplace of ideas, a conceptual space where\nhuman and AI generated ideas coexist and compete for attention. We examine the\ncurrent state of AI agents in idea generation, propagation, and engagement,\npositioning their role as cultural agents through the lens of memetics and\nencouraging further inquiry into their cultural and societal impact.\nAdditionally, we address the implications of this paradigm for privacy,\nintellectual property, and governance, highlighting the societal and legal\nchallenges of integrating AI agents into the hybrid marketplace of ideas.\n", "date": "2025-01-03", "categories": []}
{"id": "2501.05423", "title": "Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese\n  Micro-bloggers", "abstract": "  Studying public sentiment during crises is crucial for understanding how\nopinions and sentiments shift, resulting in polarized societies. We study\nWeibo, the most popular microblogging site in China, using posts made during\nthe outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19\nstage, the outbreak stage, and the early stage of epidemic prevention. We use\nLlama 3 8B, a Large Language Model, to analyze users' sentiments on the\nplatform by classifying them into positive, negative, sarcastic, and neutral\ncategories. Analyzing sentiment shifts on Weibo provides insights into how\nsocial events and government actions influence public opinion. This study\ncontributes to understanding the dynamics of social sentiments during health\ncrises, fulfilling a gap in sentiment analysis for Chinese platforms. By\nexamining these dynamics, we aim to offer valuable perspectives on digital\ncommunication's role in shaping society's responses during unprecedented global\nchallenges.\n", "date": "2025-01-09", "categories": []}
{"id": "2501.03370", "title": "Advanced Machine Learning Techniques for Social Support Detection on\n  Social Media", "abstract": "  The widespread use of social media highlights the need to understand its\nimpact, particularly the role of online social support. This study uses a\ndataset focused on online social support, which includes binary and multiclass\nclassifications of social support content on social media. The classification\nof social support is divided into three tasks. The first task focuses on\ndistinguishing between supportive and non-supportive. The second task aims to\nidentify whether the support is directed toward an individual or a group. The\nthird task categorizes the specific type of social support, grouping it into\ncategories such as Nation, LGBTQ, Black people, Women, Religion, and Other (if\nit does not fit into the previously mentioned categories). To address data\nimbalances in these tasks, we employed K-means clustering for balancing the\ndataset and compared the results with the original unbalanced data. Using\nadvanced machine learning techniques, including transformers and zero-shot\nlearning approaches with GPT3, GPT4, and GPT4-o, we predict social support\nlevels in various contexts. The effectiveness of the dataset is evaluated using\nbaseline models across different learning approaches, with transformer-based\nmethods demonstrating superior performance. Additionally, we achieved a 0.4\\%\nincrease in the macro F1 score for the second task and a 0.7\\% increase for the\nthird task, compared to previous work utilizing traditional machine learning\nwith psycholinguistic and unigram-based TF-IDF values.\n", "date": "2025-01-06", "categories": []}
{"id": "2501.02482", "title": "Decoding News Bias: Multi Bias Detection in News Articles", "abstract": "  News Articles provides crucial information about various events happening in\nthe society but they unfortunately come with different kind of biases. These\nbiases can significantly distort public opinion and trust in the media, making\nit essential to develop techniques to detect and address them. Previous works\nhave majorly worked towards identifying biases in particular domains e.g.,\nPolitical, gender biases. However, more comprehensive studies are needed to\ndetect biases across diverse domains. Large language models (LLMs) offer a\npowerful way to analyze and understand natural language, making them ideal for\nconstructing datasets and detecting these biases. In this work, we have\nexplored various biases present in the news articles, built a dataset using\nLLMs and present results obtained using multiple detection techniques. Our\napproach highlights the importance of broad-spectrum bias detection and offers\nnew insights for improving the integrity of news articles.\n", "date": "2025-01-05", "categories": []}
{"id": "2501.00004", "title": "NewsHomepages: Homepage Layouts Capture Information Prioritization\n  Decisions", "abstract": "  Information prioritization plays an important role in how humans perceive and\nunderstand the world. Homepage layouts serve as a tangible proxy for this\nprioritization. In this work, we present NewsHomepages, a large dataset of over\n3,000 new website homepages (including local, national and topic-specific\noutlets) captured twice daily over a three-year period. We develop models to\nperform pairwise comparisons between news items to infer their relative\nsignificance. To illustrate that modeling organizational hierarchies has\nbroader implications, we applied our models to rank-order a collection of local\ncity council policies passed over a ten-year period in San Francisco, assessing\ntheir \"newsworthiness\". Our findings lay the groundwork for leveraging implicit\norganizational cues to deepen our understanding of information prioritization.\n", "date": "2024-11-21", "categories": []}
{"id": "2501.00782", "title": "Navigating Nuance: In Quest for Political Truth", "abstract": "  This study investigates the several nuanced rationales for countering the\nrise of political bias. We evaluate the performance of the Llama-3 (70B)\nlanguage model on the Media Bias Identification Benchmark (MBIB), based on a\nnovel prompting technique that incorporates subtle reasons for identifying\npolitical leaning. Our findings underscore the challenges of detecting\npolitical bias and highlight the potential of transfer learning methods to\nenhance future models. Through our framework, we achieve a comparable\nperformance with the supervised and fully fine-tuned ConvBERT model, which is\nthe state-of-the-art model, performing best among other baseline models for the\npolitical bias task on MBIB. By demonstrating the effectiveness of our\napproach, we contribute to the development of more robust tools for mitigating\nthe spread of misinformation and polarization. Our codes and dataset are made\npublicly available in github.\n", "date": "2025-01-01", "categories": []}
{"id": "2207.08112", "title": "United States Politicians' Tone Became More Negative with 2016 Primary\n  Campaigns", "abstract": "  There is a widespread belief that the tone of US political language has\nbecome more negative recently, in particular when Donald Trump entered\npolitics. At the same time, there is disagreement as to whether Trump changed\nor merely continued previous trends. To date, data-driven evidence regarding\nthese questions is scarce, partly due to the difficulty of obtaining a\ncomprehensive, longitudinal record of politicians' utterances. Here we apply\npsycholinguistic tools to a novel, comprehensive corpus of 24 million quotes\nfrom online news attributed to 18,627 US politicians in order to analyze how\nthe tone of US politicians' language evolved between 2008 and 2020. We show\nthat, whereas the frequency of negative emotion words had decreased\ncontinuously during Obama's tenure, it suddenly and lastingly increased with\nthe 2016 primary campaigns, by 1.6 pre-campaign standard deviations, or 8% of\nthe pre-campaign mean, in a pattern that emerges across parties. The effect\nsize drops by 40% when omitting Trump's quotes, and by 50% when averaging over\nspeakers rather than quotes, implying that prominent speakers, and Trump in\nparticular, have disproportionately, though not exclusively, contributed to the\nrise in negative language. This work provides the first large-scale data-driven\nevidence of a drastic shift toward a more negative political tone following\nTrump's campaign start as a catalyst, with important implications for the\ndebate about the state of US politics.\n", "date": "2022-07-17", "categories": []}
{"id": "2410.11043", "title": "Personality Differences Drive Conversational Dynamics: A\n  High-Dimensional NLP Approach", "abstract": "  This paper investigates how the topical flow of dyadic conversations emerges\nover time and how differences in interlocutors' personality traits contribute\nto this topical flow. Leveraging text embeddings, we map the trajectories of $N\n= 1655$ conversations between strangers into a high-dimensional space. Using\nnonlinear projections and clustering, we then identify when each interlocutor\nenters and exits various topics. Differences in conversational flow are\nquantified via $\\textit{topic entropy}$, a summary measure of the \"spread\" of\ntopics covered during a conversation, and $\\textit{linguistic alignment}$, a\ntime-varying measure of the cosine similarity between interlocutors'\nembeddings. Our findings suggest that interlocutors with a larger difference in\nthe personality dimension of openness influence each other to spend more time\ndiscussing a wider range of topics and that interlocutors with a larger\ndifference in extraversion experience a larger decrease in linguistic alignment\nthroughout their conversation. We also examine how participants' affect\n(emotion) changes from before to after a conversation, finding that a larger\ndifference in extraversion predicts a larger difference in affect change and\nthat a greater topic entropy predicts a larger affect increase. This work\ndemonstrates how communication research can be advanced through the use of\nhigh-dimensional NLP methods and identifies personality difference as an\nimportant driver of social influence.\n", "date": "2024-10-14", "categories": []}
{"id": "2404.03021", "title": "Blessing or curse? A survey on the Impact of Generative AI on Fake News", "abstract": "  Fake news significantly influence our society. They impact consumers, voters,\nand many other societal groups. While Fake News exist for a centuries,\nGenerative AI brings fake news on a new level. It is now possible to automate\nthe creation of masses of high-quality individually targeted Fake News. On the\nother end, Generative AI can also help detecting Fake News. Both fields are\nyoung but developing fast.\n  This survey provides a comprehensive examination of the research and\npractical use of Generative AI for Fake News detection and creation in 2024.\nFollowing the Structured Literature Survey approach, the paper synthesizes\ncurrent results in the following topic clusters 1) enabling technologies, 2)\ncreation of Fake News, 3) case study social media as most relevant distribution\nchannel, 4) detection of Fake News, and 5) deepfakes as upcoming technology.\n  The article also identifies current challenges and open issues.\n", "date": "2024-04-03", "categories": []}
{"id": "2412.16925", "title": "Quantifying Public Response to COVID-19 Events: Introducing the\n  Community Sentiment and Engagement Index", "abstract": "  This study introduces the Community Sentiment and Engagement Index (CSEI),\ndeveloped to capture nuanced public sentiment and engagement variations on\nsocial media, particularly in response to major events related to COVID-19.\nConstructed with diverse sentiment indicators, CSEI integrates features like\nengagement, daily post count, compound sentiment, fine-grain sentiments (fear,\nsurprise, joy, sadness, anger, disgust, and neutral), readability,\noffensiveness, and domain diversity. Each component is systematically weighted\nthrough a multi-step Principal Component Analysis (PCA)-based framework,\nprioritizing features according to their variance contributions across temporal\nsentiment shifts. This approach dynamically adjusts component importance,\nenabling CSEI to precisely capture high-sensitivity shifts in public sentiment.\nThe development of CSEI showed statistically significant correlations with its\nconstituent features, underscoring internal consistency and sensitivity to\nspecific sentiment dimensions. CSEI's responsiveness was validated using a\ndataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15\nmajor events, including the WHO's declaration of COVID-19 as a pandemic, the\nfirst reported cases of COVID-19 across different countries, national\nlockdowns, vaccine developments, and crucial public health measures. Cumulative\nchanges in CSEI revealed prominent peaks and valleys aligned with these events,\nindicating significant patterns in public sentiment across different phases of\nthe pandemic. Pearson correlation analysis further confirmed a statistically\nsignificant relationship between CSEI daily fluctuations and these events (p =\n0.0428), highlighting the capacity of CSEI to infer and interpret shifts in\npublic sentiment and engagement in response to major events related to\nCOVID-19.\n", "date": "2024-12-22", "categories": []}
{"id": "2412.14414", "title": "In-Group Love, Out-Group Hate: A Framework to Measure Affective\n  Polarization via Contentious Online Discussions", "abstract": "  Affective polarization, the emotional divide between ideological groups\nmarked by in-group love and out-group hate, has intensified in the United\nStates, driving contentious issues like masking and lockdowns during the\nCOVID-19 pandemic. Despite its societal impact, existing models of opinion\nchange fail to account for emotional dynamics nor offer methods to quantify\naffective polarization robustly and in real-time. In this paper, we introduce a\ndiscrete choice model that captures decision-making within affectively\npolarized social networks and propose a statistical inference method estimate\nkey parameters -- in-group love and out-group hate -- from social media data.\nThrough empirical validation from online discussions about the COVID-19\npandemic, we demonstrate that our approach accurately captures real-world\npolarization dynamics and explains the rapid emergence of a partisan gap in\nattitudes towards masking and lockdowns. This framework allows for tracking\naffective polarization across contentious issues has broad implications for\nfostering constructive online dialogues in digital spaces.\n", "date": "2024-12-18", "categories": []}
{"id": "2412.15098", "title": "A Cross-Domain Study of the Use of Persuasion Techniques in Online\n  Disinformation", "abstract": "  Disinformation, irrespective of domain or language, aims to deceive or\nmanipulate public opinion, typically through employing advanced persuasion\ntechniques. Qualitative and quantitative research on the weaponisation of\npersuasion techniques in disinformation has been mostly topic-specific (e.g.,\nCOVID-19) with limited cross-domain studies, resulting in a lack of\ncomprehensive understanding of these strategies. This study employs a\nstate-of-the-art persuasion technique classifier to conduct a large-scale,\nmulti-domain analysis of the role of 16 persuasion techniques in disinformation\nnarratives. It shows how different persuasion techniques are employed\ndisproportionately in different disinformation domains. We also include a\ndetailed case study on climate change disinformation, highlighting how\nlinguistic, psychological, and cultural factors shape the adaptation of\npersuasion strategies to fit unique thematic contexts.\n", "date": "2024-12-19", "categories": []}
{"id": "2411.12196", "title": "A More Advanced Group Polarization Measurement Approach Based on\n  LLM-Based Agents and Graphs", "abstract": "  Group polarization is an important research direction in social media content\nanalysis, attracting many researchers to explore this field. Therefore, how to\neffectively measure group polarization has become a critical topic. Measuring\ngroup polarization on social media presents several challenges that have not\nyet been addressed by existing solutions. First, social media group\npolarization measurement involves processing vast amounts of text, which poses\na significant challenge for information extraction. Second, social media texts\noften contain hard-to-understand content, including sarcasm, memes, and\ninternet slang. Additionally, group polarization research focuses on holistic\nanalysis, while texts is typically fragmented. To address these challenges, we\ndesigned a solution based on a multi-agent system and used a graph-structured\nCommunity Sentiment Network (CSN) to represent polarization states.\nFurthermore, we developed a metric called Community Opposition Index (COI)\nbased on the CSN to quantify polarization. Finally, we tested our multi-agent\nsystem through a zero-shot stance detection task and achieved outstanding\nresults. In summary, the proposed approach has significant value in terms of\nusability, accuracy, and interpretability.\n", "date": "2024-11-19", "categories": []}
{"id": "2412.07144", "title": "Political Actor Agent: Simulating Legislative System for Roll Call Votes\n  Prediction with Large Language Models", "abstract": "  Predicting roll call votes through modeling political actors has emerged as a\nfocus in quantitative political science and computer science. Widely used\nembedding-based methods generate vectors for legislators from diverse data sets\nto predict legislative behaviors. However, these methods often contend with\nchallenges such as the need for manually predefined features, reliance on\nextensive training data, and a lack of interpretability. Achieving more\ninterpretable predictions under flexible conditions remains an unresolved\nissue. This paper introduces the Political Actor Agent (PAA), a novel\nagent-based framework that utilizes Large Language Models to overcome these\nlimitations. By employing role-playing architectures and simulating legislative\nsystem, PAA provides a scalable and interpretable paradigm for predicting\nroll-call votes. Our approach not only enhances the accuracy of predictions but\nalso offers multi-view, human-understandable decision reasoning, providing new\ninsights into political actor behaviors. We conducted comprehensive experiments\nusing voting records from the 117-118th U.S. House of Representatives,\nvalidating the superior performance and interpretability of PAA. This study not\nonly demonstrates PAA's effectiveness but also its potential in political\nscience research.\n", "date": "2024-12-10", "categories": []}
{"id": "2412.08868", "title": "Words of War: Exploring the Presidential Rhetorical Arsenal with Deep\n  Learning", "abstract": "  In political discourse and geopolitical analysis, national leaders words hold\nprofound significance, often serving as harbingers of pivotal historical\nmoments. From impassioned rallying cries to calls for caution, presidential\nspeeches preceding major conflicts encapsulate the multifaceted dynamics of\ndecision-making at the apex of governance. This project aims to use deep\nlearning techniques to decode the subtle nuances and underlying patterns of US\npresidential rhetoric that may signal US involvement in major wars. While\naccurate classification is desirable, we seek to take a step further and\nidentify discriminative features between the two classes (i.e. interpretable\nlearning).\n  Through an interdisciplinary fusion of machine learning and historical\ninquiry, we aspire to unearth insights into the predictive capacity of neural\nnetworks in discerning the preparatory rhetoric of US presidents preceding war.\nIndeed, as the venerable Prussian General and military theorist Carl von\nClausewitz admonishes, War is not merely an act of policy but a true political\ninstrument, a continuation of political intercourse carried on with other means\n(Clausewitz, 1832).\n", "date": "2024-12-12", "categories": []}
{"id": "2412.09335", "title": "Does Low Spoilage Under Cold Conditions Foster Cultural Complexity\n  During the Foraging Era? -- A Theoretical and Computational Inquiry", "abstract": "  Human cultural complexity did not arise in a vacuum. Scholars in the\nhumanities and social sciences have long debated how ecological factors, such\nas climate and resource availability, enabled early hunter-gatherers to\nallocate time and energy beyond basic subsistence tasks. This paper presents a\nformal, interdisciplinary approach that integrates theoretical modeling with\ncomputational methods to examine whether conditions that allow lower spoilage\nof stored food, often associated with colder climates and abundant large fauna,\ncould indirectly foster the emergence of cultural complexity. Our contribution\nis twofold. First, we propose a mathematical framework that relates spoilage\nrates, yield levels, resource management skills, and cultural activities. Under\nthis framework, we prove that lower spoilage and adequate yields reduce the\nfrequency of hunting, thus freeing substantial time for cultural pursuits.\nSecond, we implement a reinforcement learning simulation, inspired by\nengineering optimization techniques, to validate the theoretical predictions.\nBy training agents in different $(Y,p)$ environments, where $Y$ is yield and\n$p$ is the probability of daily spoilage, we observe patterns consistent with\nthe theoretical model: stable conditions with lower spoilage strongly correlate\nwith increased cultural complexity. While we do not claim to replicate\nprehistoric social realities directly, our results suggest that ecologically\nstable niches provided a milieu in which cultural forms could germinate and\nevolve. This study, therefore, offers an integrative perspective that unites\nhumanistic inquiries into the origins of culture with the formal rigor and\nexploratory power of computational modeling.\n", "date": "2024-12-12", "categories": []}
{"id": "2412.09578", "title": "DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial\n  NEtworks and Semantic Topic classification", "abstract": "  The emergence of the COVID-19 pandemic resulted in a significant rise in the\nspread of misinformation on online platforms such as Twitter. Oftentimes this\ngrowth is blamed on the idea of the \"echo chamber.\" However, the behavior said\nto characterize these echo chambers exists in two dimensions. The first is in a\nuser's social interactions, where they are said to stick with the same clique\nof like-minded users. The second is in the content of their posts, where they\nare said to repeatedly espouse homogeneous ideas. In this study, we link the\ntwo by using Twitter's network of retweets to study social interactions and\ntopic modeling to study tweet content. In order to measure the diversity of a\nuser's interactions over time, we develop a novel metric to track the speed at\nwhich they travel through the social network. The application of these analysis\nmethods to misinformation-focused data from the pandemic demonstrates\ncorrelation between social behavior and tweet content. We believe this\ncorrelation supports the common intuition about how antisocial users behave,\nand further suggests that it holds even in subcommunities already rife with\nmisinformation.\n", "date": "2024-12-12", "categories": []}
{"id": "2410.12622", "title": "From Measurement Instruments to Data: Leveraging Theory-Driven Synthetic\n  Training Data for Classifying Social Constructs", "abstract": "  Computational text classification is a challenging task, especially for\nmulti-dimensional social constructs. Recently, there has been increasing\ndiscussion that synthetic training data could enhance classification by\noffering examples of how these constructs are represented in texts. In this\npaper, we systematically examine the potential of theory-driven synthetic\ntraining data for improving the measurement of social constructs. In\nparticular, we explore how researchers can transfer established knowledge from\nmeasurement instruments in the social sciences, such as survey scales or\nannotation codebooks, into theory-driven generation of synthetic data. Using\ntwo studies on measuring sexism and political topics, we assess the added value\nof synthetic training data for fine-tuning text classification models. Although\nthe results of the sexism study were less promising, our findings demonstrate\nthat synthetic data can be highly effective in reducing the need for labeled\ndata in political topic classification. With only a minimal drop in\nperformance, synthetic data allows for substituting large amounts of labeled\ndata. Furthermore, theory-driven synthetic data performed markedly better than\ndata generated without conceptual information in mind.\n", "date": "2024-10-16", "categories": []}
{"id": "2412.06833", "title": "Detecting Fake News on Social Media: A Novel Reliability Aware\n  Machine-Crowd Hybrid Intelligence-Based Method", "abstract": "  Fake news on social media platforms poses a significant threat to societal\nsystems, underscoring the urgent need for advanced detection methods. The\nexisting detection methods can be divided into machine intelligence-based,\ncrowd intelligence-based, and hybrid intelligence-based methods. Among them,\nhybrid intelligence-based methods achieve the best performance but fail to\nconsider the reliability issue in detection. In light of this, we propose a\nnovel Reliability Aware Hybrid Intelligence (RAHI) method for fake news\ndetection. Our method comprises three integral modules. The first module\nemploys a Bayesian deep learning model to capture the inherent reliability\nwithin machine intelligence. The second module uses an Item Response Theory\n(IRT)-based user response aggregation to account for the reliability in crowd\nintelligence. The third module introduces a new distribution fusion mechanism,\nwhich takes the distributions derived from both machine and crowd intelligence\nas input, and outputs a fused distribution that provides predictions along with\nthe associated reliability. The experiments on the Weibo dataset demonstrate\nthe advantages of our method. This study contributes to the research field with\na novel RAHI-based method, and the code is shared at\nhttps://github.com/Kangwei-g/RAHI. This study has practical implications for\nthree key stakeholders: internet users, online platform managers, and the\ngovernment.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.07042", "title": "Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job\n  Advertisements", "abstract": "  The rapid advancement of Generative AI (Gen AI) technologies, particularly\ntools like ChatGPT, is significantly impacting the labor market by reshaping\njob roles and skill requirements. This study examines the demand for\nChatGPT-related skills in the U.S. labor market by analyzing job advertisements\ncollected from major job platforms between May and December 2023. Using text\nmining and topic modeling techniques, we extracted and analyzed the Gen\nAI-related skills that employers are hiring for. Our analysis identified five\ndistinct ChatGPT-related skill sets: general familiarity, creative content\ngeneration, marketing, advanced functionalities (such as prompt engineering),\nand product development. In addition, the study provides insights into job\nattributes such as occupation titles, degree requirements, salary ranges, and\nother relevant job characteristics. These findings highlight the increasing\nintegration of Gen AI across various industries, emphasizing the growing need\nfor both foundational knowledge and advanced technical skills. The study offers\nvaluable insights into the evolving demands of the labor market, as employers\nseek candidates equipped to leverage generative AI tools to improve\nproductivity, streamline processes, and drive innovation.\n", "date": "2024-12-09", "categories": []}
{"id": "2401.12986", "title": "Crowdsourced Adaptive Surveys", "abstract": "  Public opinion surveys are vital for informing democratic decision-making,\nbut responding to rapidly evolving information environments and measuring\nbeliefs within niche communities can be challenging for traditional survey\nmethods. This paper introduces a crowdsourced adaptive survey methodology\n(CSAS) that unites advances in natural language processing and adaptive\nalgorithms to generate question banks that evolve with user input. The CSAS\nmethod converts open-ended text provided by participants into survey items and\napplies a multi-armed bandit algorithm to determine which questions should be\nprioritized in the survey. The method's adaptive nature allows for the\nexploration of new survey questions, while imposing minimal costs in survey\nlength. Applications in the domains of Latino information environments,\nnational issue importance, and local politics showcase CSAS's ability to\nidentify topics that might otherwise escape the notice of survey researchers. I\nconclude by highlighting CSAS's potential to bridge conceptual gaps between\nresearchers and participants in survey research.\n", "date": "2024-01-16", "categories": []}
{"id": "2412.06087", "title": "Ethnography and Machine Learning: Synergies and New Directions", "abstract": "  Ethnography (social scientific methods that illuminate how people understand,\nnavigate and shape the real world contexts in which they live their lives) and\nmachine learning (computational techniques that use big data and statistical\nlearning models to perform quantifiable tasks) are each core to contemporary\nsocial science. Yet these tools have remained largely separate in practice.\nThis chapter draws on a growing body of scholarship that argues that\nethnography and machine learning can be usefully combined, particularly for\nlarge comparative studies. Specifically, this paper (a) explains the value (and\nchallenges) of using machine learning alongside qualitative field research for\ncertain types of projects, (b) discusses recent methodological trends to this\neffect, (c) provides examples that illustrate workflow drawn from several large\nprojects, and (d) concludes with a roadmap for enabling productive coevolution\nof field methods and machine learning.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.04859", "title": "Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate", "abstract": "  The rapid spread of rumors on social media platforms during breaking events\nseverely hinders the dissemination of the truth. Previous studies reveal that\nthe lack of annotated resources hinders the direct detection of unforeseen\nbreaking events not covered in yesterday's news. Leveraging large language\nmodels (LLMs) for rumor detection holds significant promise. However, it is\nchallenging for LLMs to provide comprehensive responses to complex or\ncontroversial issues due to limited diversity. In this work, we propose the\nStance Separated Multi-Agent Debate (S2MAD) to address this issue.\nSpecifically, we firstly introduce Stance Separation, categorizing comments as\neither supporting or opposing the original claim. Subsequently, claims are\nclassified as subjective or objective, enabling agents to generate reasonable\ninitial viewpoints with different prompt strategies for each type of claim.\nDebaters then follow specific instructions through multiple rounds of debate to\nreach a consensus. If a consensus is not reached, a judge agent evaluates the\nopinions and delivers a final verdict on the claim's veracity. Extensive\nexperiments conducted on two real-world datasets demonstrate that our proposed\nmodel outperforms state-of-the-art methods in terms of performance and\neffectively improves the performance of LLMs in breaking event rumor detection.\n", "date": "2024-12-06", "categories": []}
{"id": "2412.04236", "title": "A History of Philosophy in Colombia through Topic Modelling", "abstract": "  Data-driven approaches to philosophy have emerged as a valuable tool for\nstudying the history of the discipline. However, most studies in this area have\nfocused on a limited number of journals from specific regions and subfields. We\nexpand the scope of this research by applying dynamic topic modelling\ntechniques to explore the history of philosophy in Colombia and Latin America.\nOur study examines the Colombian philosophy journal Ideas y Valores, founded in\n1951 and currently one of the most influential academic philosophy journals in\nthe region. By analyzing the evolution of topics across the journal's history,\nwe identify various trends and specific dynamics in philosophical discourse\nwithin the Colombian and Latin American context. Our findings reveal that the\nmost prominent topics are value theory (including ethics, political philosophy,\nand aesthetics), epistemology, and the philosophy of science. We also trace the\nevolution of articles focusing on the historical and interpretive aspects of\nphilosophical texts, and we note a notable emphasis on German philosophers such\nas Kant, Husserl, and Hegel on various topics throughout the journal's\nlifetime. Additionally, we investigate whether articles with a historical focus\nhave decreased over time due to editorial pressures. Our analysis suggests no\nsignificant decline in such articles. Finally, we propose ideas for extending\nthis research to other Latin American journals and suggest improvements for\nnatural language processing workflows in non-English languages.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.03563", "title": "From Individual to Society: A Survey on Social Simulation Driven by\n  Large Language Model-based Agents", "abstract": "  Traditional sociological research often relies on human participation, which,\nthough effective, is expensive, challenging to scale, and with ethical\nconcerns. Recent advancements in large language models (LLMs) highlight their\npotential to simulate human behavior, enabling the replication of individual\nresponses and facilitating studies on many interdisciplinary studies. In this\npaper, we conduct a comprehensive survey of this field, illustrating the recent\nprogress in simulation driven by LLM-empowered agents. We categorize the\nsimulations into three types: (1) Individual Simulation, which mimics specific\nindividuals or demographic groups; (2) Scenario Simulation, where multiple\nagents collaborate to achieve goals within specific contexts; and (3) Society\nSimulation, which models interactions within agent societies to reflect the\ncomplexity and variety of real-world dynamics. These simulations follow a\nprogression, ranging from detailed individual modeling to large-scale societal\nphenomena. We provide a detailed discussion of each simulation type, including\nthe architecture or key components of the simulation, the classification of\nobjectives or scenarios and the evaluation method. Afterward, we summarize\ncommonly used datasets and benchmarks. Finally, we discuss the trends across\nthese three types of simulation. A repository for the related sources is at\n{\\url{https://github.com/FudanDISC/SocialAgent}}.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.02369", "title": "The Impact of Featuring Comments in Online Discussions", "abstract": "  A widespread moderation strategy by online news platforms is to feature what\nthe platform deems high quality comments, usually called editor picks or\nfeatured comments. In this paper, we compare online discussions of news\narticles in which certain comments are featured, versus discussions in which no\ncomments are featured. We measure the impact of featuring comments on the\ndiscussion, by estimating and comparing the quality of discussions from the\nperspective of the user base and the platform itself. Our analysis shows that\nthe impact on discussion quality is limited. However, we do observe an increase\nin discussion activity after the first comments are featured by moderators,\nsuggesting that the moderation strategy might be used to increase user\nengagement and to postpone the natural decline in user activity over time.\n", "date": "2024-12-03", "categories": []}
{"id": "2405.08254", "title": "Detecting Fallacies in Climate Misinformation: A Technocognitive\n  Approach to Identifying Misleading Argumentation", "abstract": "  Misinformation about climate change is a complex societal issue requiring\nholistic, interdisciplinary solutions at the intersection between technology\nand psychology. One proposed solution is a \"technocognitive\" approach,\ninvolving the synthesis of psychological and computer science research.\nPsychological research has identified that interventions in response to\nmisinformation require both fact-based (e.g., factual explanations) and\ntechnique-based (e.g., explanations of misleading techniques) content. However,\nlittle progress has been made on documenting and detecting fallacies in climate\nmisinformation. In this study, we apply a previously developed critical\nthinking methodology for deconstructing climate misinformation, in order to\ndevelop a dataset mapping different types of climate misinformation to\nreasoning fallacies. This dataset is used to train a model to detect fallacies\nin climate misinformation. Our study shows F1 scores that are 2.5 to 3.5 better\nthan previous works. The fallacies that are easiest to detect include fake\nexperts and anecdotal arguments, while fallacies that require background\nknowledge, such as oversimplification, misrepresentation, and slothful\ninduction, are relatively more difficult to detect. This research lays the\ngroundwork for development of solutions where automatically detected climate\nmisinformation can be countered with generative technique-based corrections.\n", "date": "2024-05-14", "categories": []}
{"id": "2412.00011", "title": "The use of knowledge in open-ended systems", "abstract": "  Economists model knowledge use and acquisition as a cause-and-effect calculus\nassociating observations made by a decision-maker about their world with\npossible underlying causes. Knowledge models are well-established for static\ncontexts, but not for contexts of innovative and unbounded change. We develop a\nrepresentation of knowledge use and acquisition in open-ended evolutionary\nsystems and demonstrate its primary results, including that observers embedded\nin open-ended evolutionary systems can agree to disagree and that their ability\nto theorize about their systems is fundamentally local and constrained to their\nframe of reference what we call frame relativity. The results of our framework\nformalize local knowledge use, the many-selves interpretation of reasoning\nthrough time, and motivate the emergence of nonlogical modes of reasoning like\ninstitutional and aesthetic codes.\n", "date": "2024-11-13", "categories": []}
{"id": "2401.15479", "title": "Navigating the Post-API Dilemma | Search Engine Results Pages Present a\n  Biased View of Social Media Data", "abstract": "  Recent decisions to discontinue access to social media APIs are having\ndetrimental effects on Internet research and the field of computational social\nscience as a whole. This lack of access to data has been dubbed the Post-API\nera of Internet research. Fortunately, popular search engines have the means to\ncrawl, capture, and surface social media data on their Search Engine Results\nPages (SERP) if provided the proper search query, and may provide a solution to\nthis dilemma. In the present work we ask: does SERP provide a complete and\nunbiased sample of social media data? Is SERP a viable alternative to direct\nAPI-access? To answer these questions, we perform a comparative analysis\nbetween (Google) SERP results and nonsampled data from Reddit and Twitter/X. We\nfind that SERP results are highly biased in favor of popular posts; against\npolitical, pornographic, and vulgar posts; are more positive in their\nsentiment; and have large topical gaps. Overall, we conclude that SERP is not a\nviable alternative to social media API access.\n", "date": "2024-01-27", "categories": []}
{"id": "2309.13065", "title": "Personality Profiling: How informative are social media profiles in\n  predicting personal information?", "abstract": "  Personality profiling has been utilised by companies for targeted\nadvertising, political campaigns and public health campaigns. However, the\naccuracy and versatility of such models remains relatively unknown. Here we\nexplore the extent to which peoples' online digital footprints can be used to\nprofile their Myers-Briggs personality type. We analyse and compare four\nmodels: logistic regression, naive Bayes, support vector machines (SVMs) and\nrandom forests. We discover that a SVM model achieves the best accuracy of\n20.95% for predicting a complete personality type. However, logistic regression\nmodels perform only marginally worse and are significantly faster to train and\nperform predictions. Moreover, we develop a statistical framework for assessing\nthe importance of different sets of features in our models. We discover some\nfeatures to be more informative than others in the Intuitive/Sensory (p =\n0.032) and Thinking/Feeling (p = 0.019) models. Many labelled datasets present\nsubstantial class imbalances of personal characteristics on social media,\nincluding our own. We therefore highlight the need for attentive consideration\nwhen reporting model performance on such datasets and compare a number of\nmethods to fix class-imbalance problems.\n", "date": "2023-09-15", "categories": []}
{"id": "2408.06120", "title": "How ChatGPT Changed the Media's Narratives on AI: A Semi-Automated\n  Narrative Analysis Through Frame Semantics", "abstract": "  We perform a mixed-method frame semantics-based analysis on a dataset of more\nthan 49,000 sentences collected from 5846 news articles that mention AI. The\ndataset covers the twelve-month period centred around the launch of OpenAI's\nchatbot ChatGPT and is collected from the most visited open-access\nEnglish-language news publishers. Our findings indicate that during the six\nmonths succeeding the launch, media attention rose tenfold$\\unicode{x2014}$from\nalready historically high levels. During this period, discourse has become\nincreasingly centred around experts and political leaders, and AI has become\nmore closely associated with dangers and risks. A deeper review of the data\nalso suggests a qualitative shift in the types of threat AI is thought to\nrepresent, as well as the anthropomorphic qualities ascribed to it.\n", "date": "2024-08-12", "categories": []}
{"id": "2407.21753", "title": "Characterizing User Archetypes and Discussions on Scored.co", "abstract": "  In recent years, the proliferation of social platforms has drastically\ntransformed the way individuals interact, organize, and share information. In\nthis scenario, we experience an unprecedented increase in the scale and\ncomplexity of interactions and, at the same time, little to no research about\nsome fringe social platforms. In this paper, we present a multi-dimensional\nframework for characterizing nodes and hyperedges in social hypernetworks, with\na focus on the understudied alt-right platform Scored.co. Our approach\nintegrates the possibility of studying higher-order interactions, thanks to the\nhypernetwork representation, and various node features such as user activity,\nsentiment, and toxicity, with the aim to define distinct user archetypes and\nunderstand their roles within the network. Utilizing a comprehensive dataset\nfrom Scored.co, we analyze the dynamics of these archetypes over time and\nexplore their interactions and influence within the community. The framework's\nversatility allows for detailed analysis of both individual user behaviors and\nbroader social structures. Our findings highlight the importance of\nhigher-order interactions in understanding social dynamics, offering new\ninsights into the roles and behaviors that emerge in complex online\nenvironments.\n", "date": "2024-07-31", "categories": []}
{"id": "2411.15068", "title": "Locating the Leading Edge of Cultural Change", "abstract": "  Measures of textual similarity and divergence are increasingly used to study\ncultural change. But which measures align, in practice, with social evidence\nabout change? We apply three different representations of text (topic models,\ndocument embeddings, and word-level perplexity) to three different corpora\n(literary studies, economics, and fiction). In every case, works by\nhighly-cited authors and younger authors are textually ahead of the curve. We\ndon't find clear evidence that one representation of text is to be preferred\nover the others. But alignment with social evidence is strongest when texts are\nrepresented through the top quartile of passages, suggesting that a text's\nimpact may depend more on its most forward-looking moments than on sustaining a\nhigh level of innovation throughout.\n", "date": "2024-11-22", "categories": []}
{"id": "2210.04359", "title": "Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years\n  of German Parliamentary Debates", "abstract": "  Solidarity is a crucial concept to understand social relations in societies.\nIn this paper, we explore fine-grained solidarity frames to study solidarity\ntowards women and migrants in German parliamentary debates between 1867 and\n2022. Using 2,864 manually annotated text snippets (with a cost exceeding 18k\nEuro), we evaluate large language models (LLMs) like Llama 3, GPT-3.5, and\nGPT-4. We find that GPT-4 outperforms other LLMs, approaching human annotation\nquality. Using GPT-4, we automatically annotate more than 18k further instances\n(with a cost of around 500 Euro) across 155 years and find that solidarity with\nmigrants outweighs anti-solidarity but that frequencies and solidarity types\nshift over time. Most importantly, group-based notions of (anti-)solidarity\nfade in favor of compassionate solidarity, focusing on the vulnerability of\nmigrant groups, and exchange-based anti-solidarity, focusing on the lack of\n(economic) contribution. Our study highlights the interplay of historical\nevents, socio-economic needs, and political ideologies in shaping migration\ndiscourse and social cohesion. We also show that powerful LLMs, if carefully\nprompted, can be cost-effective alternatives to human annotation for hard\nsocial scientific tasks.\n", "date": "2022-10-09", "categories": []}
{"id": "2411.14073", "title": "Meaning at the Planck scale? Contextualized word embeddings for doing\n  history, philosophy, and sociology of science", "abstract": "  This paper explores the potential of contextualized word embeddings (CWEs) as\na new tool in the history, philosophy, and sociology of science (HPSS) for\nstudying contextual and evolving meanings of scientific concepts. Using the\nterm \"Planck\" as a test case, I evaluate five BERT-based models with varying\ndegrees of domain-specific pretraining, including my custom model\nAstro-HEP-BERT, trained on the Astro-HEP Corpus, a dataset containing 21.84\nmillion paragraphs from 600,000 articles in astrophysics and high-energy\nphysics. For this analysis, I compiled two labeled datasets: (1) the\nAstro-HEP-Planck Corpus, consisting of 2,900 labeled occurrences of \"Planck\"\nsampled from 1,500 paragraphs in the Astro-HEP Corpus, and (2) a\nphysics-related Wikipedia dataset comprising 1,186 labeled occurrences of\n\"Planck\" across 885 paragraphs. Results demonstrate that the domain-adapted\nmodels outperform the general-purpose ones in disambiguating the target term,\npredicting its known meanings, and generating high-quality sense clusters, as\nmeasured by a novel purity indicator I developed. Additionally, this approach\nreveals semantic shifts in the target term over three decades in the unlabeled\nAstro-HEP Corpus, highlighting the emergence of the Planck space mission as a\ndominant sense. The study underscores the importance of domain-specific\npretraining for analyzing scientific language and demonstrates the\ncost-effectiveness of adapting pretrained models for HPSS research. By offering\na scalable and transferable method for modeling the meanings of scientific\nconcepts, CWEs open up new avenues for investigating the socio-historical\ndynamics of scientific discourses.\n", "date": "2024-11-21", "categories": []}
{"id": "2410.19272", "title": "Coordinated Reply Attacks in Influence Operations: Characterization and\n  Detection", "abstract": "  Coordinated reply attacks are a tactic observed in online influence\noperations and other coordinated campaigns to support or harass targeted\nindividuals, or influence them or their followers. Despite its potential to\ninfluence the public, past studies have yet to analyze or provide a methodology\nto detect this tactic. In this study, we characterize coordinated reply attacks\nin the context of influence operations on Twitter. Our analysis reveals that\nthe primary targets of these attacks are influential people such as\njournalists, news media, state officials, and politicians.\n  We propose two supervised machine-learning models, one to classify tweets to\ndetermine whether they are targeted by a reply attack, and one to classify\naccounts that reply to a targeted tweet to determine whether they are part of a\ncoordinated attack. The classifiers achieve AUC scores of 0.88 and 0.97,\nrespectively. These results indicate that accounts involved in reply attacks\ncan be detected, and the targeted accounts themselves can serve as sensors for\ninfluence operation detection.\n", "date": "2024-10-25", "categories": []}
{"id": "2402.01661", "title": "Tracing the Genealogies of Ideas with Large Language Model Embeddings", "abstract": "  In this paper, I present a novel method to detect intellectual influence\nacross a large corpus. Taking advantage of the unique affordances of large\nlanguage models in encoding semantic and structural meaning while remaining\nrobust to paraphrasing, we can search for substantively similar ideas and hints\nof intellectual influence in a computationally efficient manner. Such a method\nallows us to operationalize different levels of confidence: we can allow for\ndirect quotation, paraphrase, or speculative similarity while remaining open\nabout the limitations of each threshold. I apply an ensemble method combining\nGeneral Text Embeddings, a state-of-the-art sentence embedding method optimized\nto capture semantic content and an Abstract Meaning Representation graph\nrepresentation designed to capture structural similarities in argumentation\nstyle and the use of metaphor. I apply this method to vectorize sentences from\na corpus of roughly 400,000 nonfiction books and academic publications from the\n19th century for instances of ideas and arguments appearing in Darwin's\npublications. This functions as an initial evaluation and proof of concept; the\nmethod is not limited to detecting Darwinian ideas but is capable of detecting\nsimilarities on a large scale in a wide range of corpora and contexts.\n", "date": "2024-01-13", "categories": []}
{"id": "2403.20204", "title": "The Future of Combating Rumors? Retrieval, Discrimination, and\n  Generation", "abstract": "  Artificial Intelligence Generated Content (AIGC) technology development has\nfacilitated the creation of rumors with misinformation, impacting societal,\neconomic, and political ecosystems, challenging democracy. Current rumor\ndetection efforts fall short by merely labeling potentially misinformation\n(classification task), inadequately addressing the issue, and it is unrealistic\nto have authoritative institutions debunk every piece of information on social\nmedia. Our proposed comprehensive debunking process not only detects rumors but\nalso provides explanatory generated content to refute the authenticity of the\ninformation. The Expert-Citizen Collective Wisdom (ECCW) module we designed\naensures high-precision assessment of the credibility of information and the\nretrieval module is responsible for retrieving relevant knowledge from a\nReal-time updated debunking database based on information keywords. By using\nprompt engineering techniques, we feed results and knowledge into a LLM (Large\nLanguage Model), achieving satisfactory discrimination and explanatory effects\nwhile eliminating the need for fine-tuning, saving computational costs, and\ncontributing to debunking efforts.\n", "date": "2024-03-29", "categories": []}
{"id": "2407.09661", "title": "Bridging Dictionary: AI-Generated Dictionary of Partisan Language Use", "abstract": "  Words often carry different meanings for people from diverse backgrounds.\nToday's era of social polarization demands that we choose words carefully to\nprevent miscommunication, especially in political communication and journalism.\nTo address this issue, we introduce the Bridging Dictionary, an interactive\ntool designed to illuminate how words are perceived by people with different\npolitical views. The Bridging Dictionary includes a static, printable document\nfeaturing 796 terms with summaries generated by a large language model. These\nsummaries highlight how the terms are used distinctively by Republicans and\nDemocrats. Additionally, the Bridging Dictionary offers an interactive\ninterface that lets users explore selected words, visualizing their frequency,\nsentiment, summaries, and examples across political divides. We present a use\ncase for journalists and emphasize the importance of human agency and trust in\nfurther enhancing this tool. The deployed version of Bridging Dictionary is\navailable at https://dictionary.ccc-mit.org/.\n", "date": "2024-07-12", "categories": []}
{"id": "2411.09612", "title": "The Moral Foundations Weibo Corpus", "abstract": "  Moral sentiments expressed in natural language significantly influence both\nonline and offline environments, shaping behavioral styles and interaction\npatterns, including social media selfpresentation, cyberbullying, adherence to\nsocial norms, and ethical decision-making. To effectively measure moral\nsentiments in natural language processing texts, it is crucial to utilize\nlarge, annotated datasets that provide nuanced understanding for accurate\nanalysis and modeltraining. However, existing corpora, while valuable, often\nface linguistic limitations. To address this gap in the Chinese language\ndomain,we introduce the Moral Foundation Weibo Corpus. This corpus consists of\n25,671 Chinese comments on Weibo, encompassing six diverse topic areas. Each\ncomment is manually annotated by at least three systematically trained\nannotators based on ten moral categories derived from a grounded theory of\nmorality. To assess annotator reliability, we present the kappa testresults, a\ngold standard for measuring consistency. Additionally, we apply several the\nlatest large language models to supplement the manual annotations, conducting\nanalytical experiments to compare their performance and report baseline results\nfor moral sentiment classification.\n", "date": "2024-11-14", "categories": []}
{"id": "2310.10429", "title": "Exploiting User Comments for Early Detection of Fake News Prior to\n  Users' Commenting", "abstract": "  Both accuracy and timeliness are key factors in detecting fake news on social\nmedia. However, most existing methods encounter an accuracy-timeliness dilemma:\nContent-only methods guarantee timeliness but perform moderately because of\nlimited available information, while social con-text-based ones generally\nperform better but inevitably lead to latency because of social context\naccumulation needs. To break such a dilemma, a feasible but not well-studied\nsolution is to leverage social contexts (e.g., comments) from historical news\nfor training a detection model and apply it to newly emerging news without\nsocial contexts. This requires the model to (1) sufficiently learn helpful\nknowledge from social contexts, and (2) be well compatible with situations that\nsocial contexts are available or not. To achieve this goal, we propose to\nabsorb and parameterize useful knowledge from comments in historical news and\nthen inject it into a content-only detection model. Specifically, we design the\nComments ASsisted FakE News Detection method (CAS-FEND), which transfers useful\nknowledge from a comment-aware teacher model to a content-only student model\nand detects newly emerging news with the student model. Experiments show that\nthe CAS-FEND student model outperforms all content-only methods and even\ncomment-aware ones with 1/4 comments as inputs, demonstrating its superiority\nfor early detection.\n", "date": "2023-10-16", "categories": []}
{"id": "2403.00037", "title": "Evolving to the Future: Unseen Event Adaptive Fake News Detection on\n  Social Media", "abstract": "  With the rapid development of social media, the wide dissemination of fake\nnews on social media is increasingly threatening both individuals and society.\nOne of the unique challenges for fake news detection on social media is how to\ndetect fake news on future events. Recently, numerous fake news detection\nmodels that utilize textual information and the propagation structure of posts\nhave been proposed. Unfortunately, most of the existing approaches can hardly\nhandle this challenge since they rely heavily on event-specific features for\nprediction and cannot generalize to unseen events. To address this, we\nintroduce \\textbf{F}uture \\textbf{AD}aptive \\textbf{E}vent-based Fake news\nDetection (FADE) framework. Specifically, we train a target predictor through\nan adaptive augmentation strategy and graph contrastive learning to obtain\nhigher-quality features and make more accurate overall predictions.\nSimultaneously, we independently train an event-only predictor to obtain biased\npredictions. We further mitigate event bias by subtracting the event-only\npredictor's output from the target predictor's output to obtain the final\nprediction. Encouraging results from experiments designed to emulate real-world\nsocial media conditions validate the effectiveness of our method in comparison\nto existing state-of-the-art approaches.\n", "date": "2024-02-29", "categories": []}
{"id": "2405.10231", "title": "Influencer Cartels", "abstract": "  Social media influencers account for a growing share of marketing worldwide.\nWe demonstrate the existence of a novel form of market failure in this\nadvertising market: influencer cartels, where groups of influencers collude to\nincrease their advertising revenue by inflating their engagement. Our\ntheoretical model shows that influencer cartels can improve consumer welfare if\nthey expand social media engagement to the target audience, or reduce welfare\nif they divert engagement to less relevant audiences. Drawing on the model's\ninsights, we empirically examine influencer cartels using novel datasets and\nmachine learning tools, and derive policy implications.\n", "date": "2024-05-16", "categories": []}
{"id": "2410.24190", "title": "Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters", "abstract": "  How could LLMs influence our democracy? We investigate LLMs' political\nleanings and the potential influence of LLMs on voters by conducting multiple\nexperiments in a U.S. presidential election context. Through a voting\nsimulation, we first demonstrate 18 open- and closed-weight LLMs' political\npreference for a Democratic nominee over a Republican nominee. We show how this\nleaning towards the Democratic nominee becomes more pronounced in\ninstruction-tuned models compared to their base versions by analyzing their\nresponses to candidate-policy related questions. We further explore the\npotential impact of LLMs on voter choice by conducting an experiment with 935\nU.S. registered voters. During the experiments, participants interacted with\nLLMs (Claude-3, Llama-3, and GPT-4) over five exchanges. The experiment results\nshow a shift in voter choices towards the Democratic nominee following LLM\ninteraction, widening the voting margin from 0.7% to 4.6%, even though LLMs\nwere not asked to persuade users to support the Democratic nominee during the\ndiscourse. This effect is larger than many previous studies on the\npersuasiveness of political campaigns, which have shown minimal effects in\npresidential elections. Many users also expressed a desire for further\npolitical interaction with LLMs. Which aspects of LLM interactions drove these\nshifts in voter choice requires further study. Lastly, we explore how a safety\nmethod can make LLMs more politically neutral, while raising the question of\nwhether such neutrality is truly the path forward.\n", "date": "2024-10-31", "categories": []}
{"id": "2411.06626", "title": "Exploring social bots: A feature-based approach to improve bot detection\n  in social networks", "abstract": "  The importance of social media in our daily lives has unfortunately led to an\nincrease in the spread of misinformation, political messages and malicious\nlinks. One of the most popular ways of carrying out those activities is using\nautomated accounts, also known as bots, which makes the detection of such\naccounts a necessity. This paper addresses that problem by investigating\nfeatures based on the user account profile and its content, aiming to\nunderstand the relevance of each feature as a basis for improving future bot\ndetectors. Through an exhaustive process of research, inference and feature\nselection, we are able to surpass the state of the art on several metrics using\nclassical machine learning algorithms and identify the types of features that\nare most important in detecting automated accounts.\n", "date": "2024-11-10", "categories": []}
{"id": "2411.07163", "title": "A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis:\n  Evaluating Mental Health Sentiment on Social Media during COVID-19", "abstract": "  Monitoring public sentiment via social media is potentially helpful during\nhealth crises such as the COVID-19 pandemic. However, traditional\nfrequency-based, data-driven neural network-based approaches can miss newly\nrelevant content due to the evolving nature of language in a dynamically\nevolving environment. Human-curated symbolic knowledge sources, such as\nlexicons for standard language and slang terms, can potentially elevate social\nmedia signals in evolving language. We introduce a neurosymbolic method that\nintegrates neural networks with symbolic knowledge sources, enhancing the\ndetection and interpretation of mental health-related tweets relevant to\nCOVID-19. Our method was evaluated using a corpus of large datasets\n(approximately 12 billion tweets, 2.5 million subreddit data, and 700k news\narticles) and multiple knowledge graphs. This method dynamically adapts to\nevolving language, outperforming purely data-driven models with an F1 score\nexceeding 92\\%. This approach also showed faster adaptation to new data and\nlower computational demands than fine-tuning pre-trained large language models\n(LLMs). This study demonstrates the benefit of neurosymbolic methods in\ninterpreting text in a dynamic environment for tasks such as health\nsurveillance.\n", "date": "2024-11-11", "categories": []}
{"id": "2411.04328", "title": "Balancing Transparency and Accuracy: A Comparative Analysis of\n  Rule-Based and Deep Learning Models in Political Bias Classification", "abstract": "  The unchecked spread of digital information, combined with increasing\npolitical polarization and the tendency of individuals to isolate themselves\nfrom opposing political viewpoints, has driven researchers to develop systems\nfor automatically detecting political bias in media. This trend has been\nfurther fueled by discussions on social media. We explore methods for\ncategorizing bias in US news articles, comparing rule-based and deep learning\napproaches. The study highlights the sensitivity of modern self-learning\nsystems to unconstrained data ingestion, while reconsidering the strengths of\ntraditional rule-based systems. Applying both models to left-leaning (CNN) and\nright-leaning (FOX) news articles, we assess their effectiveness on data beyond\nthe original training and test sets.This analysis highlights each model's\naccuracy, offers a framework for exploring deep-learning explainability, and\nsheds light on political bias in US news media. We contrast the opaque\narchitecture of a deep learning model with the transparency of a linguistically\ninformed rule-based model, showing that the rule-based model performs\nconsistently across different data conditions and offers greater transparency,\nwhereas the deep learning model is dependent on the training set and struggles\nwith unseen data.\n", "date": "2024-11-07", "categories": []}
{"id": "2411.04862", "title": "Sentiment Analysis of Spanish Political Party Tweets Using Pre-trained\n  Language Models", "abstract": "  Title: Sentiment Analysis of Spanish Political Party Communications on\nTwitter Using Pre-trained Language Models\n  Authors: Chuqiao Song, Shunzhang Chen, Xinyi Cai, Hao Chen\n  Comments: 21 pages, 6 figures\n  Abstract: This study investigates sentiment patterns within Spanish political\nparty communications on Twitter by leveraging BETO and RoBERTuito, two\npre-trained language models optimized for Spanish text. Using a dataset of\ntweets from major Spanish political parties: PSOE, PP, Vox, Podemos, and\nCiudadanos, spanning 2019 to 2024, this research analyzes sentiment\ndistributions and explores the relationship between sentiment expression and\nparty ideology. The findings indicate that both models consistently identify a\npredominant Neutral sentiment across all parties, with significant variations\nin Negative and Positive sentiments that align with ideological distinctions.\nSpecifically, Vox exhibits higher levels of Negative sentiment, while PSOE\ndemonstrates relatively high Positive sentiment, supporting the hypothesis that\nemotional appeals in political messaging reflect ideological stances. This\nstudy underscores the potential of pre-trained language models for non-English\nsentiment analysis on social media, providing insights into sentiment dynamics\nthat shape public discourse within Spain's multi-party political system.\n  Keywords: Spanish politics, sentiment analysis, pre-trained language models,\nTwitter, BETO, RoBERTuito, political ideology, multi-party system\n", "date": "2024-11-07", "categories": []}
{"id": "2410.20746", "title": "ElectionSim: Massive Population Election Simulation Powered by Large\n  Language Model Driven Agents", "abstract": "  The massive population election simulation aims to model the preferences of\nspecific groups in particular election scenarios. It has garnered significant\nattention for its potential to forecast real-world social trends. Traditional\nagent-based modeling (ABM) methods are constrained by their ability to\nincorporate complex individual background information and provide interactive\nprediction results. In this paper, we introduce ElectionSim, an innovative\nelection simulation framework based on large language models, designed to\nsupport accurate voter simulations and customized distributions, together with\nan interactive platform to dialogue with simulated voters. We present a\nmillion-level voter pool sampled from social media platforms to support\naccurate individual simulation. We also introduce PPE, a poll-based\npresidential election benchmark to assess the performance of our framework\nunder the U.S. presidential election scenario. Through extensive experiments\nand analyses, we demonstrate the effectiveness and robustness of our framework\nin U.S. presidential election simulations.\n", "date": "2024-10-28", "categories": []}
{"id": "2408.08217", "title": "RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train\n  and Deploy Edge Classifiers for Computational Social Science", "abstract": "  Large language models (LLMs) have enhanced our ability to rapidly analyze and\nclassify unstructured natural language data. However, concerns regarding cost,\nnetwork limitations, and security constraints have posed challenges for their\nintegration into work processes. In this study, we adopt a systems design\napproach to employing LLMs as imperfect data annotators for downstream\nsupervised learning tasks, introducing novel system intervention measures aimed\nat improving classification performance. Our methodology outperforms\nLLM-generated labels in seven of eight tests, demonstrating an effective\nstrategy for incorporating LLMs into the design and deployment of specialized,\nsupervised learning models present in many industry use cases.\n", "date": "2024-08-15", "categories": []}
{"id": "2410.11897", "title": "A Structural Text-Based Scaling Model for Analyzing Political Discourse", "abstract": "  Scaling political actors based on their individual characteristics and\nbehavior helps profiling and grouping them as well as understanding changes in\nthe political landscape. In this paper we introduce the Structural Text-Based\nScaling (STBS) model to infer ideological positions of speakers for latent\ntopics from text data. We expand the usual Poisson factorization specification\nfor topic modeling of text data and use flexible shrinkage priors to induce\nsparsity and enhance interpretability. We also incorporate speaker-specific\ncovariates to assess their association with ideological positions. Applying\nSTBS to U.S. Senate speeches from Congress session 114, we identify immigration\nand gun violence as the most polarizing topics between the two major parties in\nCongress. Additionally, we find that, in discussions about abortion, the gender\nof the speaker significantly influences their position, with female speakers\nfocusing more on women's health. We also see that a speaker's region of origin\ninfluences their ideological position more than their religious affiliation.\n", "date": "2024-10-14", "categories": []}
{"id": "2410.21898", "title": "A Longitudinal Analysis of Racial and Gender Bias in New York Times and\n  Fox News Images and Articles", "abstract": "  The manner in which different racial and gender groups are portrayed in news\ncoverage plays a large role in shaping public opinion. As such, understanding\nhow such groups are portrayed in news media is of notable societal value, and\nhas thus been a significant endeavour in both the computer and social sciences.\nYet, the literature still lacks a longitudinal study examining both the\nfrequency of appearance of different racial and gender groups in online news\narticles, as well as the context in which such groups are discussed. To fill\nthis gap, we propose two machine learning classifiers to detect the race and\nage of a given subject. Next, we compile a dataset of 123,337 images and\n441,321 online news articles from New York Times (NYT) and Fox News (Fox), and\nexamine representation through two computational approaches. Firstly, we\nexamine the frequency and prominence of appearance of racial and gender groups\nin images embedded in news articles, revealing that racial and gender\nminorities are largely under-represented, and when they do appear, they are\nfeatured less prominently compared to majority groups. Furthermore, we find\nthat NYT largely features more images of racial minority groups compared to\nFox. Secondly, we examine both the frequency and context with which racial\nminority groups are presented in article text. This reveals the narrow scope in\nwhich certain racial groups are covered and the frequency with which different\ngroups are presented as victims and/or perpetrators in a given conflict. Taken\ntogether, our analysis contributes to the literature by providing two novel\nopen-source classifiers to detect race and age from images, and shedding light\non the racial and gender biases in news articles from venues on opposite ends\nof the American political spectrum.\n", "date": "2024-10-29", "categories": []}
{"id": "2410.23426", "title": "Social Science Meets LLMs: How Reliable Are Large Language Models in\n  Social Simulations?", "abstract": "  Large Language Models (LLMs) are increasingly employed for simulations,\nenabling applications in role-playing agents and Computational Social Science\n(CSS). However, the reliability of these simulations is under-explored, which\nraises concerns about the trustworthiness of LLMs in these applications. In\nthis paper, we aim to answer ``How reliable is LLM-based simulation?'' To\naddress this, we introduce TrustSim, an evaluation dataset covering 10\nCSS-related topics, to systematically investigate the reliability of the LLM\nsimulation. We conducted experiments on 14 LLMs and found that inconsistencies\npersist in the LLM-based simulated roles. In addition, the consistency level of\nLLMs does not strongly correlate with their general performance. To enhance the\nreliability of LLMs in simulation, we proposed Adaptive Learning Rate Based\nORPO (AdaORPO), a reinforcement learning-based algorithm to improve the\nreliability in simulation across 7 LLMs. Our research provides a foundation for\nfuture studies to explore more robust and trustworthy LLM-based simulations.\n", "date": "2024-10-30", "categories": []}
{"id": "2410.23842", "title": "Auditing Google's Search Algorithm: Measuring News Diversity Across\n  Brazil, the UK, and the US", "abstract": "  This study examines the influence of Google's search algorithm on news\ndiversity by analyzing search results in Brazil, the UK, and the US. It\nexplores how Google's system preferentially favors a limited number of news\noutlets. Utilizing algorithm auditing techniques, the research measures source\nconcentration with the Herfindahl-Hirschman Index (HHI) and Gini coefficient,\nrevealing significant concentration trends. The study underscores the\nimportance of conducting horizontal analyses across multiple search queries, as\nfocusing solely on individual results pages may obscure these patterns. Factors\nsuch as popularity, political bias, and recency were evaluated for their impact\non news rankings. Findings indicate a slight leftward bias in search outcomes\nand a preference for popular, often national outlets. This bias, combined with\na tendency to prioritize recent content, suggests that Google's algorithm may\nreinforce existing media inequalities. By analyzing the largest dataset to date\n-- 221,863 search results -- this research provides comprehensive, longitudinal\ninsights into how algorithms shape public access to diverse news sources.\n", "date": "2024-10-31", "categories": []}
{"id": "2410.22390", "title": "FNDEX: Fake News and Doxxing Detection with Explainable AI", "abstract": "  The widespread and diverse online media platforms and other internet-driven\ncommunication technologies have presented significant challenges in defining\nthe boundaries of freedom of expression. Consequently, the internet has been\ntransformed into a potential cyber weapon. Within this evolving landscape, two\nparticularly hazardous phenomena have emerged: fake news and doxxing. Although\nthese threats have been subjects of extensive scholarly analysis, the\ncrossroads where they intersect remain unexplored. This research addresses this\nconvergence by introducing a novel system. The Fake News and Doxxing Detection\nwith Explainable Artificial Intelligence (FNDEX) system leverages the\ncapabilities of three distinct transformer models to achieve high-performance\ndetection for both fake news and doxxing. To enhance data security, a rigorous\nthree-step anonymization process is employed, rooted in a pattern-based\napproach for anonymizing personally identifiable information. Finally, this\nresearch emphasizes the importance of generating coherent explanations for the\noutcomes produced by both detection models. Our experiments on realistic\ndatasets demonstrate that our system significantly outperforms the existing\nbaselines\n", "date": "2024-10-29", "categories": []}
{"id": "2410.12061", "title": "CrediRAG: Network-Augmented Credibility-Based Retrieval for\n  Misinformation Detection in Reddit", "abstract": "  Fake news threatens democracy and exacerbates the polarization and divisions\nin society; therefore, accurately detecting online misinformation is the\nfoundation of addressing this issue. We present CrediRAG, the first fake news\ndetection model that combines language models with access to a rich external\npolitical knowledge base with a dense social network to detect fake news across\nsocial media at scale. CrediRAG uses a news retriever to initially assign a\nmisinformation score to each post based on the source credibility of similar\nnews articles to the post title content. CrediRAG then improves the initial\nretrieval estimations through a novel weighted post-to-post network connected\nbased on shared commenters and weighted by the average stance of all shared\ncommenters across every pair of posts. We achieve 11% increase in the F1-score\nin detecting misinformative posts over state-of-the-art methods. Extensive\nexperiments conducted on curated real-world Reddit data of over 200,000 posts\ndemonstrate the superior performance of CrediRAG on existing baselines. Thus,\nour approach offers a more accurate and scalable solution to combat the spread\nof fake news across social media platforms.\n", "date": "2024-10-15", "categories": []}
{"id": "2410.20718", "title": "Lecture II: Communicative Justice and the Distribution of Attention", "abstract": "  Algorithmic intermediaries govern the digital public sphere through their\narchitectures, amplification algorithms, and moderation practices. In doing so,\nthey shape public communication and distribute attention in ways that were\npreviously infeasible with such subtlety, speed and scale. From misinformation\nand affective polarisation to hate speech and radicalisation, the many\npathologies of the digital public sphere attest that they could do so better.\nBut what ideals should they aim at? Political philosophy should be able to\nhelp, but existing theories typically assume that a healthy public sphere will\nspontaneously emerge if only we get the boundaries of free expression right.\nThey offer little guidance on how to intentionally constitute the digital\npublic sphere. In addition to these theories focused on expression, we need a\nfurther theory of communicative justice, targeted specifically at the\nalgorithmic intermediaries that shape communication and distribute attention.\nThis lecture argues that political philosophy urgently owes an account of how\nto govern communication in the digital public sphere, and introduces and\ndefends a democratic egalitarian theory of communicative justice.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.20720", "title": "Lecture I: Governing the Algorithmic City", "abstract": "  A century ago, John Dewey observed that '[s]team and electricity have done\nmore to alter the conditions under which men associate together than all the\nagencies which affected human relationships before our time'. In the last few\ndecades, computing technologies have had a similar effect. Political\nphilosophy's central task is to help us decide how to live together, by\nanalysing our social relations, diagnosing their failings, and articulating\nideals to guide their revision. But these profound social changes have left\nscarcely a dent in the model of social relations that (analytical) political\nphilosophers assume. This essay aims to reverse that trend. It first builds a\nmodel of our novel social relations as they are now, and as they are likely to\nevolved, and then explores how those differences affect our theories of how to\nlive together. I introduce the 'Algorithmic City', the network of\nalgorithmically-mediated social relations, then characterise the intermediary\npower by which it is governed. I show how algorithmic governance raises new\nchallenges for political philosophy concerning the justification of authority,\nthe foundations of procedural legitimacy, and the possibility of justificatory\nneutrality.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.18670", "title": "Health Misinformation in Social Networks: A Survey of IT Approaches", "abstract": "  In this paper, we present a comprehensive survey on the pervasive issue of\nmedical misinformation in social networks from the perspective of information\ntechnology. The survey aims at providing a systematic review of related\nresearch and helping researchers and practitioners navigate through this\nfast-changing field. Specifically, we first present manual and automatic\napproaches for fact-checking. We then explore fake news detection methods,\nusing content, propagation features, or source features, as well as mitigation\napproaches for countering the spread of misinformation. We also provide a\ndetailed list of several datasets on health misinformation and of publicly\navailable tools. We conclude the survey with a discussion on the open\nchallenges and future research directions in the battle against health\nmisinformation.\n", "date": "2024-10-24", "categories": []}
{"id": "2410.17423", "title": "Artificial Intelligence in Brazilian News: A Mixed-Methods Analysis", "abstract": "  The current surge in Artificial Intelligence (AI) interest, reflected in\nheightened media coverage since 2009, has sparked significant debate on AI's\nimplications for privacy, social justice, workers' rights, and democracy. The\nmedia plays a crucial role in shaping public perception and acceptance of AI\ntechnologies. However, research into how AI appears in media has primarily\nfocused on anglophone contexts, leaving a gap in understanding how AI is\nrepresented globally. This study addresses this gap by analyzing 3,560 news\narticles from Brazilian media published between July 1, 2023, and February 29,\n2024, from 13 popular online news outlets. Using Computational Grounded Theory\n(CGT), the study applies Latent Dirichlet Allocation (LDA), BERTopic, and\nNamed-Entity Recognition to investigate the main topics in AI coverage and the\nentities represented. The findings reveal that Brazilian news coverage of AI is\ndominated by topics related to applications in the workplace and product\nlaunches, with limited space for societal concerns, which mostly focus on\ndeepfakes and electoral integrity. The analysis also highlights a significant\npresence of industry-related entities, indicating a strong influence of\ncorporate agendas in the country's news. This study underscores the need for a\nmore critical and nuanced discussion of AI's societal impacts in Brazilian\nmedia.\n", "date": "2024-10-22", "categories": []}
{"id": "2410.17655", "title": "Mapping the Media Landscape: Predicting Factual Reporting and Political\n  Bias Through Web Interactions", "abstract": "  Bias assessment of news sources is paramount for professionals,\norganizations, and researchers who rely on truthful evidence for information\ngathering and reporting. While certain bias indicators are discernible from\ncontent analysis, descriptors like political bias and fake news pose greater\nchallenges. In this paper, we propose an extension to a recently presented news\nmedia reliability estimation method that focuses on modeling outlets and their\nlongitudinal web interactions. Concretely, we assess the classification\nperformance of four reinforcement learning strategies on a large news media\nhyperlink graph. Our experiments, targeting two challenging bias descriptors,\nfactual reporting and political bias, showed a significant performance\nimprovement at the source media level. Additionally, we validate our methods on\nthe CLEF 2023 CheckThat! Lab challenge, outperforming the reported results in\nboth, F1-score and the official MAE metric. Furthermore, we contribute by\nreleasing the largest annotated dataset of news source media, categorized with\nfactual reporting and political bias labels. Our findings suggest that\nprofiling news media sources based on their hyperlink interactions over time is\nfeasible, offering a bird's-eye view of evolving media landscapes.\n", "date": "2024-10-23", "categories": []}
{"id": "2407.14344", "title": "LLMs left, right, and center: Assessing GPT's capabilities to label\n  political bias from web domains", "abstract": "  This research investigates whether OpenAI's GPT-4, a state-of-the-art large\nlanguage model, can accurately classify the political bias of news sources\nbased solely on their URLs. Given the subjective nature of political labels,\nthird-party bias ratings like those from Ad Fontes Media, AllSides, and Media\nBias/Fact Check (MBFC) are often used in research to analyze news source\ndiversity. This study aims to determine if GPT-4 can replicate these human\nratings on a seven-degree scale (\"far-left\" to \"far-right\"). The analysis\ncompares GPT-4's classifications against MBFC's, and controls for website\npopularity using Open PageRank scores. Findings reveal a high correlation\n($\\text{Spearman's } \\rho = .89$, $n = 5,877$, $p < 0.001$) between GPT-4's and\nMBFC's ratings, indicating the model's potential reliability. However, GPT-4\nabstained from classifying approximately $\\frac{2}{3}$ of the dataset. It is\nmore likely to abstain from rating unpopular websites, which also suffer from\nless accurate assessments. The LLM tends to avoid classifying sources that MBFC\nconsiders to be centrist, resulting in more polarized outputs. Finally, this\nanalysis shows a slight leftward skew in GPT's classifications compared to\nMBFC's. Therefore, while this paper suggests that while GPT-4 can be a\nscalable, cost-effective tool for political bias classification of news\nwebsites, its use should be as a complement to human judgment to mitigate\nbiases.\n", "date": "2024-07-19", "categories": []}
{"id": "2409.13686", "title": "The Impact of Large Language Models in Academia: from Writing to\n  Speaking", "abstract": "  Large language models (LLMs) are increasingly impacting human society,\nparticularly in textual information. Based on more than 30,000 papers and 1,000\npresentations from machine learning conferences, we examined and compared the\nwords used in writing and speaking, representing the first large-scale study of\nhow LLMs influence the two main modes of verbal communication and expression\nwithin the same group of people. Our empirical results show that LLM-style\nwords such as \"significant\" have been used more frequently in abstracts and\noral presentations. The impact on speaking is beginning to emerge and is likely\nto grow in the future, calling attention to the implicit influence and ripple\neffect of LLMs on human society.\n", "date": "2024-09-20", "categories": []}
{"id": "2410.15182", "title": "The Computational Anatomy of Humility: Modeling Intellectual Humility in\n  Online Public Discourse", "abstract": "  The ability for individuals to constructively engage with one another across\nlines of difference is a critical feature of a healthy pluralistic society.\nThis is also true in online discussion spaces like social media platforms. To\ndate, much social media research has focused on preventing ills -- like\npolitical polarization and the spread of misinformation. While this is\nimportant, enhancing the quality of online public discourse requires not just\nreducing ills but also promoting foundational human virtues. In this study, we\nfocus on one particular virtue: ``intellectual humility'' (IH), or\nacknowledging the potential limitations in one's own beliefs. Specifically, we\nexplore the development of computational methods for measuring IH at scale. We\nmanually curate and validate an IH codebook on 350 posts about religion drawn\nfrom subreddits and use them to develop LLM-based models for automating this\nmeasurement. Our best model achieves a Macro-F1 score of 0.64 across labels\n(and 0.70 when predicting IH/IA/Neutral at the coarse level), higher than an\nexpected naive baseline of 0.51 (0.32 for IH/IA/Neutral) but lower than a human\nannotator-informed upper bound of 0.85 (0.83 for IH/IA/Neutral). Our results\nboth highlight the challenging nature of detecting IH online -- opening the\ndoor to new directions in NLP research -- and also lay a foundation for\ncomputational social science researchers interested in analyzing and fostering\nmore IH in online public discourse.\n", "date": "2024-10-19", "categories": []}
{"id": "2410.15884", "title": "Using GPT Models for Qualitative and Quantitative News Analytics in the\n  2024 US Presidental Election Process", "abstract": "  The paper considers an approach of using Google Search API and GPT-4o model\nfor qualitative and quantitative analyses of news through retrieval-augmented\ngeneration (RAG). This approach was applied to analyze news about the 2024 US\npresidential election process. Different news sources for different time\nperiods have been analyzed. Quantitative scores generated by GPT model have\nbeen analyzed using Bayesian regression to derive trend lines. The\ndistributions found for the regression parameters allow for the analysis of\nuncertainty in the election process. The obtained results demonstrate that\nusing the GPT models for news analysis, one can get informative analytics and\nprovide key insights that can be applied in further analyses of election\nprocesses.\n", "date": "2024-10-21", "categories": []}
{"id": "2402.14224", "title": "Framing in the Presence of Supporting Data: A Case Study in U.S.\n  Economic News", "abstract": "  The mainstream media has much leeway in what it chooses to cover and how it\ncovers it. These choices have real-world consequences on what people know and\ntheir subsequent behaviors. However, the lack of objective measures to evaluate\neditorial choices makes research in this area particularly difficult. In this\npaper, we argue that there are newsworthy topics where objective measures exist\nin the form of supporting data and propose a computational framework to analyze\neditorial choices in this setup. We focus on the economy because the reporting\nof economic indicators presents us with a relatively easy way to determine both\nthe selection and framing of various publications. Their values provide a\nground truth of how the economy is doing relative to how the publications\nchoose to cover it. To do this, we define frame prediction as a set of\ninterdependent tasks. At the article level, we learn to identify the reported\nstance towards the general state of the economy. Then, for every numerical\nquantity reported in the article, we learn to identify whether it corresponds\nto an economic indicator and whether it is being reported in a positive or\nnegative way. To perform our analysis, we track six American publishers and\neach article that appeared in the top 10 slots of their landing page between\n2015 and 2023.\n", "date": "2024-02-22", "categories": []}
{"id": "2410.13887", "title": "Observing the Southern US Culture of Honor Using Large-Scale Social\n  Media Analysis", "abstract": "  A \\textit{culture of honor} refers to a social system where individuals'\nstatus, reputation, and esteem play a central role in governing interpersonal\nrelations. Past works have associated this concept with the United States (US)\nSouth and related with it various traits such as higher sensitivity to insult,\na higher value on reputation, and a tendency to react violently to insults. In\nthis paper, we hypothesize and confirm that internet users from the US South,\nwhere a \\textit{culture of honor} is more prevalent, are more likely to display\na trait predicted by their belonging to a \\textit{culture of honor}.\nSpecifically, we test the hypothesis that US Southerners are more likely to\nretaliate to personal attacks by personally attacking back. We leverage\nOpenAI's GPT-3.5 API to both geolocate internet users and to automatically\ndetect whether users are insulting each other. We validate the use of GPT-3.5\nby measuring its performance on manually-labeled subsets of the data. Our work\ndemonstrates the potential of formulating a hypothesis based on a conceptual\nframework, operationalizing it in a way that is amenable to large-scale\nLLM-aided analysis, manually validating the use of the LLM, and drawing a\nconclusion.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.13909", "title": "Large Language Model-driven Multi-Agent Simulation for News Diffusion\n  Under Different Network Structures", "abstract": "  The proliferation of fake news in the digital age has raised critical\nconcerns, particularly regarding its impact on societal trust and democratic\nprocesses. Diverging from conventional agent-based simulation approaches, this\nwork introduces an innovative approach by employing a large language model\n(LLM)-driven multi-agent simulation to replicate complex interactions within\ninformation ecosystems. We investigate key factors that facilitate news\npropagation, such as agent personalities and network structures, while also\nevaluating strategies to combat misinformation. Through simulations across\nvarying network structures, we demonstrate the potential of LLM-based agents in\nmodeling the dynamics of misinformation spread, validating the influence of\nagent traits on the diffusion process. Our findings emphasize the advantages of\nLLM-based simulations over traditional techniques, as they uncover underlying\ncauses of information spread -- such as agents promoting discussions -- beyond\nthe predefined rules typically employed in existing agent-based models.\nAdditionally, we evaluate three countermeasure strategies, discovering that\nbrute-force blocking influential agents in the network or announcing news\naccuracy can effectively mitigate misinformation. However, their effectiveness\nis influenced by the network structure, highlighting the importance of\nconsidering network structure in the development of future misinformation\ncountermeasures.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.13915", "title": "A Simulation System Towards Solving Societal-Scale Manipulation", "abstract": "  The rise of AI-driven manipulation poses significant risks to societal trust\nand democratic processes. Yet, studying these effects in real-world settings at\nscale is ethically and logistically impractical, highlighting a need for\nsimulation tools that can model these dynamics in controlled settings to enable\nexperimentation with possible defenses. We present a simulation environment\ndesigned to address this. We elaborate upon the Concordia framework that\nsimulates offline, `real life' activity by adding online interactions to the\nsimulation through social media with the integration of a Mastodon server. We\nimprove simulation efficiency and information flow, and add a set of\nmeasurement tools, particularly longitudinal surveys. We demonstrate the\nsimulator with a tailored example in which we track agents' political positions\nand show how partisan manipulation of agents can affect election results.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.13400", "title": "Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt\n  from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and\n  Humans", "abstract": "  This chapter introduces a research project titled \"Analyzing the Political\nDiscourse: A Collaboration Between Humans and Artificial Intelligence\", which\nwas initiated in preparation for Greece's 2023 general elections. The project\nfocused on the analysis of political leaders' campaign speeches, employing\nArtificial Intelligence (AI), in conjunction with an interdisciplinary team\ncomprising journalists, a political scientist, and data scientists. The chapter\ndelves into various aspects of political discourse analysis, including\nsentiment analysis, polarization, populism, topic detection, and Named Entities\nRecognition (NER). This experimental study investigates the capabilities of\nlarge language model (LLMs), and in particular OpenAI's ChatGPT, for analyzing\npolitical speech, evaluates its strengths and weaknesses, and highlights the\nessential role of human oversight in using AI in journalism projects and\npotentially other societal sectors. The project stands as an innovative example\nof human-AI collaboration (known also as \"hybrid intelligence\") within the\nrealm of digital humanities, offering valuable insights for future initiatives.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.13654", "title": "Red and blue language: Word choices in the Trump & Harris 2024\n  presidential debate", "abstract": "  Political debates are a peculiar type of political discourse, in which\ncandidates directly confront one another, addressing not only the the\nmoderator's questions, but also their opponent's statements, as well as the\nconcerns of voters from both parties and undecided voters. Therefore, language\nis adjusted to meet specific expectations and achieve persuasion. We analyse\nhow the language of Trump and Harris during the debate (September 10th 2024)\ndiffers in relation to the following semantic and pragmatic features, for which\nwe formulated targeted hypotheses: framing values and ideology, appealing to\nemotion, using words with different degrees of concreteness and specificity,\naddressing others through singular or plural pronouns. Our findings include:\ndifferences in the use of figurative frames (Harris often framing issues around\nrecovery and empowerment, Trump often focused on crisis and decline); similar\nuse of emotional language, with Trump showing a slight higher tendency toward\nnegativity and toward less subjective language compared to Harris; no\nsignificant difference in the specificity of candidates' responses; similar use\nof abstract language, with Trump showing more variability than Harris,\ndepending on the subject discussed; differences in addressing the opponent,\nwith Trump not mentioning Harris by name, while Harris referring to Trump\nfrequently; different uses of pronouns, with Harris using both singular and\nplural pronouns equally, while Trump using more singular pronouns. The results\nare discussed in relation to previous literature on Red and Blue language,\nwhich refers to distinct linguistic patterns associated with conservative (Red)\nand liberal (Blue) political ideologies.\n", "date": "2024-10-17", "categories": []}
{"id": "2406.17232", "title": "Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human\n  Belief Networks", "abstract": "  Creating human-like large language model (LLM) agents is crucial for faithful\nsocial simulation. Having LLMs role-play based on demographic information\nsometimes improves human likeness but often does not. This study assessed\nwhether LLM alignment with human behavior can be improved by integrating\ninformation from empirically-derived human belief networks. Using data from a\nhuman survey, we estimated a belief network encompassing 64 topics loading on\nnine non-overlapping latent factors. We then seeded LLM-based agents with an\nopinion on one topic, and assessed the alignment of its expressed opinions on\nremaining test topics with corresponding human data. Role-playing based on\ndemographic information alone did not align LLM and human opinions, but seeding\nthe agent with a single belief greatly improved alignment for topics related in\nthe belief network, and not for topics outside the network. These results\nsuggest a novel path for human-LLM belief alignment in work seeking to simulate\nand understand patterns of belief distributions in society.\n", "date": "2024-06-25", "categories": []}
{"id": "2410.12791", "title": "Context is Key(NMF): Modelling Topical Information Dynamics in Chinese\n  Diaspora Media", "abstract": "  Does the People's Republic of China (PRC) interfere with European elections\nthrough ethnic Chinese diaspora media? This question forms the basis of an\nongoing research project exploring how PRC narratives about European elections\nare represented in Chinese diaspora media, and thus the objectives of PRC news\nmedia manipulation. In order to study diaspora media efficiently and at scale,\nit is necessary to use techniques derived from quantitative text analysis, such\nas topic modelling. In this paper, we present a pipeline for studying\ninformation dynamics in Chinese media. Firstly, we present KeyNMF, a new\napproach to static and dynamic topic modelling using transformer-based\ncontextual embedding models. We provide benchmark evaluations to demonstrate\nthat our approach is competitive on a number of Chinese datasets and metrics.\nSecondly, we integrate KeyNMF with existing methods for describing information\ndynamics in complex systems. We apply this pipeline to data from five news\nsites, focusing on the period of time leading up to the 2024 European\nparliamentary elections. Our methods and results demonstrate the effectiveness\nof KeyNMF for studying information dynamics in Chinese media and lay groundwork\nfor further work addressing the broader research questions.\n", "date": "2024-10-16", "categories": []}
{"id": "2311.09630", "title": "Decoding Susceptibility: Modeling Misbelief to Misinformation Through a\n  Computational Approach", "abstract": "  Susceptibility to misinformation describes the degree of belief in\nunverifiable claims, a latent aspect of individuals' mental processes that is\nnot observable. Existing susceptibility studies heavily rely on self-reported\nbeliefs, which can be subject to bias, expensive to collect, and challenging to\nscale for downstream applications. To address these limitations, in this work,\nwe propose a computational approach to model users' latent susceptibility\nlevels. As shown in previous research, susceptibility is influenced by various\nfactors (e.g., demographic factors, political ideology), and directly\ninfluences people's reposting behavior on social media. To represent the\nunderlying mental process, our susceptibility modeling incorporates these\nfactors as inputs, guided by the supervision of people's sharing behavior.\nUsing COVID-19 as a testbed domain, our experiments demonstrate a significant\nalignment between the susceptibility scores estimated by our computational\nmodeling and human judgments, confirming the effectiveness of this latent\nmodeling approach. Furthermore, we apply our model to annotate susceptibility\nscores on a large-scale dataset and analyze the relationships between\nsusceptibility with various factors. Our analysis reveals that political\nleanings and psychological factors exhibit varying degrees of association with\nsusceptibility to COVID-19 misinformation.\n", "date": "2023-11-16", "categories": []}
{"id": "2406.11622", "title": "Building Knowledge-Guided Lexica to Model Cultural Variation", "abstract": "  Cultural variation exists between nations (e.g., the United States vs.\nChina), but also within regions (e.g., California vs. Texas, Los Angeles vs.\nSan Francisco). Measuring this regional cultural variation can illuminate how\nand why people think and behave differently. Historically, it has been\ndifficult to computationally model cultural variation due to a lack of training\ndata and scalability constraints. In this work, we introduce a new research\nproblem for the NLP community: How do we measure variation in cultural\nconstructs across regions using language? We then provide a scalable solution:\nbuilding knowledge-guided lexica to model cultural variation, encouraging\nfuture work at the intersection of NLP and cultural understanding. We also\nhighlight modern LLMs' failure to measure cultural variation or generate\nculturally varied language.\n", "date": "2024-06-17", "categories": []}
{"id": "2410.08352", "title": "Revealing COVID-19's Social Dynamics: Diachronic Semantic Analysis of\n  Vaccine and Symptom Discourse on Twitter", "abstract": "  Social media is recognized as an important source for deriving insights into\npublic opinion dynamics and social impacts due to the vast textual data\ngenerated daily and the 'unconstrained' behavior of people interacting on these\nplatforms. However, such analyses prove challenging due to the semantic shift\nphenomenon, where word meanings evolve over time. This paper proposes an\nunsupervised dynamic word embedding method to capture longitudinal semantic\nshifts in social media data without predefined anchor words. The method\nleverages word co-occurrence statistics and dynamic updating to adapt\nembeddings over time, addressing the challenges of data sparseness, imbalanced\ndistributions, and synergistic semantic effects. Evaluated on a large COVID-19\nTwitter dataset, the method reveals semantic evolution patterns of vaccine- and\nsymptom-related entities across different pandemic stages, and their potential\ncorrelations with real-world statistics. Our key contributions include the\ndynamic embedding technique, empirical analysis of COVID-19 semantic shifts,\nand discussions on enhancing semantic shift modeling for computational social\nscience research. This study enables capturing longitudinal semantic dynamics\non social media to understand public discourse and collective phenomena.\n", "date": "2024-10-10", "categories": []}
{"id": "2410.03829", "title": "Misinformation with Legal Consequences (MisLC): A New Task Towards\n  Harnessing Societal Harm of Misinformation", "abstract": "  Misinformation, defined as false or inaccurate information, can result in\nsignificant societal harm when it is spread with malicious or even innocuous\nintent. The rapid online information exchange necessitates advanced detection\nmechanisms to mitigate misinformation-induced harm. Existing research, however,\nhas predominantly focused on assessing veracity, overlooking the legal\nimplications and social consequences of misinformation. In this work, we take a\nnovel angle to consolidate the definition of misinformation detection using\nlegal issues as a measurement of societal ramifications, aiming to bring\ninterdisciplinary efforts to tackle misinformation and its consequence. We\nintroduce a new task: Misinformation with Legal Consequence (MisLC), which\nleverages definitions from a wide range of legal domains covering 4 broader\nlegal topics and 11 fine-grained legal issues, including hate speech, election\nlaws, and privacy regulations. For this task, we advocate a two-step dataset\ncuration approach that utilizes crowd-sourced checkworthiness and expert\nevaluations of misinformation. We provide insights about the MisLC task through\nempirical evidence, from the problem definition to experiments and expert\ninvolvement. While the latest large language models and retrieval-augmented\ngeneration are effective baselines for the task, we find they are still far\nfrom replicating expert performance.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.04164", "title": "Towards Effective Counter-Responses: Aligning Human Preferences with\n  Strategies to Combat Online Trolling", "abstract": "  Trolling in online communities typically involves disruptive behaviors such\nas provoking anger and manipulating discussions, leading to a polarized\natmosphere and emotional distress. Robust moderation is essential for\nmitigating these negative impacts and maintaining a healthy and constructive\ncommunity atmosphere. However, effectively addressing trolls is difficult\nbecause their behaviors vary widely and require different response strategies\n(RSs) to counter them. This diversity makes it challenging to choose an\nappropriate RS for each specific situation. To address this challenge, our\nresearch investigates whether humans have preferred strategies tailored to\ndifferent types of trolling behaviors. Our findings reveal a correlation\nbetween the types of trolling encountered and the preferred RS. In this paper,\nwe introduce a methodology for generating counter-responses to trolls by\nrecommending appropriate RSs, supported by a dataset aligning these strategies\nwith human preferences across various troll contexts. The experimental results\ndemonstrate that our proposed approach guides constructive discussion and\nreduces the negative effects of trolls, thereby enhancing the online community\nenvironment.\n", "date": "2024-10-05", "categories": []}
{"id": "2410.04236", "title": "Overview of Factify5WQA: Fact Verification through 5W Question-Answering", "abstract": "  Researchers have found that fake news spreads much times faster than real\nnews. This is a major problem, especially in today's world where social media\nis the key source of news for many among the younger population. Fact\nverification, thus, becomes an important task and many media sites contribute\nto the cause. Manual fact verification is a tedious task, given the volume of\nfake news online. The Factify5WQA shared task aims to increase research towards\nautomated fake news detection by providing a dataset with an aspect-based\nquestion answering based fact verification method. Each claim and its\nsupporting document is associated with 5W questions that help compare the two\ninformation sources. The objective performance measure in the task is done by\ncomparing answers using BLEU score to measure the accuracy of the answers,\nfollowed by an accuracy measure of the classification. The task had submissions\nusing custom training setup and pre-trained language-models among others. The\nbest performing team posted an accuracy of 69.56%, which is a near 35%\nimprovement over the baseline.\n", "date": "2024-10-05", "categories": []}
{"id": "2410.04282", "title": "Locating Information Gaps and Narrative Inconsistencies Across\n  Languages: A Case Study of LGBT People Portrayals on Wikipedia", "abstract": "  To explain social phenomena and identify systematic biases, much research in\ncomputational social science focuses on comparative text analyses. These\nstudies often rely on coarse corpus-level statistics or local word-level\nanalyses, mainly in English. We introduce the InfoGap method -- an efficient\nand reliable approach to locating information gaps and inconsistencies in\narticles at the fact level, across languages. We evaluate InfoGap by analyzing\nLGBT people's portrayals, across 2.7K biography pages on English, Russian, and\nFrench Wikipedias. We find large discrepancies in factual coverage across the\nlanguages. Moreover, our analysis reveals that biographical facts carrying\nnegative connotations are more likely to be highlighted in Russian Wikipedia.\nCrucially, InfoGap both facilitates large scale analyses, and pinpoints local\ndocument- and fact-level information gaps, laying a new foundation for targeted\nand nuanced comparative language analysis at scale.\n", "date": "2024-10-05", "categories": []}
{"id": "2403.05020", "title": "Is this the real life? Is this just fantasy? The Misleading Success of\n  Simulating Social Interactions With LLMs", "abstract": "  Recent advances in large language models (LLM) have enabled richer social\nsimulations, allowing for the study of various social phenomena. However, most\nrecent work has used a more omniscient perspective on these simulations (e.g.,\nsingle LLM to generate all interlocutors), which is fundamentally at odds with\nthe non-omniscient, information asymmetric interactions that involve humans and\nAI agents in the real world. To examine these differences, we develop an\nevaluation framework to simulate social interactions with LLMs in various\nsettings (omniscient, non-omniscient). Our experiments show that LLMs perform\nbetter in unrealistic, omniscient simulation settings but struggle in ones that\nmore accurately reflect real-world conditions with information asymmetry. Our\nfindings indicate that addressing information asymmetry remains a fundamental\nchallenge for LLM-based agents.\n", "date": "2024-03-08", "categories": []}
{"id": "2410.03545", "title": "Enhancing Data Quality through Simple De-duplication: Navigating\n  Responsible Computational Social Science Research", "abstract": "  Research in natural language processing (NLP) for Computational Social\nScience (CSS) heavily relies on data from social media platforms. This data\nplays a crucial role in the development of models for analysing\nsocio-linguistic phenomena within online communities. In this work, we conduct\nan in-depth examination of 20 datasets extensively used in NLP for CSS to\ncomprehensively examine data quality. Our analysis reveals that social media\ndatasets exhibit varying levels of data duplication. Consequently, this gives\nrise to challenges like label inconsistencies and data leakage, compromising\nthe reliability of models. Our findings also suggest that data duplication has\nan impact on the current claims of state-of-the-art performance, potentially\nleading to an overestimation of model effectiveness in real-world scenarios.\nFinally, we propose new protocols and best practices for improving dataset\ndevelopment from social media data and its usage.\n", "date": "2024-10-04", "categories": []}
{"id": "2310.15757", "title": "Do Differences in Values Influence Disagreements in Online Discussions?", "abstract": "  Disagreements are common in online discussions. Disagreement may foster\ncollaboration and improve the quality of a discussion under some conditions.\nAlthough there exist methods for recognizing disagreement, a deeper\nunderstanding of factors that influence disagreement is lacking in the\nliterature. We investigate a hypothesis that differences in personal values are\nindicative of disagreement in online discussions. We show how state-of-the-art\nmodels can be used for estimating values in online discussions and how the\nestimated values can be aggregated into value profiles. We evaluate the\nestimated value profiles based on human-annotated agreement labels. We find\nthat the dissimilarity of value profiles correlates with disagreement in\nspecific cases. We also find that including value information in agreement\nprediction improves performance.\n", "date": "2023-10-24", "categories": []}
{"id": "2408.11962", "title": "Characterizing Online Toxicity During the 2022 Mpox Outbreak: A\n  Computational Analysis of Topical and Network Dynamics", "abstract": "  Background: Online toxicity, encompassing behaviors such as harassment,\nbullying, hate speech, and the dissemination of misinformation, has become a\npressing social concern in the digital age. The 2022 Mpox outbreak, initially\ntermed \"Monkeypox\" but subsequently renamed to mitigate associated stigmas and\nsocietal concerns, serves as a poignant backdrop to this issue. Objective: In\nthis research, we undertake a comprehensive analysis of the toxic online\ndiscourse surrounding the 2022 Mpox outbreak. Our objective is to dissect its\norigins, characterize its nature and content, trace its dissemination patterns,\nand assess its broader societal implications, with the goal of providing\ninsights that can inform strategies to mitigate such toxicity in future crises.\nMethods: We collected more than 1.6 million unique tweets and analyzed them\nfrom five dimensions, including context, extent, content, speaker, and intent.\nUtilizing BERT-based topic modeling and social network community clustering, we\ndelineated the toxic dynamics on Twitter. Results: We identified five\nhigh-level topic categories in the toxic online discourse on Twitter, including\ndisease (46.6%), health policy and healthcare (19.3%), homophobia (23.9%),\npolitics (6.0%), and racism (4.1%). Through the toxicity diffusion networks of\nmentions, retweets, and the top users, we found that retweets of toxic content\nwere widespread, while influential users rarely engaged with or countered this\ntoxicity through retweets. Conclusions: By tracking topical dynamics, we can\ntrack the changing popularity of toxic content online, providing a better\nunderstanding of societal challenges. Network dynamics spotlight key social\nmedia influencers and their intents, indicating that addressing these central\nfigures in toxic discourse can enhance crisis communication and inform\npolicy-making.\n", "date": "2024-08-21", "categories": []}
{"id": "2310.09848", "title": "Enhancing Stance Classification on Social Media Using Quantified Moral\n  Foundations", "abstract": "  This study enhances stance detection on social media by incorporating deeper\npsychological attributes, specifically individuals' moral foundations. These\ntheoretically-derived dimensions aim to provide a comprehensive profile of an\nindividual's moral concerns which, in recent work, has been linked to behaviour\nin a range of domains, including society, politics, health, and the\nenvironment. In this paper, we investigate how moral foundation dimensions can\ncontribute to predicting an individual's stance on a given target. Specifically\nwe incorporate moral foundation features extracted from text, along with\nmessage semantic features, to classify stances at both message- and user-levels\nusing both traditional machine learning models and large language models. Our\npreliminary results suggest that encoding moral foundations can enhance the\nperformance of stance detection tasks and help illuminate the associations\nbetween specific moral foundations and online stances on target topics. The\nresults highlight the importance of considering deeper psychological attributes\nin stance analysis and underscores the role of moral foundations in guiding\nonline social behavior.\n", "date": "2023-10-15", "categories": []}
{"id": "2409.18548", "title": "Research on Predicting Public Opinion Event Heat Levels Based on Large\n  Language Models", "abstract": "  In recent years, with the rapid development of large language models, serval\nmodels such as GPT-4o have demonstrated extraordinary capabilities, surpassing\nhuman performance in various language tasks. As a result, many researchers have\nbegun exploring their potential applications in the field of public opinion\nanalysis. This study proposes a novel large-language-models-based method for\npublic opinion event heat level prediction. First, we preprocessed and\nclassified 62,836 Chinese hot event data collected between July 2022 and\nDecember 2023. Then, based on each event's online dissemination heat index, we\nused the MiniBatchKMeans algorithm to automatically cluster the events and\ncategorize them into four heat levels (ranging from low heat to very high\nheat). Next, we randomly selected 250 events from each heat level, totalling\n1,000 events, to build the evaluation dataset. During the evaluation process,\nwe employed various large language models to assess their accuracy in\npredicting event heat levels in two scenarios: without reference cases and with\nsimilar case references. The results showed that GPT-4o and DeepseekV2\nperformed the best in the latter case, achieving prediction accuracies of 41.4%\nand 41.5%, respectively. Although the overall prediction accuracy remains\nrelatively low, it is worth noting that for low-heat (Level 1) events, the\nprediction accuracies of these two models reached 73.6% and 70.4%,\nrespectively. Additionally, the prediction accuracy showed a downward trend\nfrom Level 1 to Level 4, which correlates with the uneven distribution of data\nacross the heat levels in the actual dataset. This suggests that with the more\nrobust dataset, public opinion event heat level prediction based on large\nlanguage models will have significant research potential for the future.\n", "date": "2024-09-27", "categories": []}
{"id": "2409.16946", "title": "Setting the AI Agenda -- Evidence from Sweden in the ChatGPT Era", "abstract": "  This paper examines the development of the Artificial Intelligence (AI)\nmeta-debate in Sweden before and after the release of ChatGPT. From the\nperspective of agenda-setting theory, we propose that it is an elite outside of\nparty politics that is leading the debate -- i.e. that the politicians are\nrelatively silent when it comes to this rapid development. We also suggest that\nthe debate has become more substantive and risk-oriented in recent years. To\ninvestigate this claim, we draw on an original dataset of elite-level documents\nfrom the early 2010s to the present, using op-eds published in a number of\nleading Swedish newspapers. By conducting a qualitative content analysis of\nthese materials, our preliminary findings lend support to the expectation that\nan academic, rather than a political elite is steering the debate.\n", "date": "2024-09-25", "categories": []}
{"id": "2405.09439", "title": "Facilitating Opinion Diversity through Hybrid NLP Approaches", "abstract": "  Modern democracies face a critical issue of declining citizen participation\nin decision-making. Online discussion forums are an important avenue for\nenhancing citizen participation. This thesis proposal 1) identifies the\nchallenges involved in facilitating large-scale online discussions with Natural\nLanguage Processing (NLP), 2) suggests solutions to these challenges by\nincorporating hybrid human-AI technologies, and 3) investigates what these\ntechnologies can reveal about individual perspectives in online discussions. We\npropose a three-layered hierarchy for representing perspectives that can be\nobtained by a mixture of human intelligence and large language models. We\nillustrate how these representations can draw insights into the diversity of\nperspectives and allow us to investigate interactions in online discussions.\n", "date": "2024-05-15", "categories": []}
{"id": "2304.04190", "title": "Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual\n  and Multilingual Approaches for Detecting Online News Genre, Framing and\n  Persuasion Techniques", "abstract": "  This paper describes the participation of team QUST in the SemEval2023 task\n3. The monolingual models are first evaluated with the under-sampling of the\nmajority classes in the early stage of the task. Then, the pre-trained\nmultilingual model is fine-tuned with a combination of the class weights and\nthe sample weights. Two different fine-tuning strategies, the task-agnostic and\nthe task-dependent, are further investigated. All experiments are conducted\nunder the 10-fold cross-validation, the multilingual approaches are superior to\nthe monolingual ones. The submitted system achieves the second best in Italian\nand Spanish (zero-shot) in subtask-1.\n", "date": "2023-04-09", "categories": []}
{"id": "2409.14464", "title": "AggregHate: An Efficient Aggregative Approach for the Detection of\n  Hatemongers on Social Platforms", "abstract": "  Automatic detection of online hate speech serves as a crucial step in the\ndetoxification of the online discourse. Moreover, accurate classification can\npromote a better understanding of the proliferation of hate as a social\nphenomenon. While most prior work focus on the detection of hateful utterances,\nwe argue that focusing on the user level is as important, albeit challenging.\nIn this paper we consider a multimodal aggregative approach for the detection\nof hate-mongers, taking into account the potentially hateful texts, user\nactivity, and the user network. We evaluate our methods on three unique\ndatasets X (Twitter), Gab, and Parler showing that a processing a user's texts\nin her social context significantly improves the detection of hate mongers,\ncompared to previously used text and graph-based methods. Our method can be\nthen used to improve the classification of coded messages, dog-whistling, and\nracial gas-lighting, as well as inform intervention measures. Moreover, our\napproach is highly efficient even for very large datasets and networks.\n", "date": "2024-09-22", "categories": []}
{"id": "2409.12194", "title": "Gender Representation and Bias in Indian Civil Service Mock Interviews", "abstract": "  This paper makes three key contributions. First, via a substantial corpus of\n51,278 interview questions sourced from 888 YouTube videos of mock interviews\nof Indian civil service candidates, we demonstrate stark gender bias in the\nbroad nature of questions asked to male and female candidates. Second, our\nexperiments with large language models show a strong presence of gender bias in\nexplanations provided by the LLMs on the gender inference task. Finally, we\npresent a novel dataset of 51,278 interview questions that can inform future\nsocial science studies.\n", "date": "2024-09-18", "categories": []}
{"id": "2409.13064", "title": "Fear and Loathing on the Frontline: Decoding the Language of Othering by\n  Russia-Ukraine War Bloggers", "abstract": "  Othering, the act of portraying outgroups as fundamentally different from the\ningroup, often escalates into framing them as existential threats--fueling\nintergroup conflict and justifying exclusion and violence. These dynamics are\nalarmingly pervasive, spanning from the extreme historical examples of\ngenocides against minorities in Germany and Rwanda to the ongoing violence and\nrhetoric targeting migrants in the US and Europe. While concepts like hate\nspeech and fear speech have been explored in existing literature, they capture\nonly part of this broader and more nuanced dynamic which can often be harder to\ndetect, particularly in online speech and propaganda. To address this\nchallenge, we introduce a novel computational framework that leverages large\nlanguage models (LLMs) to quantify othering across diverse contexts, extending\nbeyond traditional linguistic indicators of hostility. Applying the model to\nreal-world data from Telegram war bloggers and political discussions on Gab\nreveals how othering escalates during conflicts, interacts with moral language,\nand garners significant attention, particularly during periods of crisis. Our\nframework, designed to offer deeper insights into othering dynamics, combines\nwith a rapid adaptation process to provide essential tools for mitigating\nothering's adverse impacts on social cohesion.\n", "date": "2024-09-19", "categories": []}
{"id": "2409.12658", "title": "Exploring the topics, sentiments and hate speech in the Spanish\n  information environment", "abstract": "  In the digital era, the internet and social media have transformed\ncommunication but have also facilitated the spread of hate speech and\ndisinformation, leading to radicalization, polarization, and toxicity. This is\nespecially concerning for media outlets due to their significant role in\nshaping public discourse. This study examines the topics, sentiments, and hate\nprevalence in 337,807 response messages (website comments and tweets) to news\nfrom five Spanish media outlets (La Vanguardia, ABC, El Pa\\'is, El Mundo, and\n20 Minutos) in January 2021. These public reactions were originally labeled as\ndistinct types of hate by experts following an original procedure, and they are\nnow classified into three sentiment values (negative, neutral, or positive) and\nmain topics. The BERTopic unsupervised framework was used to extract 81 topics,\nmanually named with the help of Large Language Models (LLMs) and grouped into\nnine primary categories.\n  Results show social issues (22.22%), expressions and slang (20.35%), and\npolitical issues (11.80%) as the most discussed. Content is mainly negative\n(62.7%) and neutral (28.57%), with low positivity (8.73%). Toxic narratives\nrelate to conversation expressions, gender, feminism, and COVID-19. Despite low\nlevels of hate speech (3.98%), the study confirms high toxicity in online\nresponses to social and political topics.\n", "date": "2024-09-19", "categories": []}
{"id": "2305.09548", "title": "Measuring Dimensions of Self-Presentation in Twitter Bios and their\n  Links to Misinformation Sharing", "abstract": "  Social media platforms provide users with a profile description field,\ncommonly known as a ``bio,\" where they can present themselves to the world. A\ngrowing literature shows that text in these bios can improve our understanding\nof online self-presentation and behavior, but existing work relies exclusively\non keyword-based approaches to do so. We here propose and evaluate a suite of\n\\hl{simple, effective, and theoretically motivated} approaches to embed bios in\nspaces that capture salient dimensions of social meaning, such as age and\npartisanship. We \\hl{evaluate our methods on four tasks, showing that the\nstrongest one out-performs several practical baselines.} We then show the\nutility of our method in helping understand associations between\nself-presentation and the sharing of URLs from low-quality news sites on\nTwitter\\hl{, with a particular focus on explore the interactions between age\nand partisanship, and exploring the effects of self-presentations of\nreligiosity}. Our work provides new tools to help computational social\nscientists make use of information in bios, and provides new insights into how\nmisinformation sharing may be perceived on Twitter.\n", "date": "2023-05-16", "categories": []}
{"id": "2409.10550", "title": "Agentic Society: Merging skeleton from real world and texture from Large\n  Language Model", "abstract": "  Recent advancements in large language models (LLMs) and agent technologies\noffer promising solutions to the simulation of social science experiments, but\nthe availability of data of real-world population required by many of them\nstill poses as a major challenge. This paper explores a novel framework that\nleverages census data and LLMs to generate virtual populations, significantly\nreducing resource requirements and bypassing privacy compliance issues\nassociated with real-world data, while keeping a statistical truthfulness.\nDrawing on real-world census data, our approach first generates a persona that\nreflects demographic characteristics of the population. We then employ LLMs to\nenrich these personas with intricate details, using techniques akin to those in\nimage generative models but applied to textual data. Additionally, we propose a\nframework for the evaluation of the feasibility of our method with respect to\ncapability of LLMs based on personality trait tests, specifically the Big Five\nmodel, which also enhances the depth and realism of the generated personas.\nThrough preliminary experiments and analysis, we demonstrate that our method\nproduces personas with variability essential for simulating diverse human\nbehaviors in social science experiments. But the evaluation result shows that\nonly weak sign of statistical truthfulness can be produced due to limited\ncapability of current LLMs. Insights from our study also highlight the tension\nwithin LLMs between aligning with human values and reflecting real-world\ncomplexities. Thorough and rigorous test call for further research. Our codes\nare released at https://github.com/baiyuqi/agentic-society.git\n", "date": "2024-09-02", "categories": []}
{"id": "2409.11170", "title": "Capturing Differences in Character Representations Between Communities:\n  An Initial Study with Fandom", "abstract": "  Sociolinguistic theories have highlighted how narratives are often retold,\nco-constructed and reconceptualized in collaborative settings. This working\npaper focuses on the re-interpretation of characters, an integral part of the\nnarrative story-world, and attempts to study how this may be computationally\ncompared between online communities. Using online fandom - a highly communal\nphenomenon that has been largely studied qualitatively - as data, computational\nmethods were applied to explore shifts in character representations between two\ncommunities and the original text. Specifically, text from the Harry Potter\nnovels, r/HarryPotter subreddit, and fanfiction on Archive of Our Own were\nanalyzed for changes in character mentions, centrality measures from\nco-occurrence networks, and semantic associations. While fandom elevates\nsecondary characters as found in past work, the two fan communities prioritize\ndifferent subsets of characters. Word embedding tests reveal starkly different\nassociations of the same characters between communities on the gendered\nconcepts of femininity/masculinity, cruelty, and beauty. Furthermore,\nfanfiction descriptions of a male character analyzed between romance pairings\nscored higher for feminine-coded characteristics in male-male romance, matching\npast qualitative theorizing. The results high-light the potential for\ncomputational methods to assist in capturing the re-conceptualization of\nnarrative elements across communities and in supporting qualitative research on\nfandom.\n", "date": "2024-09-17", "categories": []}
{"id": "2409.08305", "title": "Mapping the Russian Internet Troll Network on Twitter using a Predictive\n  Model", "abstract": "  Russian Internet Trolls use fake personas to spread disinformation through\nmultiple social media streams. Given the increased frequency of this threat\nacross social media platforms, understanding those operations is paramount in\ncombating their influence. Using Twitter content identified as part of the\nRussian influence network, we created a predictive model to map the network\noperations. We classify accounts type based on their authenticity function for\na sub-sample of accounts by introducing logical categories and training a\npredictive model to identify similar behavior patterns across the network. Our\nmodel attains 88% prediction accuracy for the test set. Validation is done by\ncomparing the similarities with the 3 million Russian troll tweets dataset. The\nresult indicates a 90.7% similarity between the two datasets. Furthermore, we\ncompare our model predictions on a Russian tweets dataset, and the results\nstate that there is 90.5% correspondence between the predictions and the actual\ncategories. The prediction and validation results suggest that our predictive\nmodel can assist with mapping the actors in such networks.\n", "date": "2024-09-11", "categories": []}
{"id": "2409.08522", "title": "MAPX: An explainable model-agnostic framework for the detection of false\n  information on social media networks", "abstract": "  The automated detection of false information has become a fundamental task in\ncombating the spread of \"fake news\" on online social media networks (OSMN) as\nit reduces the need for manual discernment by individuals. In the literature,\nleveraging various content or context features of OSMN documents have been\nfound useful. However, most of the existing detection models often utilise\nthese features in isolation without regard to the temporal and dynamic changes\noft-seen in reality, thus, limiting the robustness of the models. Furthermore,\nthere has been little to no consideration of the impact of the quality of\ndocuments' features on the trustworthiness of the final prediction. In this\npaper, we introduce a novel model-agnostic framework, called MAPX, which allows\nevidence based aggregation of predictions from existing models in an\nexplainable manner. Indeed, the developed aggregation method is adaptive,\ndynamic and considers the quality of OSMN document features. Further, we\nperform extensive experiments on benchmarked fake news datasets to demonstrate\nthe effectiveness of MAPX using various real-world data quality scenarios. Our\nempirical results show that the proposed framework consistently outperforms all\nstate-of-the-art models evaluated. For reproducibility, a demo of MAPX is\navailable at \\href{https://github.com/SCondran/MAPX_framework}{this link}\n", "date": "2024-09-13", "categories": []}
{"id": "2409.08761", "title": "Journalists, Emotions, and the Introduction of Generative AI Chatbots: A\n  Large-Scale Analysis of Tweets Before and After the Launch of ChatGPT", "abstract": "  As part of a broader look at the impact of generative AI, this study\ninvestigated the emotional responses of journalists to the release of ChatGPT\nat the time of its launch. By analyzing nearly 1 million Tweets from\njournalists at major U.S. news outlets, we tracked changes in emotional tone\nand sentiment before and after the introduction of ChatGPT in November 2022.\nUsing various computational and natural language processing techniques to\nmeasure emotional shifts in response to ChatGPT's release, we found an increase\nin positive emotion and a more favorable tone post-launch, suggesting initial\noptimism toward AI's potential. This research underscores the pivotal role of\njournalists as interpreters of technological innovation and disruption,\nhighlighting how their emotional reactions may shape public narratives around\nemerging technologies. The study contributes to understanding the intersection\nof journalism, emotion, and AI, offering insights into the broader societal\nimpact of generative AI tools.\n", "date": "2024-09-13", "categories": []}
{"id": "2409.09001", "title": "E2MoCase: A Dataset for Emotional, Event and Moral Observations in News\n  Articles on High-impact Legal Cases", "abstract": "  The way media reports on legal cases can significantly shape public opinion,\noften embedding subtle biases that influence societal views on justice and\nmorality. Analyzing these biases requires a holistic approach that captures the\nemotional tone, moral framing, and specific events within the narratives. In\nthis work we introduce E2MoCase, a novel dataset designed to facilitate the\nintegrated analysis of emotions, moral values, and events within legal\nnarratives and media coverage. By leveraging advanced models for emotion\ndetection, moral value identification, and event extraction, E2MoCase offers a\nmulti-dimensional perspective on how legal cases are portrayed in news\narticles.\n", "date": "2024-09-13", "categories": []}
{"id": "2409.07684", "title": "Modeling Information Narrative Detection and Evolution on Telegram\n  during the Russia-Ukraine War", "abstract": "  Following the Russian Federation's full-scale invasion of Ukraine in February\n2022, a multitude of information narratives emerged within both pro-Russian and\npro-Ukrainian communities online. As the conflict progresses, so too do the\ninformation narratives, constantly adapting and influencing local and global\ncommunity perceptions and attitudes. This dynamic nature of the evolving\ninformation environment (IE) underscores a critical need to fully discern how\nnarratives evolve and affect online communities. Existing research, however,\noften fails to capture information narrative evolution, overlooking both the\nfluid nature of narratives and the internal mechanisms that drive their\nevolution. Recognizing this, we introduce a novel approach designed to both\nmodel narrative evolution and uncover the underlying mechanisms driving them.\nIn this work we perform a comparative discourse analysis across communities on\nTelegram covering the initial three months following the invasion. First, we\nuncover substantial disparities in narratives and perceptions between\npro-Russian and pro-Ukrainian communities. Then, we probe deeper into prevalent\nnarratives of each group, identifying key themes and examining the underlying\nmechanisms fueling their evolution. Finally, we explore influences and factors\nthat may shape the development and spread of narratives.\n", "date": "2024-09-12", "categories": []}
{"id": "2409.06540", "title": "Mapping News Narratives Using LLMs and Narrative-Structured Text\n  Embeddings", "abstract": "  Given the profound impact of narratives across various societal levels, from\npersonal identities to international politics, it is crucial to understand\ntheir distribution and development over time. This is particularly important in\nonline spaces. On the Web, narratives can spread rapidly and intensify societal\ndivides and conflicts. While many qualitative approaches exist, quantifying\nnarratives remains a significant challenge. Computational narrative analysis\nlacks frameworks that are both comprehensive and generalizable. To address this\ngap, we introduce a numerical narrative representation grounded in\nstructuralist linguistic theory. Chiefly, Greimas' Actantial Model represents a\nnarrative through a constellation of six functional character roles. These\nso-called actants are genre-agnostic, making the model highly generalizable. We\nextract the actants using an open-source LLM and integrate them into a\nNarrative-Structured Text Embedding that captures both the semantics and\nnarrative structure of a text. We demonstrate the analytical insights of the\nmethod on the example of 5000 full-text news articles from Al Jazeera and The\nWashington Post on the Israel-Palestine conflict. Our method successfully\ndistinguishes articles that cover the same topics but differ in narrative\nstructure.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.03948", "title": "The Veracity Problem: Detecting False Information and its Propagation on\n  Online Social Media Networks", "abstract": "  Detecting false information on social media is critical in mitigating its\nnegative societal impacts. To reduce the propagation of false information,\nautomated detection provide scalable, unbiased, and cost-effective methods.\nHowever, there are three potential research areas identified which once solved\nimprove detection. First, current AI-based solutions often provide a\nuni-dimensional analysis on a complex, multi-dimensional issue, with solutions\ndiffering based on the features used. Furthermore, these methods do not account\nfor the temporal and dynamic changes observed within the document's life cycle.\nSecond, there has been little research on the detection of coordinated\ninformation campaigns and in understanding the intent of the actors and the\ncampaign. Thirdly, there is a lack of consideration of cross-platform analysis,\nwith existing datasets focusing on a single platform, such as X, and detection\nmodels designed for specific platform.\n  This work aims to develop methods for effective detection of false\ninformation and its propagation. To this end, firstly we aim to propose the\ncreation of an ensemble multi-faceted framework that leverages multiple aspects\nof false information. Secondly, we propose a method to identify actors and\ntheir intent when working in coordination to manipulate a narrative. Thirdly,\nwe aim to analyse the impact of cross-platform interactions on the propagation\nof false information via the creation of a new dataset.\n", "date": "2024-09-06", "categories": []}
{"id": "2409.04085", "title": "Structure and dynamics of growing networks of Reddit threads", "abstract": "  Millions of people use online social networks to reinforce their sense of\nbelonging, for example by giving and asking for feedback as a form of social\nvalidation and self-recognition. It is common to observe disagreement among\npeople beliefs and points of view when expressing this feedback. Modeling and\nanalyzing such interactions is crucial to understand social phenomena that\nhappen when people face different opinions while expressing and discussing\ntheir values. In this work, we study a Reddit community in which people\nparticipate to judge or be judged with respect to some behavior, as it\nrepresents a valuable source to study how users express judgments online. We\nmodel threads of this community as complex networks of user interactions\ngrowing in time, and we analyze the evolution of their structural properties.\nWe show that the evolution of Reddit networks differ from other real social\nnetworks, despite falling in the same category. This happens because their\nglobal clustering coefficient is extremely small and the average shortest path\nlength increases over time. Such properties reveal how users discuss in\nthreads, i.e. with mostly one other user and often by a single message. We\nstrengthen such result by analyzing the role that disagreement and reciprocity\nplay in such conversations. We also show that Reddit thread's evolution over\ntime is governed by two subgraphs growing at different speeds. We discover\nthat, in the studied community, the difference of such speed is higher than in\nother communities because of the user guidelines enforcing specific user\ninteractions. Finally, we interpret the obtained results on user behavior\ndrawing back to Social Judgment Theory.\n", "date": "2024-09-06", "categories": []}
{"id": "2408.13651", "title": "Narratives at Conflict: Computational Analysis of News Framing in\n  Multilingual Disinformation Campaigns", "abstract": "  Any report frames issues to favor a particular interpretation by highlighting\nor excluding certain aspects of a story. Despite the widespread use of framing\nin disinformation, framing properties and detection methods remain\nunderexplored outside the English-speaking world. We explore how multilingual\nframing of the same issue differs systematically. We use eight years of\nRussia-backed disinformation campaigns, spanning 8k news articles in 4\nlanguages targeting 15 countries. We find that disinformation campaigns\nconsistently and intentionally favor specific framing, depending on the target\nlanguage of the audience. We further discover how Russian-language articles\nconsistently highlight selected frames depending on the region of the media\ncoverage. We find that the two most prominent models for automatic frame\nanalysis underperform and show high disagreement, highlighting the need for\nfurther research.\n", "date": "2024-08-24", "categories": []}
{"id": "2408.16147", "title": "Improving the Prediction of Individual Engagement in Recommendations\n  Using Cognitive Models", "abstract": "  For public health programs with limited resources, the ability to predict how\nbehaviors change over time and in response to interventions is crucial for\ndeciding when and to whom interventions should be allocated. Using data from a\nreal-world maternal health program, we demonstrate how a cognitive model based\non Instance-Based Learning (IBL) Theory can augment existing purely\ncomputational approaches. Our findings show that, compared to general\ntime-series forecasters (e.g., LSTMs), IBL models, which reflect human\ndecision-making processes, better predict the dynamics of individuals' states.\nAdditionally, IBL provides estimates of the volatility in individuals' states\nand their sensitivity to interventions, which can improve the efficiency of\ntraining of other time series models.\n", "date": "2024-08-28", "categories": []}
{"id": "2409.02690", "title": "Detecting Calls to Action in Multimodal Content: Analysis of the 2021\n  German Federal Election Campaign on Instagram", "abstract": "  This study investigates the automated classification of Calls to Action\n(CTAs) within the 2021 German Instagram election campaign to advance the\nunderstanding of mobilization in social media contexts. We analyzed over 2,208\nInstagram stories and 712 posts using fine-tuned BERT models and OpenAI's GPT-4\nmodels. The fine-tuned BERT model incorporating synthetic training data\nachieved a macro F1 score of 0.93, demonstrating a robust classification\nperformance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of\nstories contained CTAs, highlighting significant differences in mobilization\nstrategies between these content types. Additionally, we found that FDP and the\nGreens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in\nstory CTAs.\n", "date": "2024-09-04", "categories": []}
{"id": "2409.00216", "title": "Structuring Quantitative Image Analysis with Object Prominence", "abstract": "  When photographers and other editors of image material produce an image, they\nmake a statement about what matters by situating some objects in the foreground\nand others in the background. While this prominence of objects is a key\nanalytical category to qualitative scholars, recent quantitative approaches to\nautomated image analysis have not yet made this important distinction but treat\nall areas of an image similarly. We suggest carefully considering objects'\nprominence as an essential step in analyzing images as data. Its modeling\nrequires defining an object and operationalizing and measuring how much\nattention a human eye would pay. Our approach combines qualitative analyses\nwith the scalability of quantitative approaches. Exemplifying object prominence\nwith different implementations -- object size and centeredness, the pixels'\nimage depth, and salient image regions -- we showcase the usefulness of our\napproach with two applications. First, we scale the ideology of eight US\nnewspapers based on images. Second, we analyze the prominence of women in the\ncampaign videos of the U.S. presidential races in 2016 and 2020. We hope that\nour article helps all keen to study image data in a conceptually meaningful way\nat scale.\n", "date": "2024-08-30", "categories": []}
{"id": "2409.00272", "title": "Finding frames with BERT: A transformer-based approach to generic news\n  frame detection", "abstract": "  Framing is among the most extensively used concepts in the field of\ncommunication science. The availability of digital data offers new\npossibilities for studying how specific aspects of social reality are made more\nsalient in online communication but also raises challenges related to the\nscaling of framing analysis and its adoption to new research areas (e.g.\nstudying the impact of artificial intelligence-powered systems on\nrepresentation of societally relevant issues). To address these challenges, we\nintroduce a transformer-based approach for generic news frame detection in\nAnglophone online content. While doing so, we discuss the composition of the\ntraining and test datasets, the model architecture, and the validation of the\napproach and reflect on the possibilities and limitations of the automated\ndetection of generic news frames.\n", "date": "2024-08-30", "categories": []}
{"id": "2406.12614", "title": "EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin\n  Disinformation in News Articles", "abstract": "  This work introduces EUvsDisinfo, a multilingual dataset of disinformation\narticles originating from pro-Kremlin outlets, along with trustworthy articles\nfrom credible / less biased sources. It is sourced directly from the debunk\narticles written by experts leading the EUvsDisinfo project. Our dataset is the\nlargest to-date resource in terms of the overall number of articles and\ndistinct languages. It also provides the largest topical and temporal coverage.\nUsing this dataset, we investigate the dissemination of pro-Kremlin\ndisinformation across different languages, uncovering language-specific\npatterns targeting certain disinformation topics. We further analyse the\nevolution of topic distribution over an eight-year period, noting a significant\nsurge in disinformation content before the full-scale invasion of Ukraine in\n2022. Lastly, we demonstrate the dataset's applicability in training models to\neffectively distinguish between disinformation and trustworthy content in\nmultilingual settings.\n", "date": "2024-06-18", "categories": []}
{"id": "2408.16942", "title": "A longitudinal sentiment analysis of Sinophobia during COVID-19 using\n  large language models", "abstract": "  The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia,\nleading to widespread discrimination against individuals of Chinese descent.\nLarge language models (LLMs) are pre-trained deep learning models used for\nnatural language processing (NLP) tasks. The ability of LLMs to understand and\ngenerate human-like text makes them particularly useful for analysing social\nmedia data to detect and evaluate sentiments. We present a sentiment analysis\nframework utilising LLMs for longitudinal sentiment analysis of the Sinophobic\nsentiments expressed in X (Twitter) during the COVID-19 pandemic. The results\nshow a significant correlation between the spikes in Sinophobic tweets,\nSinophobic sentiments and surges in COVID-19 cases, revealing that the\nevolution of the pandemic influenced public sentiment and the prevalence of\nSinophobic discourse. Furthermore, the sentiment analysis revealed a\npredominant presence of negative sentiments, such as annoyance and denial,\nwhich underscores the impact of political narratives and misinformation shaping\npublic opinion. The lack of empathetic sentiment which was present in previous\nstudies related to COVID-19 highlights the way the political narratives in\nmedia viewed the pandemic and how it blamed the Chinese community. Our study\nhighlights the importance of transparent communication in mitigating xenophobic\nsentiments during global crises.\n", "date": "2024-08-29", "categories": []}
{"id": "2306.07457", "title": "Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from\n  Web Search Logs", "abstract": "  To design effective vaccine policies, policymakers need detailed data about\nwho has been vaccinated, who is holding out, and why. However, existing data in\nthe US are insufficient: reported vaccination rates are often delayed or\nmissing, and surveys of vaccine hesitancy are limited by high-level questions\nand self-report biases. Here, we show how large-scale search engine logs and\nmachine learning can be leveraged to fill these gaps and provide novel insights\nabout vaccine intentions and behaviors. First, we develop a vaccine intent\nclassifier that can accurately detect when a user is seeking the COVID-19\nvaccine on search. Our classifier demonstrates strong agreement with CDC\nvaccination rates, with correlations above 0.86, and estimates vaccine intent\nrates to the level of ZIP codes in real time, allowing us to pinpoint more\ngranular trends in vaccine seeking across regions, demographics, and time. To\ninvestigate vaccine hesitancy, we use our classifier to identify two groups,\nvaccine early adopters and vaccine holdouts. We find that holdouts, compared to\nearly adopters matched on covariates, are 69% more likely to click on untrusted\nnews sites. Furthermore, we organize 25,000 vaccine-related URLs into a\nhierarchical ontology of vaccine concerns, and we find that holdouts are far\nmore concerned about vaccine requirements, vaccine development and approval,\nand vaccine myths, and even within holdouts, concerns vary significantly across\ndemographic groups. Finally, we explore the temporal dynamics of vaccine\nconcerns and vaccine seeking, and find that key indicators emerge when\nindividuals convert from holding out to preparing to accept the vaccine.\n", "date": "2023-06-12", "categories": []}
{"id": "2408.16089", "title": "Is Personality Prediction Possible Based on Reddit Comments?", "abstract": "  In this assignment, we examine whether there is a correlation between the\npersonality type of a person and the texts they wrote. In order to do this, we\naggregated datasets of Reddit comments labeled with the Myers-Briggs Type\nIndicator (MBTI) of the author and built different supervised classifiers based\non BERT to try to predict the personality of an author given a text. Despite\nexperiencing issues with the unfiltered character of the dataset, we can\nobserve potential in the classification.\n", "date": "2024-08-28", "categories": []}
{"id": "2311.11844", "title": "Towards Human-Level Text Coding with LLMs: The Case of Fatherhood Roles\n  in Public Policy Documents", "abstract": "  Recent advances in large language models (LLMs) like GPT-3.5 and GPT-4\npromise automation with better results and less programming, opening up new\nopportunities for text analysis in political science. In this study, we\nevaluate LLMs on three original coding tasks involving typical complexities\nencountered in political science settings: a non-English language, legal and\npolitical jargon, and complex labels based on abstract constructs. Along the\npaper, we propose a practical workflow to optimize the choice of the model and\nthe prompt. We find that the best prompting strategy consists of providing the\nLLMs with a detailed codebook, as the one provided to human coders. In this\nsetting, an LLM can be as good as or possibly better than a human annotator\nwhile being much faster, considerably cheaper, and much easier to scale to\nlarge amounts of text. We also provide a comparison of GPT and popular\nopen-source LLMs, discussing the trade-offs in the model's choice. Our software\nallows LLMs to be easily used as annotators and is publicly available:\nhttps://github.com/lorelupo/pappa.\n", "date": "2023-11-20", "categories": []}
{"id": "2408.15213", "title": "Classifying populist language in American presidential and governor\n  speeches using automatic text analysis", "abstract": "  Populism is a concept that is often used but notoriously difficult to\nmeasure. Common qualitative measurements like holistic grading or content\nanalysis require great amounts of time and labour, making it difficult to\nquickly scope out which politicians should be classified as populist and which\nshould not, while quantitative methods show mixed results when it comes to\nclassifying populist rhetoric. In this paper, we develop a pipeline to train\nand validate an automated classification model to estimate the use of populist\nlanguage. We train models based on sentences that were identified as populist\nand pluralist in 300 US governors' speeches from 2010 to 2018 and in 45\nspeeches of presidential candidates in 2016. We find that these models classify\nmost speeches correctly, including 84% of governor speeches and 89% of\npresidential speeches. These results extend to different time periods (with 92%\naccuracy on more recent American governors), different amounts of data (with as\nfew as 70 training sentences per category achieving similar results), and when\nclassifying politicians instead of individual speeches. This pipeline is thus\nan effective tool that can optimise the systematic and swift classification of\nthe use of populist language in politicians' speeches.\n", "date": "2024-08-27", "categories": []}
{"id": "2408.07052", "title": "The News Comment Gap and Algorithmic Agenda Setting in Online Forums", "abstract": "  The disparity between news stories valued by journalists and those preferred\nby readers, known as the \"News Gap\", is well-documented. However, the\ndifference in expectations regarding news related user-generated content is\nless studied. Comment sections, hosted by news websites, are popular venues for\nreader engagement, yet still subject to editorial decisions. It is thus\nimportant to understand journalist vs reader comment preferences and how these\nare served by various comment ranking algorithms that represent discussions\ndifferently. We analyse 1.2 million comments from Austrian newspaper Der\nStandard to understand the \"News Comment Gap\" and the effects of different\nranking algorithms. We find that journalists prefer positive, timely, complex,\ndirect responses, while readers favour comments similar to article content from\nelite authors. We introduce the versatile Feature-Oriented Ranking Utility\nMetric (FORUM) to assess the impact of different ranking algorithms and find\ndramatic differences in how they prioritise the display of comments by\nsentiment, topical relevance, lexical diversity, and readability. Journalists\ncan exert substantial influence over the discourse through both curatorial and\nalgorithmic means. Understanding these choices' implications is vital in\nfostering engaging and civil discussions while aligning with journalistic\nobjectives, especially given the increasing legal scrutiny and societal\nimportance of online discourse.\n", "date": "2024-08-13", "categories": []}
{"id": "2408.11936", "title": "Estimating Contribution Quality in Online Deliberations Using a Large\n  Language Model", "abstract": "  Deliberation involves participants exchanging knowledge, arguments, and\nperspectives and has been shown to be effective at addressing polarization. The\nStanford Online Deliberation Platform facilitates large-scale deliberations. It\nenables video-based online discussions on a structured agenda for small groups\nwithout requiring human moderators. This paper's data comes from various\ndeliberation events, including one conducted in collaboration with Meta in 32\ncountries, and another with 38 post-secondary institutions in the US.\n  Estimating the quality of contributions in a conversation is crucial for\nassessing feature and intervention impacts. Traditionally, this is done by\nhuman annotators, which is time-consuming and costly. We use a large language\nmodel (LLM) alongside eight human annotators to rate contributions based on\njustification, novelty, expansion of the conversation, and potential for\nfurther expansion, with scores ranging from 1 to 5. Annotators also provide\nbrief justifications for their ratings. Using the average rating from other\nhuman annotators as the ground truth, we find the model outperforms individual\nhuman annotators. While pairs of human annotators outperform the model in\nrating justification and groups of three outperform it on all four metrics, the\nmodel remains competitive.\n  We illustrate the usefulness of the automated quality rating by assessing the\neffect of nudges on the quality of deliberation. We first observe that\nindividual nudges after prolonged inactivity are highly effective, increasing\nthe likelihood of the individual requesting to speak in the next 30 seconds by\n65%. Using our automated quality estimation, we show that the quality ratings\nfor statements prompted by nudging are similar to those made without nudging,\nsignifying that nudging leads to more ideas being generated in the conversation\nwithout losing overall quality.\n", "date": "2024-08-21", "categories": []}
{"id": "2406.03390", "title": "What Drives Online Popularity: Author, Content or Sharers? Estimating\n  Spread Dynamics with Bayesian Mixture Hawkes", "abstract": "  The spread of content on social media is shaped by intertwining factors on\nthree levels: the source, the content itself, and the pathways of content\nspread. At the lowest level, the popularity of the sharing user determines its\neventual reach. However, higher-level factors such as the nature of the online\nitem and the credibility of its source also play crucial roles in determining\nhow widely and rapidly the online item spreads. In this work, we propose the\nBayesian Mixture Hawkes (BMH) model to jointly learn the influence of source,\ncontent and spread. We formulate the BMH model as a hierarchical mixture model\nof separable Hawkes processes, accommodating different classes of Hawkes\ndynamics and the influence of feature sets on these classes. We test the BMH\nmodel on two learning tasks, cold-start popularity prediction and temporal\nprofile generalization performance, applying to two real-world retweet cascade\ndatasets referencing articles from controversial and traditional media\npublishers. The BMH model outperforms the state-of-the-art models and\npredictive baselines on both datasets and utilizes cascade- and item-level\ninformation better than the alternatives. Lastly, we perform a counter-factual\nanalysis where we apply the trained publisher-level BMH models to a set of\narticle headlines and show that effectiveness of headline writing style\n(neutral, clickbait, inflammatory) varies across publishers. The BMH model\nunveils differences in style effectiveness between controversial and reputable\npublishers, where we find clickbait to be notably more effective for reputable\npublishers as opposed to controversial ones, which links to the latter's\noveruse of clickbait.\n", "date": "2024-06-05", "categories": []}
{"id": "2408.11174", "title": "Combining Objective and Subjective Perspectives for Political News\n  Understanding", "abstract": "  Researchers and practitioners interested in computational politics rely on\nautomatic content analysis tools to make sense of the large amount of political\ntexts available on the Web. Such tools should provide objective and subjective\naspects at different granularity levels to make the analyses useful in\npractice. Existing methods produce interesting insights for objective aspects,\nbut are limited for subjective ones, are often limited to national contexts,\nand have limited explainability. We introduce a text analysis framework which\nintegrates both perspectives and provides a fine-grained processing of\nsubjective aspects. Information retrieval techniques and knowledge bases\ncomplement powerful natural language processing components to allow a flexible\naggregation of results at different granularity levels. Importantly, the\nproposed bottom-up approach facilitates the explainability of the obtained\nresults. We illustrate its functioning with insights on news outlets, political\norientations, topics, individual entities, and demographic segments. The\napproach is instantiated on a large corpus of French news, but is designed to\nwork seamlessly for other languages and countries.\n", "date": "2024-08-20", "categories": []}
{"id": "2402.11895", "title": "Bridging or Breaking: Impact of Intergroup Interactions on Religious\n  Polarization", "abstract": "  While exposure to diverse viewpoints may reduce polarization, it can also\nhave a backfire effect and exacerbate polarization when the discussion is\nadversarial. Here, we examine the question whether intergroup interactions\naround important events affect polarization between majority and minority\ngroups in social networks. We compile data on the religious identity of nearly\n700,000 Indian Twitter users engaging in COVID-19-related discourse during\n2020. We introduce a new measure for an individual's group conformity based on\ncontextualized embeddings of tweet text, which helps us assess polarization\nbetween religious groups. We then use a meta-learning framework to examine\nheterogeneous treatment effects of intergroup interactions on an individual's\ngroup conformity in the light of communal, political, and socio-economic\nevents. We find that for political and social events, intergroup interactions\nreduce polarization. This decline is weaker for individuals at the extreme who\nalready exhibit high conformity to their group. In contrast, during communal\nevents, intergroup interactions can increase group conformity. Finally, we\ndecompose the differential effects across religious groups in terms of emotions\nand topics of discussion. The results show that the dynamics of religious\npolarization are sensitive to the context and have important implications for\nunderstanding the role of intergroup interactions.\n", "date": "2024-02-19", "categories": []}
{"id": "2402.10230", "title": "Temporal Analysis of Drifting Hashtags in Textual Data Streams: A\n  Graph-Based Application", "abstract": "  Initially supported by Twitter, hashtags are now used on several social media\nplatforms. Hashtags are helpful for tagging, tracking, and grouping posts on\nsimilar topics. In this paper, based on a hashtag stream regarding the hashtag\n#mybodymychoice, we analyze hashtag drifts over time using concepts from graph\nanalysis and textual data streams using the Girvan-Newman method to uncover\nhashtag communities in annual snapshots between 2018 and 2022. In addition, we\noffer insights about some correlated hashtags found in the study. Our approach\ncan be useful for monitoring changes over time in opinions and sentiment\npatterns about an entity on social media. Even though the hashtag\n#mybodymychoice was initially coupled with women's rights, abortion, and bodily\nautonomy, we observe that it suffered drifts during the studied period across\ntopics such as drug legalization, vaccination, political protests, war, and\ncivil rights. The year 2021 was the most significant drifting year, in which\nthe communities detected and their respective sizes suggest that\n#mybodymychoice had a significant drift to vaccination and Covid-19-related\ntopics.\n", "date": "2024-02-08", "categories": []}
{"id": "2402.14424", "title": "Automating psychological hypothesis generation with AI: when large\n  language models meet causal graph", "abstract": "  Leveraging the synergy between causal knowledge graphs and a large language\nmodel (LLM), our study introduces a groundbreaking approach for computational\nhypothesis generation in psychology. We analyzed 43,312 psychology articles\nusing a LLM to extract causal relation pairs. This analysis produced a\nspecialized causal graph for psychology. Applying link prediction algorithms,\nwe generated 130 potential psychological hypotheses focusing on `well-being',\nthen compared them against research ideas conceived by doctoral scholars and\nthose produced solely by the LLM. Interestingly, our combined approach of a LLM\nand causal graphs mirrored the expert-level insights in terms of novelty,\nclearly surpassing the LLM-only hypotheses (t(59) = 3.34, p=0.007 and t(59) =\n4.32, p<0.001, respectively). This alignment was further corroborated using\ndeep semantic analysis. Our results show that combining LLM with machine\nlearning techniques such as causal knowledge graphs can revolutionize automated\ndiscovery in psychology, extracting novel insights from the extensive\nliterature. This work stands at the crossroads of psychology and artificial\nintelligence, championing a new enriched paradigm for data-driven hypothesis\ngeneration in psychological research.\n", "date": "2024-02-22", "categories": []}
{"id": "2408.06900", "title": "Entendre, a Social Bot Detection Tool for Niche, Fringe, and Extreme\n  Social Media", "abstract": "  Social bots-automated accounts that generate and spread content on social\nmedia-are exploiting vulnerabilities in these platforms to manipulate public\nperception and disseminate disinformation. This has prompted the development of\npublic bot detection services; however, most of these services focus primarily\non Twitter, leaving niche platforms vulnerable. Fringe social media platforms\nsuch as Parler, Gab, and Gettr often have minimal moderation, which facilitates\nthe spread of hate speech and misinformation. To address this gap, we introduce\nEntendre, an open-access, scalable, and platform-agnostic bot detection\nframework. Entendre can process a labeled dataset from any social platform to\nproduce a tailored bot detection model using a random forest classification\napproach, ensuring robust social bot detection. We exploit the idea that most\nsocial platforms share a generic template, where users can post content,\napprove content, and provide a bio (common data features). By emphasizing\ngeneral data features over platform-specific ones, Entendre offers rapid\nextensibility at the expense of some accuracy. To demonstrate Entendre's\neffectiveness, we used it to explore the presence of bots among accounts\nposting racist content on the now-defunct right-wing platform Parler. We\nexamined 233,000 posts from 38,379 unique users and found that 1,916 unique\nusers (4.99%) exhibited bot-like behavior. Visualization techniques further\nrevealed that these bots significantly impacted the network, amplifying\ninfluential rhetoric and hashtags (e.g., #qanon, #trump, #antilgbt). These\npreliminary findings underscore the need for tools like Entendre to monitor and\nassess bot activity across diverse platforms.\n", "date": "2024-08-13", "categories": []}
{"id": "2407.12451", "title": "Across Platforms and Languages: Dutch Influencers and Legal Disclosures\n  on Instagram, YouTube and TikTok", "abstract": "  Content monetization on social media fuels a growing influencer economy.\nInfluencer marketing remains largely undisclosed or inappropriately disclosed\non social media. Non-disclosure issues have become a priority for national and\nsupranational authorities worldwide, who are starting to impose increasingly\nharsher sanctions on them. This paper proposes a transparent methodology for\nmeasuring whether and how influencers comply with disclosures based on legal\nstandards. We introduce a novel distinction between disclosures that are\nlegally sufficient (green) and legally insufficient (yellow). We apply this\nmethodology to an original dataset reflecting the content of 150 Dutch\ninfluencers publicly registered with the Dutch Media Authority based on\nrecently introduced registration obligations. The dataset consists of 292,315\nposts and is multi-language (English and Dutch) and cross-platform (Instagram,\nYouTube and TikTok). We find that influencer marketing remains generally\nunderdisclosed on social media, and that bigger influencers are not necessarily\nmore compliant with disclosure standards.\n", "date": "2024-07-17", "categories": []}
{"id": "2408.04463", "title": "Crowd Intelligence for Early Misinformation Prediction on Social Media", "abstract": "  Misinformation spreads rapidly on social media, causing serious damage by\ninfluencing public opinion, promoting dangerous behavior, or eroding trust in\nreliable sources. It spreads too fast for traditional fact-checking, stressing\nthe need for predictive methods. We introduce CROWDSHIELD, a crowd\nintelligence-based method for early misinformation prediction. We hypothesize\nthat the crowd's reactions to misinformation reveal its accuracy. Furthermore,\nwe hinge upon exaggerated assertions/claims and replies with particular\npositions/stances on the source post within a conversation thread. We employ\nQ-learning to capture the two dimensions -- stances and claims. We utilize deep\nQ-learning due to its proficiency in navigating complex decision spaces and\neffectively learning network properties. Additionally, we use a\ntransformer-based encoder to develop a comprehensive understanding of both\ncontent and context. This multifaceted approach helps ensure the model pays\nattention to user interaction and stays anchored in the communication's\ncontent. We propose MIST, a manually annotated misinformation detection Twitter\ncorpus comprising nearly 200 conversation threads with more than 14K replies.\nIn experiments, CROWDSHIELD outperformed ten baseline systems, achieving an\nimprovement of ~4% macro-F1 score. We conduct an ablation study and error\nanalysis to validate our proposed model's performance. The source code and\ndataset are available at https://github.com/LCS2-IIITD/CrowdShield.git.\n", "date": "2024-08-08", "categories": []}
{"id": "2402.08318", "title": "Values That Are Explicitly Present in Fairy Tales: Comparing Samples\n  from German, Italian and Portuguese Traditions", "abstract": "  Looking at how social values are represented in fairy tales can give insights\nabout the variations in communication of values across cultures. We study how\nvalues are communicated in fairy tales from Portugal, Italy and Germany using a\ntechnique called word embedding with a compass to quantify vocabulary\ndifferences and commonalities. We study how these three national traditions\ndiffer in their explicit references to values. To do this, we specify a list of\nvalue-charged tokens, consider their word stems and analyse the distance\nbetween these in a bespoke pre-trained Word2Vec model. We triangulate and\ncritically discuss the validity of the resulting hypotheses emerging from this\nquantitative model. Our claim is that this is a reusable and reproducible\nmethod for the study of the values explicitly referenced in historical corpora.\nFinally, our preliminary findings hint at a shared cultural understanding and\nthe expression of values such as Benevolence, Conformity, and Universalism\nacross the studied cultures, suggesting the potential existence of a\npan-European cultural memory.\n", "date": "2024-02-13", "categories": []}
{"id": "2402.19135", "title": "Think Fast, Think Slow, Think Critical: Designing an Automated\n  Propaganda Detection Tool", "abstract": "  In today's digital age, characterized by rapid news consumption and\nincreasing vulnerability to propaganda, fostering citizens' critical thinking\nis crucial for stable democracies. This paper introduces the design of\nClarifAI, a novel automated propaganda detection tool designed to nudge readers\ntowards more critical news consumption by activating the analytical mode of\nthinking, following Kahneman's dual-system theory of cognition. Using Large\nLanguage Models, ClarifAI detects propaganda in news articles and provides\ncontext-rich explanations, enhancing users' understanding and critical\nthinking. Our contribution is threefold: first, we propose the design of\nClarifAI; second, in an online experiment, we demonstrate that this design\neffectively encourages news readers to engage in more critical reading; and\nthird, we emphasize the value of explanations for fostering critical thinking.\nThe study thus offers both a practical tool and useful design knowledge for\nmitigating propaganda in digital news.\n", "date": "2024-02-29", "categories": []}
{"id": "2408.02948", "title": "Are Female Carpenters like Blue Bananas? A Corpus Investigation of\n  Occupation Gender Typicality", "abstract": "  People tend to use language to mention surprising properties of events: for\nexample, when a banana is blue, we are more likely to mention color than when\nit is yellow. This fact is taken to suggest that yellowness is somehow a\ntypical feature of bananas, and blueness is exceptional. Similar to how a\nyellow color is typical of bananas, there may also be genders that are typical\nof occupations. In this work, we explore this question using information\ntheoretic techniques coupled with corpus statistic analysis. In two distinct\nlarge corpora, we do not find strong evidence that occupations and gender\ndisplay the same patterns of mentioning as do bananas and color. Instead, we\nfind that gender mentioning is correlated with femaleness of occupation in\nparticular, suggesting perhaps that woman-dominated occupations are seen as\nsomehow ``more gendered'' than male-dominated ones, and thereby they encourage\nmore gender mentioning overall.\n", "date": "2024-08-06", "categories": []}
{"id": "2408.01911", "title": "Brief state of the art in social information mining: Practical\n  application in analysis of trends in French legislative 2024", "abstract": "  The analysis of social media information has undergone significant evolution\nin the last decade due to advancements in artificial intelligence (AI) and\nmachine learning (ML). This paper provides an overview of the state-of-the-art\ntechniques in social media mining, with a practical application in analyzing\ntrends in the 2024 French legislative elections. We leverage natural language\nprocessing (NLP) tools to gauge public opinion by extracting and analyzing\ncomments and reactions from the AgoraVox platform. The study reveals that the\nNational Rally party, led by Marine Le Pen, maintains a high level of\nengagement on social media, outperforming traditional parties. This trend is\ncorroborated by user interactions, indicating a strong digital presence. The\nresults highlight the utility of advanced AI models, such as transformers and\nlarge language models (LLMs), in capturing nuanced public sentiments and\npredicting political leanings, demonstrating their potential in real-time\nreputation management and crisis response.\n", "date": "2024-07-11", "categories": []}
{"id": "2408.00818", "title": "Y Social: an LLM-powered Social Media Digital Twin", "abstract": "  In this paper we introduce Y, a new-generation digital twin designed to\nreplicate an online social media platform. Digital twins are virtual replicas\nof physical systems that allow for advanced analyses and experimentation. In\nthe case of social media, a digital twin such as Y provides a powerful tool for\nresearchers to simulate and understand complex online interactions. {\\tt Y}\nleverages state-of-the-art Large Language Models (LLMs) to replicate\nsophisticated agent behaviors, enabling accurate simulations of user\ninteractions, content dissemination, and network dynamics. By integrating these\naspects, Y offers valuable insights into user engagement, information spread,\nand the impact of platform policies. Moreover, the integration of LLMs allows Y\nto generate nuanced textual content and predict user responses, facilitating\nthe study of emergent phenomena in online environments.\n  To better characterize the proposed digital twin, in this paper we describe\nthe rationale behind its implementation, provide examples of the analyses that\ncan be performed on the data it enables to be generated, and discuss its\nrelevance for multidisciplinary research.\n", "date": "2024-08-01", "categories": []}
{"id": "2405.03452", "title": "Large Language Models (LLMs) as Agents for Augmented Democracy", "abstract": "  We explore an augmented democracy system built on off-the-shelf LLMs\nfine-tuned to augment data on citizen's preferences elicited over policies\nextracted from the government programs of the two main candidates of Brazil's\n2022 presidential election. We use a train-test cross-validation setup to\nestimate the accuracy with which the LLMs predict both: a subject's individual\npolitical choices and the aggregate preferences of the full sample of\nparticipants. At the individual level, we find that LLMs predict out of sample\npreferences more accurately than a \"bundle rule\", which would assume that\ncitizens always vote for the proposals of the candidate aligned with their\nself-reported political orientation. At the population level, we show that a\nprobabilistic sample augmented by an LLM provides a more accurate estimate of\nthe aggregate preferences of a population than the non-augmented probabilistic\nsample alone. Together, these results indicates that policy preference data\naugmented using LLMs can capture nuances that transcend party lines and\nrepresents a promising avenue of research for data augmentation.\n", "date": "2024-05-06", "categories": []}
{"id": "2408.00534", "title": "The Monetisation of Toxicity: Analysing YouTube Content Creators and\n  Controversy-Driven Engagement", "abstract": "  YouTube is a major social media platform that plays a significant role in\ndigital culture, with content creators at its core. These creators often engage\nin controversial behaviour to drive engagement, which can foster toxicity. This\npaper presents a quantitative analysis of controversial content on YouTube,\nfocusing on the relationship between controversy, toxicity, and monetisation.\nWe introduce a curated dataset comprising 20 controversial YouTube channels\nextracted from Reddit discussions, including 16,349 videos and more than 105\nmillion comments. We identify and categorise monetisation cues from video\ndescriptions into various models, including affiliate marketing and direct\nselling, using lists of URLs and keywords. Additionally, we train a machine\nlearning model to measure the toxicity of comments in these videos. Our\nfindings reveal that while toxic comments correlate with higher engagement,\nthey negatively impact monetisation, indicating that controversy-driven\ninteraction does not necessarily lead to financial gain. We also observed\nsignificant variation in monetisation strategies, with some creators showing\nextensive monetisation despite high toxicity levels. Our study introduces a\ncurated dataset, lists of URLs and keywords to categorise monetisation, a\nmachine learning model to measure toxicity, and is a significant step towards\nunderstanding the complex relationship between controversy, engagement, and\nmonetisation on YouTube. The lists used for detecting and categorising\nmonetisation cues are available on https://github.com/thalesbertaglia/toxmon.\n", "date": "2024-08-01", "categories": []}
{"id": "2408.00633", "title": "DisTrack: a new Tool for Semi-automatic Misinformation Tracking in\n  Online Social Networks", "abstract": "  Introduction: This article introduces DisTrack, a methodology and a tool\ndeveloped for tracking and analyzing misinformation within Online Social\nNetworks (OSNs). DisTrack is designed to combat the spread of misinformation\nthrough a combination of Natural Language Processing (NLP) Social Network\nAnalysis (SNA) and graph visualization. The primary goal is to detect\nmisinformation, track its propagation, identify its sources, and assess the\ninfluence of various actors within the network.\n  Methods: DisTrack's architecture incorporates a variety of methodologies\nincluding keyword search, semantic similarity assessments, and graph generation\ntechniques. These methods collectively facilitate the monitoring of\nmisinformation, the categorization of content based on alignment with known\nfalse claims, and the visualization of dissemination cascades through detailed\ngraphs. The tool is tailored to capture and analyze the dynamic nature of\nmisinformation spread in digital environments.\n  Results: The effectiveness of DisTrack is demonstrated through three case\nstudies focused on different themes: discredit/hate speech, anti-vaccine\nmisinformation, and false narratives about the Russia-Ukraine conflict. These\nstudies show DisTrack's capabilities in distinguishing posts that propagate\nfalsehoods from those that counteract them, and tracing the evolution of\nmisinformation from its inception.\n  Conclusions: The research confirms that DisTrack is a valuable tool in the\nfield of misinformation analysis. It effectively distinguishes between\ndifferent types of misinformation and traces their development over time. By\nproviding a comprehensive approach to understanding and combating\nmisinformation in digital spaces, DisTrack proves to be an essential asset for\nresearchers and practitioners working to mitigate the impact of false\ninformation in online social environments.\n", "date": "2024-08-01", "categories": []}
{"id": "2407.20668", "title": "Mimicking the Mavens: Agent-based Opinion Synthesis and Emotion\n  Prediction for Social Media Influencers", "abstract": "  Predicting influencers' views and public sentiment on social media is crucial\nfor anticipating societal trends and guiding strategic responses. This study\nintroduces a novel computational framework to predict opinion leaders'\nperspectives and the emotive reactions of the populace, addressing the inherent\nchallenges posed by the unstructured, context-sensitive, and heterogeneous\nnature of online communication. Our research introduces an innovative module\nthat starts with the automatic 5W1H (Where, Who, When, What, Why, and How)\nquestions formulation engine, tailored to emerging news stories and trending\ntopics. We then build a total of 60 anonymous opinion leader agents in six\ndomains and realize the views generation based on an enhanced large language\nmodel (LLM) coupled with retrieval-augmented generation (RAG). Subsequently, we\nsynthesize the potential views of opinion leaders and predicted the emotional\nresponses to different events. The efficacy of our automated 5W1H module is\ncorroborated by an average GPT-4 score of 8.83/10, indicative of high fidelity.\nThe influencer agents exhibit a consistent performance, achieving an average\nGPT-4 rating of 6.85/10 across evaluative metrics. Utilizing the\n'Russia-Ukraine War' as a case study, our methodology accurately foresees key\ninfluencers' perspectives and aligns emotional predictions with real-world\nsentiment trends in various domains.\n", "date": "2024-07-30", "categories": []}
{"id": "2407.03190", "title": "Cutting through the noise to motivate people: A comprehensive analysis\n  of COVID-19 social media posts de/motivating vaccination", "abstract": "  The COVID-19 pandemic exposed significant weaknesses in the healthcare\ninformation system. The overwhelming volume of misinformation on social media\nand other socioeconomic factors created extraordinary challenges to motivate\npeople to take proper precautions and get vaccinated. In this context, our work\nexplored a novel direction by analyzing an extensive dataset collected over two\nyears, identifying the topics de/motivating the public about COVID-19\nvaccination. We analyzed these topics based on time, geographic location, and\npolitical orientation. We noticed that while the motivating topics remain the\nsame over time and geographic location, the demotivating topics change rapidly.\nWe also identified that intrinsic motivation, rather than external mandate, is\nmore advantageous to inspire the public. This study addresses scientific\ncommunication and public motivation in social media. It can help public health\nofficials, policymakers, and social media platforms develop more effective\nmessaging strategies to cut through the noise of misinformation and educate the\npublic about scientific findings.\n", "date": "2024-06-15", "categories": []}
{"id": "2407.12500", "title": "Automate or Assist? The Role of Computational Models in Identifying\n  Gendered Discourse in US Capital Trial Transcripts", "abstract": "  The language used by US courtroom actors in criminal trials has long been\nstudied for biases. However, systematic studies for bias in high-stakes court\ntrials have been difficult, due to the nuanced nature of bias and the legal\nexpertise required. Large language models offer the possibility to automate\nannotation. But validating the computational approach requires both an\nunderstanding of how automated methods fit in existing annotation workflows and\nwhat they really offer. We present a case study of adding a computational model\nto a complex and high-stakes problem: identifying gender-biased language in US\ncapital trials for women defendants. Our team of experienced death-penalty\nlawyers and NLP technologists pursue a three-phase study: first annotating\nmanually, then training and evaluating computational models, and finally\ncomparing expert annotations to model predictions. Unlike many typical NLP\ntasks, annotating for gender bias in months-long capital trials is complicated,\nwith many individual judgment calls. Contrary to standard arguments for\nautomation that are based on efficiency and scalability, legal experts find the\ncomputational models most useful in providing opportunities to reflect on their\nown bias in annotation and to build consensus on annotation rules. This\nexperience suggests that seeking to replace experts with computational models\nfor complex annotation is both unrealistic and undesirable. Rather,\ncomputational models offer valuable opportunities to assist the legal experts\nin annotation-based studies.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.19196", "title": "Why Misinformation is Created? Detecting them by Integrating Intent\n  Features", "abstract": "  Various social media platforms, e.g., Twitter and Reddit, allow people to\ndisseminate a plethora of information more efficiently and conveniently.\nHowever, they are inevitably full of misinformation, causing damage to diverse\naspects of our daily lives. To reduce the negative impact, timely\nidentification of misinformation, namely Misinformation Detection (MD), has\nbecome an active research topic receiving widespread attention. As a complex\nphenomenon, the veracity of an article is influenced by various aspects. In\nthis paper, we are inspired by the opposition of intents between misinformation\nand real information. Accordingly, we propose to reason the intent of articles\nand form the corresponding intent features to promote the veracity\ndiscrimination of article features. To achieve this, we build a hierarchy of a\nset of intents for both misinformation and real information by referring to the\nexisting psychological theories, and we apply it to reason the intent of\narticles by progressively generating binary answers with an encoder-decoder\nstructure. We form the corresponding intent features and integrate it with the\ntoken features to achieve more discriminative article features for MD. Upon\nthese ideas, we suggest a novel MD method, namely Detecting Misinformation by\nIntegrating Intent featuRes (DM-INTER). To evaluate the performance of\nDM-INTER, we conduct extensive experiments on benchmark MD datasets. The\nexperimental results validate that DM-INTER can outperform the existing\nbaseline MD methods.\n", "date": "2024-07-27", "categories": []}
{"id": "2310.19834", "title": "AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination\n  Datasets based Recommendation System", "abstract": "  Misinformation has emerged as a major societal threat in recent years in\ngeneral; specifically in the context of the COVID-19 pandemic, it has wrecked\nhavoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable\nsolutions for combating misinformation are the need of the hour. This work\nexplored how existing information obtained from social media and augmented with\nmore curated fact checked data repositories can be harnessed to facilitate\nautomated rebuttal of misinformation at scale. While the ideas herein can be\ngeneralized and reapplied in the broader context of misinformation mitigation\nusing a multitude of information sources and catering to the spectrum of social\nmedia platforms, this work serves as a proof of concept, and as such, it is\nconfined in its scope to only rebuttal of tweets, and in the specific context\nof misinformation regarding COVID-19. It leverages two publicly available\ndatasets, viz. FaCov (fact-checked articles) and misleading (social media\nTwitter) data on COVID-19 Vaccination.\n", "date": "2023-10-29", "categories": []}
{"id": "2407.18147", "title": "The FIGNEWS Shared Task on News Media Narratives", "abstract": "  We present an overview of the FIGNEWS shared task, organized as part of the\nArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses\nbias and propaganda annotation in multilingual news posts. We focus on the\nearly days of the Israel War on Gaza as a case study. The task aims to foster\ncollaboration in developing annotation guidelines for subjective tasks by\ncreating frameworks for analyzing diverse narratives highlighting potential\nbias and propaganda. In a spirit of fostering and encouraging diversity, we\naddress the problem from a multilingual perspective, namely within five\nlanguages: English, French, Arabic, Hebrew, and Hindi. A total of 17 teams\nparticipated in two annotation subtasks: bias (16 teams) and propaganda (6\nteams). The teams competed in four evaluation tracks: guidelines development,\nannotation quality, annotation quantity, and consistency. Collectively, the\nteams produced 129,800 data points. Key findings and implications for the field\nare discussed.\n", "date": "2024-07-25", "categories": []}
{"id": "2403.09092", "title": "MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection", "abstract": "  The prevalence of fake news across various online sources has had a\nsignificant influence on the public. Existing Chinese fake news detection\ndatasets are limited to news sourced solely from Weibo. However, fake news\noriginating from multiple sources exhibits diversity in various aspects,\nincluding its content and social context. Methods trained on purely one single\nnews source can hardly be applicable to real-world scenarios. Our pilot\nexperiment demonstrates that the F1 score of the state-of-the-art method that\nlearns from a large Chinese fake news detection dataset, Weibo-21, drops\nsignificantly from 0.943 to 0.470 when the test data is changed to multi-source\nnews data, failing to identify more than one-third of the multi-source fake\nnews. To address this limitation, we constructed the first multi-source\nbenchmark dataset for Chinese fake news detection, termed MCFEND, which is\ncomposed of news we collected from diverse sources such as social platforms,\nmessaging apps, and traditional online news outlets. Notably, such news has\nbeen fact-checked by 14 authoritative fact-checking agencies worldwide. In\naddition, various existing Chinese fake news detection methods are thoroughly\nevaluated on our proposed dataset in cross-source, multi-source, and unseen\nsource ways. MCFEND, as a benchmark dataset, aims to advance Chinese fake news\ndetection approaches in real-world scenarios.\n", "date": "2024-03-14", "categories": []}
{"id": "2407.17425", "title": "Media Manipulations in the Coverage of Events of the Ukrainian\n  Revolution of Dignity: Historical, Linguistic, and Psychological Approaches", "abstract": "  This article examines the use of manipulation in the coverage of events of\nthe Ukrainian Revolution of Dignity in the mass media, namely in the content of\nthe online newspaper Ukrainian Truth (Ukrainska pravda), online newspaper High\nCastle (Vysokyi Zamok), and online newspaper ZIK during the public protest,\nnamely during the Ukrainian Revolution of Dignity. Contents of these online\nnewspapers the historical, linguistic, and psychological approaches are used.\nAlso media manipulations in the coverage of events of the Ukrainian Revolution\nof Dignity are studied. Internet resources that cover news are analyzed.\nCurrent and most popular Internet resources are identified. The content of\nonline newspapers is analyzed and statistically processed. Internet content of\nnewspapers by the level of significance of data (very significant data,\nsignificant data and insignificant data) is classified. The algorithm of\ndetection of the media manipulations in the highlighting the course of the\nUkrainian revolutions based on historical, linguistic, and psychological\napproaches is designed. Methods of counteracting information attacks in online\nnewspapers are developed.\n", "date": "2024-07-09", "categories": []}
{"id": "2306.03009", "title": "Using Sequences of Life-events to Predict Human Lives", "abstract": "  Over the past decade, machine learning has revolutionized computers' ability\nto analyze text through flexible computational models. Due to their structural\nsimilarity to written language, transformer-based architectures have also shown\npromise as tools to make sense of a range of multi-variate sequences from\nprotein-structures, music, electronic health records to weather-forecasts. We\ncan also represent human lives in a way that shares this structural similarity\nto language. From one perspective, lives are simply sequences of events: People\nare born, visit the pediatrician, start school, move to a new location, get\nmarried, and so on. Here, we exploit this similarity to adapt innovations from\nnatural language processing to examine the evolution and predictability of\nhuman lives based on detailed event sequences. We do this by drawing on\narguably the most comprehensive registry data in existence, available for an\nentire nation of more than six million individuals across decades. Our data\ninclude information about life-events related to health, education, occupation,\nincome, address, and working hours, recorded with day-to-day resolution. We\ncreate embeddings of life-events in a single vector space showing that this\nembedding space is robust and highly structured. Our models allow us to predict\ndiverse outcomes ranging from early mortality to personality nuances,\noutperforming state-of-the-art models by a wide margin. Using methods for\ninterpreting deep learning models, we probe the algorithm to understand the\nfactors that enable our predictions. Our framework allows researchers to\nidentify new potential mechanisms that impact life outcomes and associated\npossibilities for personalized interventions.\n", "date": "2023-06-05", "categories": []}
{"id": "2407.15227", "title": "A Community-Centric Perspective for Characterizing and Detecting\n  Anti-Asian Violence-Provoking Speech", "abstract": "  Violence-provoking speech -- speech that implicitly or explicitly promotes\nviolence against the members of the targeted community, contributed to a\nmassive surge in anti-Asian crimes during the pandemic. While previous works\nhave characterized and built tools for detecting other forms of harmful speech,\nlike fear speech and hate speech, our work takes a community-centric approach\nto studying anti-Asian violence-provoking speech. Using data from ~420k Twitter\nposts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we\ndevelop a codebook to characterize anti-Asian violence-provoking speech and\ncollect a community-crowdsourced dataset to facilitate its large-scale\ndetection using state-of-the-art classifiers. We contrast the capabilities of\nnatural language processing classifiers, ranging from BERT-based to LLM-based\nclassifiers, in detecting violence-provoking speech with their capabilities to\ndetect anti-Asian hateful speech. In contrast to prior work that has\ndemonstrated the effectiveness of such classifiers in detecting hateful speech\n($F_1 = 0.89$), our work shows that accurate and reliable detection of\nviolence-provoking speech is a challenging task ($F_1 = 0.69$). We discuss the\nimplications of our findings, particularly the need for proactive interventions\nto support Asian communities during public health crises. The resources related\nto the study are available at\nhttps://claws-lab.github.io/violence-provoking-speech/.\n", "date": "2024-07-21", "categories": []}
{"id": "2407.15316", "title": "AI as a Tool for Fair Journalism: Case Studies from Malta", "abstract": "  In today`s media landscape, the role of Artificial Intelligence (AI) in\nshaping societal perspectives and journalistic integrity is becoming\nincreasingly apparent. This paper presents two case studies centred on Malta`s\nmedia market featuring technical novelty. Despite its relatively small scale,\nMalta offers invaluable insights applicable to both similar and broader media\ncontexts. These two projects focus on media monitoring and present tools\ndesigned to analyse potential biases in news articles and television news\nsegments. The first project uses Computer Vision and Natural Language\nProcessing techniques to analyse the coherence between images in news articles\nand their corresponding captions, headlines, and article bodies. The second\nproject employs computer vision techniques to track individuals` on-screen time\nor visual exposure in news videos, providing queryable data. These initiatives\naim to contribute to society by providing both journalists and the public with\nthe means to identify biases. Furthermore, we make these tools accessible to\njournalists to improve the trustworthiness of media outlets by offering robust\ntools for detecting and reducing bias.\n", "date": "2024-07-08", "categories": []}
{"id": "2407.14180", "title": "Automatic Classification of News Subjects in Broadcast News: Application\n  to a Gender Bias Representation Analysis", "abstract": "  This paper introduces a computational framework designed to delineate gender\ndistribution biases in topics covered by French TV and radio news. We\ntranscribe a dataset of 11.7k hours, broadcasted in 2023 on 21 French channels.\nA Large Language Model (LLM) is used in few-shot conversation mode to obtain a\ntopic classification on those transcriptions. Using the generated LLM\nannotations, we explore the finetuning of a specialized smaller classification\nmodel, to reduce the computational cost. To evaluate the performances of these\nmodels, we construct and annotate a dataset of 804 dialogues. This dataset is\nmade available free of charge for research purposes. We show that women are\nnotably underrepresented in subjects such as sports, politics and conflicts.\nConversely, on topics such as weather, commercials and health, women have more\nspeaking time than their overall average across all subjects. We also observe\nrepresentations differences between private and public service channels.\n", "date": "2024-07-19", "categories": []}
{"id": "2212.11672", "title": "Trustworthy Social Bias Measurement", "abstract": "  How do we design measures of social bias that we trust? While prior work has\nintroduced several measures, no measure has gained widespread trust: instead,\nmounting evidence argues we should distrust these measures. In this work, we\ndesign bias measures that warrant trust based on the cross-disciplinary theory\nof measurement modeling. To combat the frequently fuzzy treatment of social\nbias in NLP, we explicitly define social bias, grounded in principles drawn\nfrom social science research. We operationalize our definition by proposing a\ngeneral bias measurement framework DivDist, which we use to instantiate 5\nconcrete bias measures. To validate our measures, we propose a rigorous testing\nprotocol with 8 testing criteria (e.g. predictive validity: do measures predict\nbiases in US employment?). Through our testing, we demonstrate considerable\nevidence to trust our measures, showing they overcome conceptual, technical,\nand empirical deficiencies present in prior measures.\n", "date": "2022-12-20", "categories": []}
{"id": "2407.13071", "title": "Analysing the Public Discourse around OpenAI's Text-To-Video Model\n  'Sora' using Topic Modeling", "abstract": "  The recent introduction of OpenAI's text-to-video model Sora has sparked\nwidespread public discourse across online communities. This study aims to\nuncover the dominant themes and narratives surrounding Sora by conducting topic\nmodeling analysis on a corpus of 1,827 Reddit comments from five relevant\nsubreddits (r/OpenAI, r/technology, r/singularity, r/vfx, and r/ChatGPT). The\ncomments were collected over a two-month period following Sora's announcement\nin February 2024. After preprocessing the data, Latent Dirichlet Allocation\n(LDA) was employed to extract four key topics: 1) AI Impact and Trends in Sora\nDiscussions, 2) Public Opinion and Concerns about Sora, 3) Artistic Expression\nand Video Creation with Sora, and 4) Sora's Applications in Media and\nEntertainment. Visualizations including word clouds, bar charts, and t-SNE\nclustering provided insights into the importance of topic keywords and the\ndistribution of comments across topics. The results highlight prominent\nnarratives around Sora's potential impact on industries and employment, public\nsentiment and ethical concerns, creative applications, and use cases in the\nmedia and entertainment sectors. While limited to Reddit data within a specific\ntimeframe, this study offers a framework for understanding public perceptions\nof emerging generative AI technologies through online discourse analysis.\n", "date": "2024-05-30", "categories": []}
{"id": "2301.06178", "title": "Bike Frames: Understanding the Implicit Portrayal of Cyclists in the\n  News", "abstract": "  Increasing cycling for transportation or recreation can boost health and\nreduce the environmental impacts of vehicles. However, news agencies'\nideologies and reporting styles often influence public perception of cycling.\nFor example, if news agencies overly report cycling accidents, it may make\npeople perceive cyclists as \"dangerous,\" reducing the number of cyclists who\nopt to cycle. Additionally, a decline in cycling can result in less government\nfunding for safe infrastructure. In this paper, we develop a method for\ndetecting the perceived perception of cyclists within news headlines. We\nintroduce a new dataset called ``Bike Frames'' to accomplish this. The dataset\nconsists of 31,480 news headlines and 1,500 annotations. Our focus is on\nanalyzing 11,385 headlines from the United States. We also introduce the\nBikeFrame Chain-of-Code framework to predict cyclist perception, identify\naccident-related headlines, and determine fault. This framework uses pseudocode\nfor precise logic and integrates news agency bias analysis for improved\npredictions over traditional chain-of-thought reasoning in large language\nmodels. Our method substantially outperforms other methods, and most\nimportantly, we find that incorporating news bias information substantially\nimpacts performance, improving the average F1 from .739 to .815. Finally, we\nperform a comprehensive case study on US-based news headlines, finding\nreporting differences between news agencies and cycling-specific websites as\nwell as differences in reporting depending on the gender of cyclists. WARNING:\nThis paper contains descriptions of accidents and death.\n", "date": "2023-01-15", "categories": []}
{"id": "2407.11190", "title": "In Silico Sociology: Forecasting COVID-19 Polarization with Large\n  Language Models", "abstract": "  By training deep neural networks on massive archives of digitized text, large\nlanguage models (LLMs) learn the complex linguistic patterns that constitute\nhistoric and contemporary discourses. We argue that LLMs can serve as a\nvaluable tool for sociological inquiry by enabling accurate simulation of\nrespondents from specific social and cultural contexts. Applying LLMs in this\ncapacity, we reconstruct the public opinion landscape of 2019 to examine the\nextent to which the future polarization over COVID-19 was prefigured in\nexisting political discourse. Using an LLM trained on texts published through\n2019, we simulate the responses of American liberals and conservatives to a\nbattery of pandemic-related questions. We find that the simulated respondents\nreproduce observed partisan differences in COVID-19 attitudes in 84% of cases,\nsignificantly greater than chance. Prompting the simulated respondents to\njustify their responses, we find that much of the observed partisan gap\ncorresponds to differing appeals to freedom, safety, and institutional trust.\nOur findings suggest that the politicization of COVID-19 was largely consistent\nwith the prior ideological landscape, and this unprecedented event served to\nadvance history along its track rather than change the rails.\n", "date": "2024-05-23", "categories": []}
{"id": "2407.11409", "title": "Representation Bias in Political Sample Simulations with Large Language\n  Models", "abstract": "  This study seeks to identify and quantify biases in simulating political\nsamples with Large Language Models, specifically focusing on vote choice and\npublic opinion. Using the GPT-3.5-Turbo model, we leverage data from the\nAmerican National Election Studies, German Longitudinal Election Study, Zuobiao\nDataset, and China Family Panel Studies to simulate voting behaviors and public\nopinions. This methodology enables us to examine three types of representation\nbias: disparities based on the the country's language, demographic groups, and\npolitical regime types. The findings reveal that simulation performance is\ngenerally better for vote choice than for public opinions, more accurate in\nEnglish-speaking countries, more effective in bipartisan systems than in\nmulti-partisan systems, and stronger in democratic settings than in\nauthoritarian regimes. These results contribute to enhancing our understanding\nand developing strategies to mitigate biases in AI applications within the\nfield of computational social science.\n", "date": "2024-07-16", "categories": []}
{"id": "2407.10745", "title": "What distinguishes conspiracy from critical narratives? A computational\n  analysis of oppositional discourse", "abstract": "  The current prevalence of conspiracy theories on the internet is a\nsignificant issue, tackled by many computational approaches. However, these\napproaches fail to recognize the relevance of distinguishing between texts\nwhich contain a conspiracy theory and texts which are simply critical and\noppose mainstream narratives. Furthermore, little attention is usually paid to\nthe role of inter-group conflict in oppositional narratives. We contribute by\nproposing a novel topic-agnostic annotation scheme that differentiates between\nconspiracies and critical texts, and that defines span-level categories of\ninter-group conflict. We also contribute with the multilingual\nXAI-DisInfodemics corpus (English and Spanish), which contains a high-quality\nannotation of Telegram messages related to COVID-19 (5,000 messages per\nlanguage). We also demonstrate the feasibility of an NLP-based automatization\nby performing a range of experiments that yield strong baseline solutions.\nFinally, we perform an analysis which demonstrates that the promotion of\nintergroup conflict and the presence of violence and anger are key aspects to\ndistinguish between the two types of oppositional narratives, i.e., conspiracy\nvs. critical.\n", "date": "2024-07-15", "categories": []}
{"id": "2407.10829", "title": "BiasScanner: Automatic Detection and Classification of News Bias to\n  Strengthen Democracy", "abstract": "  The increasing consumption of news online in the 21st century coincided with\nincreased publication of disinformation, biased reporting, hate speech and\nother unwanted Web content. We describe BiasScanner, an application that aims\nto strengthen democracy by supporting news consumers with scrutinizing news\narticles they are reading online. BiasScanner contains a server-side\npre-trained large language model to identify biased sentences of news articles\nand a front-end Web browser plug-in. At the time of writing, BiasScanner can\nidentify and classify more than two dozen types of media bias at the sentence\nlevel, making it the most fine-grained model and only deployed application\n(automatic system in use) of its kind. It was implemented in a light-weight and\nprivacy-respecting manner, and in addition to highlighting likely biased\nsentence it also provides explanations for each classification decision as well\nas a summary analysis for each news article. While prior research has addressed\nnews bias detection, we are not aware of any work that resulted in a deployed\nbrowser plug-in (c.f. also biasscanner.org for a Web demo).\n", "date": "2024-07-15", "categories": []}
{"id": "2406.02798", "title": "Promotional Language and the Adoption of Innovative Ideas in Science", "abstract": "  How are the merits of innovative ideas communicated in science? Here we\nconduct semantic analyses of grant application success with a focus on\nscientific promotional language, which has been growing in frequency in many\ncontexts and purportedly may convey an innovative idea's originality and\nsignificance. Our analysis attempts to surmount limitations of prior studies by\nexamining the full text of tens of thousands of both funded and unfunded grants\nfrom three leading public and private funding agencies: the NIH, the NSF, and\nthe Novo Nordisk Foundation, one of the world's largest private science\nfoundations. We find a robust association between promotional language and the\nsupport and adoption of innovative ideas by funders and other scientists.\nFirst, the percentage of promotional language in a grant proposal is associated\nwith up to a doubling of the grant's probability of being funded. Second, a\ngrant's promotional language reflects its intrinsic level of innovativeness.\nThird, the percentage of promotional language predicts the expected citation\nand productivity impact of publications that are supported by funded grants.\nLastly, a computer-assisted experiment that manipulates the promotional\nlanguage in our data demonstrates how promotional language can communicate the\nmerit of ideas through cognitive activation. With the incidence of promotional\nlanguage in science steeply rising, and the pivotal role of grants in\nconverting promising and aspirational ideas into solutions, our analysis\nprovides empirical evidence that promotional language is associated with\neffectively communicating the merits of innovative scientific ideas.\n", "date": "2024-06-04", "categories": []}
{"id": "2407.09327", "title": "Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and\n  Propaganda", "abstract": "  The proliferation of bias and propaganda on social media is an increasingly\nsignificant concern, leading to the development of techniques for automatic\ndetection. This article presents a multilingual corpus of 12, 000 Facebook\nposts fully annotated for bias and propaganda. The corpus was created as part\nof the FigNews 2024 Shared Task on News Media Narratives for framing the\nIsraeli War on Gaza. It covers various events during the War from October 7,\n2023 to January 31, 2024. The corpus comprises 12, 000 posts in five languages\n(Arabic, Hebrew, English, French, and Hindi), with 2, 400 posts for each\nlanguage. The annotation process involved 10 graduate students specializing in\nLaw. The Inter-Annotator Agreement (IAA) was used to evaluate the annotations\nof the corpus, with an average IAA of 80.8% for bias and 70.15% for propaganda\nannotations. Our team was ranked among the bestperforming teams in both Bias\nand Propaganda subtasks. The corpus is open-source and available at\nhttps://sina.birzeit.edu/fada\n", "date": "2024-07-12", "categories": []}
{"id": "2210.05401", "title": "MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation\n  Detection", "abstract": "  The rapid dissemination of misinformation through online social networks\nposes a pressing issue with harmful consequences jeopardizing human health,\npublic safety, democracy, and the economy; therefore, urgent action is required\nto address this problem. In this study, we construct a new human-annotated\ndataset, called MiDe22, having 5,284 English and 5,064 Turkish tweets with\ntheir misinformation labels for several recent events between 2020 and 2022,\nincluding the Russia-Ukraine war, COVID-19 pandemic, and Refugees. The dataset\nincludes user engagements with the tweets in terms of likes, replies, retweets,\nand quotes. We also provide a detailed data analysis with descriptive\nstatistics and the experimental results of a benchmark evaluation for\nmisinformation detection.\n", "date": "2022-10-11", "categories": []}
{"id": "2407.06233", "title": "AI and Social Theory", "abstract": "  In this paper, we sketch a programme for AI driven social theory. We begin by\ndefining what we mean by artificial intelligence (AI) in this context. We then\nlay out our model for how AI based models can draw on the growing availability\nof digital data to help test the validity of different social theories based on\ntheir predictive power. In doing so, we use the work of Randall Collins and his\nstate breakdown model to exemplify that, already today, AI based models can\nhelp synthesize knowledge from a variety of sources, reason about the world,\nand apply what is known across a wide range of problems in a systematic way.\nHowever, we also find that AI driven social theory remains subject to a range\nof practical, technical, and epistemological limitations. Most critically,\nexisting AI systems lack three essential capabilities needed to advance social\ntheory in ways that are cumulative, holistic, open-ended, and purposeful. These\nare (1) semanticization, i.e., the ability to develop and operationalize verbal\nconcepts to represent machine-manipulable knowledge, (2) transferability, i.e.,\nthe ability to transfer what has been learned in one context to another, and\n(3) generativity, i.e., the ability to independently create and improve on\nconcepts and models. We argue that if the gaps identified here are addressed by\nfurther research, there is no reason why, in the future, the most advanced\nprogramme in social theory should not be led by AI-driven cumulative advances.\n", "date": "2024-07-07", "categories": []}
{"id": "2407.03255", "title": "How Similar Are Elected Politicians and Their Constituents? Quantitative\n  Evidence From Online Social Networks", "abstract": "  How similar are politicians to those who vote for them? This is a critical\nquestion at the heart of democratic representation and particularly relevant at\ntimes when political dissatisfaction and populism are on the rise. To answer\nthis question we compare the online discourse of elected politicians and their\nconstituents. We collect a two and a half years (September 2020 - February\n2023) constituency-level dataset for USA and UK that includes: (i) the Twitter\ntimelines (5.6 Million tweets) of elected political representatives (595 UK\nMembers of Parliament and 433 USA Representatives), (ii) the Nextdoor posts\n(21.8 Million posts) of the constituency (98.4% USA and 91.5% UK\nconstituencies). We find that elected politicians tend to be equally similar to\ntheir constituents in terms of content and style regardless of whether a\nconstituency elects a right or left-wing politician. The size of the electoral\nvictory and the level of income of a constituency shows a nuanced picture. The\nnarrower the electoral victory, the more similar the style and the more\ndissimilar the content is. The lower the income of a constituency, the more\nsimilar the content is. In terms of style, poorer constituencies tend to have a\nmore similar sentiment and more dissimilar psychological text traits (i.e.\nmeasured with LIWC categories).\n", "date": "2024-07-03", "categories": []}
{"id": "2407.03446", "title": "Towards Asimov's Psychohistory: Harnessing Topological Data Analysis,\n  Artificial Intelligence and Social Media data to Forecast Societal Trends", "abstract": "  In the age of big data and advanced computational methods, the prediction of\nlarge-scale social behaviors, reminiscent of Isaac Asimov's fictional science\nof Psychohistory, is becoming increasingly feasible. This paper consists of a\ntheoretical exploration of the integration of computational power and\nmathematical frameworks, particularly through Topological Data Analysis (TDA)\n(Carlsson, Vejdemo-Johansson, 2022) and Artificial Intelligence (AI), to\nforecast societal trends through social media data analysis. By examining\nsocial media as a reflective surface of collective human behavior through the\nsystematic behaviorist approach (Glenn, et al., 2016), I argue that these tools\nprovide unprecedented clarity into the dynamics of large communities. This\nstudy dialogues with Asimov's work, drawing parallels between his visionary\nconcepts and contemporary methodologies, illustrating how modern computational\ntechniques can uncover patterns and predict shifts in social behavior,\ncontributing to the emerging field of digital sociology -- or even,\nPsychohistory itself.\n", "date": "2024-07-03", "categories": []}
{"id": "2407.02662", "title": "Supporters and Skeptics: LLM-based Analysis of Engagement with Mental\n  Health (Mis)Information Content on Video-sharing Platforms", "abstract": "  Over one in five adults in the US lives with a mental illness. In the face of\na shortage of mental health professionals and offline resources, online\nshort-form video content has grown to serve as a crucial conduit for\ndisseminating mental health help and resources. However, the ease of content\ncreation and access also contributes to the spread of misinformation, posing\nrisks to accurate diagnosis and treatment. Detecting and understanding\nengagement with such content is crucial to mitigating their harmful effects on\npublic health. We perform the first quantitative study of the phenomenon using\nYouTube Shorts and Bitchute as the sites of study. We contribute MentalMisinfo,\na novel labeled mental health misinformation (MHMisinfo) dataset of 739 videos\n(639 from Youtube and 100 from Bitchute) and 135372 comments in total, using an\nexpert-driven annotation schema. We first found that few-shot in-context\nlearning with large language models (LLMs) are effective in detecting MHMisinfo\nvideos. Next, we discover distinct and potentially alarming linguistic patterns\nin how audiences engage with MHMisinfo videos through commentary on both\nvideo-sharing platforms. Across the two platforms, comments could exacerbate\nprevailing stigma with some groups showing heightened susceptibility to and\nalignment with MHMisinfo. We discuss technical and public health-driven\nadaptive solutions to tackling the \"epidemic\" of mental health misinformation\nonline.\n", "date": "2024-07-02", "categories": []}
{"id": "2309.15176", "title": "Robust Stance Detection: Understanding Public Perceptions in Social\n  Media", "abstract": "  The abundance of social media data has presented opportunities for accurately\ndetermining public and group-specific stances around policy proposals or\ncontroversial topics. In contrast with sentiment analysis which focuses on\nidentifying prevailing emotions, stance detection identifies precise positions\n(i.e., supportive, opposing, neutral) relative to a well-defined topic, such as\nperceptions toward specific global health interventions during the COVID-19\npandemic. Traditional stance detection models, while effective within their\nspecific domain (e.g., attitudes towards masking protocols during COVID-19),\noften lag in performance when applied to new domains and topics due to changes\nin data distribution. This limitation is compounded by the scarcity of\ndomain-specific, labeled datasets, which are expensive and labor-intensive to\ncreate. A solution we present in this paper combines counterfactual data\naugmentation with contrastive learning to enhance the robustness of stance\ndetection across domains and topics of interest. We evaluate the performance of\ncurrent state-of-the-art stance detection models, including a prompt-optimized\nlarge language model, relative to our proposed framework succinctly called\nSTANCE-C3 (domain-adaptive Cross-target STANCE detection via Contrastive\nlearning and Counterfactual generation). Empirical evaluations demonstrate\nSTANCE-C3's consistent improvements over the baseline models with respect to\naccuracy across domains and varying focal topics. Despite the increasing\nprevalence of general-purpose models such as generative AI, specialized models\nsuch as STANCE-C3 provide utility in safety-critical domains wherein precision\nis highly valued, especially when a nuanced understanding of the concerns of\ndifferent population segments could result in crafting more impactful public\npolicies.\n", "date": "2023-09-26", "categories": []}
{"id": "2312.15099", "title": "Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in\n  Large Language Models", "abstract": "  Online hate is an escalating problem that negatively impacts the lives of\nInternet users, and is also subject to rapid changes due to evolving events,\nresulting in new waves of online hate that pose a critical threat. Detecting\nand mitigating these new waves present two key challenges: it demands\nreasoning-based complex decision-making to determine the presence of hateful\ncontent, and the limited availability of training samples hinders updating the\ndetection model. To address this critical issue, we present a novel framework\ncalled HATEGUARD for effectively moderating new waves of online hate. HATEGUARD\nemploys a reasoning-based approach that leverages the recently introduced\nchain-of-thought (CoT) prompting technique, harnessing the capabilities of\nlarge language models (LLMs). HATEGUARD further achieves prompt-based zero-shot\ndetection by automatically generating and updating detection prompts with new\nderogatory terms and targets in new wave samples to effectively address new\nwaves of online hate. To demonstrate the effectiveness of our approach, we\ncompile a new dataset consisting of tweets related to three recently witnessed\nnew waves: the 2022 Russian invasion of Ukraine, the 2021 insurrection of the\nUS Capitol, and the COVID-19 pandemic. Our studies reveal crucial longitudinal\npatterns in these new waves concerning the evolution of events and the pressing\nneed for techniques to rapidly update existing moderation tools to counteract\nthem. Comparative evaluations against state-of-the-art tools illustrate the\nsuperiority of our framework, showcasing a substantial 22.22% to 83.33%\nimprovement in detecting the three new waves of online hate. Our work\nhighlights the severe threat posed by the emergence of new waves of online hate\nand represents a paradigm shift in addressing this threat practically.\n", "date": "2023-12-22", "categories": []}
{"id": "2406.14373", "title": "Artificial Leviathan: Exploring Social Evolution of LLM Agents Through\n  the Lens of Hobbesian Social Contract Theory", "abstract": "  The emergence of Large Language Models (LLMs) and advancements in Artificial\nIntelligence (AI) offer an opportunity for computational social science\nresearch at scale. Building upon prior explorations of LLM agent design, our\nwork introduces a simulated agent society where complex social relationships\ndynamically form and evolve over time. Agents are imbued with psychological\ndrives and placed in a sandbox survival environment. We conduct an evaluation\nof the agent society through the lens of Thomas Hobbes's seminal Social\nContract Theory (SCT). We analyze whether, as the theory postulates, agents\nseek to escape a brutish \"state of nature\" by surrendering rights to an\nabsolute sovereign in exchange for order and security. Our experiments unveil\nan alignment: Initially, agents engage in unrestrained conflict, mirroring\nHobbes's depiction of the state of nature. However, as the simulation\nprogresses, social contracts emerge, leading to the authorization of an\nabsolute sovereign and the establishment of a peaceful commonwealth founded on\nmutual cooperation. This congruence between our LLM agent society's\nevolutionary trajectory and Hobbes's theoretical account indicates LLMs'\ncapability to model intricate social dynamics and potentially replicate forces\nthat shape human societies. By enabling such insights into group behavior and\nemergent societal phenomena, LLM-driven multi-agent simulations, while unable\nto simulate all the nuances of human behavior, may hold potential for advancing\nour understanding of social structures, group dynamics, and complex human\nsystems.\n", "date": "2024-06-20", "categories": []}
{"id": "2407.01817", "title": "Race and Privacy in Broadcast Police Communications", "abstract": "  Radios are essential for the operations of modern police departments, and\nthey function as both a collaborative communication technology and a\nsociotechnical system. However, little prior research has examined their usage\nor their connections to individual privacy and the role of race in policing,\ntwo growing topics of concern in the US. As a case study, we examine the\nChicago Police Department's (CPD's) use of broadcast police communications\n(BPC) to coordinate the activity of law enforcement officers (LEOs) in the\ncity. From a recently assembled archive of 80,775 hours of BPC associated with\nCPD operations, we analyze text transcripts of radio transmissions broadcast\n9:00 AM to 5:00 PM on August 10th, 2018 in one majority Black, one majority\nwhite, and one majority Hispanic area of the city (24 hours of audio) to\nexplore three research questions: (1) Do BPC reflect reported racial\ndisparities in policing? (2) How and when is gender, race/ethnicity, and age\nmentioned in BPC? (3) To what extent do BPC include sensitive information, and\nwho is put at most risk by this practice? (4) To what extent can large language\nmodels (LLMs) heighten this risk? We explore the vocabulary and speech acts\nused by police in BPC, comparing mentions of personal characteristics to local\ndemographics, the personal information shared over BPC, and the privacy\nconcerns that it poses. Analysis indicates (a) policing professionals in the\ncity of Chicago exhibit disproportionate attention to Black members of the\npublic regardless of context, (b) sociodemographic characteristics like gender,\nrace/ethnicity, and age are primarily mentioned in BPC about event information,\nand (c) disproportionate attention introduces disproportionate privacy risks\nfor Black members of the public.\n", "date": "2024-07-01", "categories": []}
{"id": "2407.02284", "title": "Renard: A Modular Pipeline for Extracting Character Networks from\n  Narrative Texts", "abstract": "  Renard (Relationships Extraction from NARrative Documents) is a Python\nlibrary that allows users to define custom natural language processing (NLP)\npipelines to extract character networks from narrative texts. Contrary to the\nfew existing tools, Renard can extract dynamic networks, as well as the more\ncommon static networks. Renard pipelines are modular: users can choose the\nimplementation of each NLP subtask needed to extract a character network. This\nallows users to specialize pipelines to particular types of texts and to study\nthe impact of each subtask on the extracted network.\n", "date": "2024-07-02", "categories": []}
{"id": "2304.13568", "title": "Toxic comments reduce the activity of volunteer editors on Wikipedia", "abstract": "  Wikipedia is one of the most successful collaborative projects in history. It\nis the largest encyclopedia ever created, with millions of users worldwide\nrelying on it as the first source of information as well as for fact-checking\nand in-depth research. As Wikipedia relies solely on the efforts of its\nvolunteer-editors, its success might be particularly affected by toxic speech.\nIn this paper, we analyze all 57 million comments made on user talk pages of\n8.5 million editors across the six most active language editions of Wikipedia\nto study the potential impact of toxicity on editors' behaviour. We find that\ntoxic comments consistently reduce the activity of editors, leading to an\nestimated loss of 0.5-2 active days per user in the short term. This amounts to\nmultiple human-years of lost productivity when considering the number of active\ncontributors to Wikipedia. The effects of toxic comments are even greater in\nthe long term, as they significantly increase the risk of editors leaving the\nproject altogether. Using an agent-based model, we demonstrate that toxicity\nattacks on Wikipedia have the potential to impede the progress of the entire\nproject. Our results underscore the importance of mitigating toxic speech on\ncollaborative platforms such as Wikipedia to ensure their continued success.\n", "date": "2023-04-26", "categories": []}
{"id": "2404.02205", "title": "A Holistic Indicator of Polarization to Measure Online Sexism", "abstract": "  The online trend of the manosphere and feminist discourse on social networks\nrequires a holistic measure of the level of sexism in an online community. This\nindicator is important for policymakers and moderators of online communities\n(e.g., subreddits) and computational social scientists, either to revise\nmoderation strategies based on the degree of sexism or to match and compare the\ntemporal sexism across different platforms and communities with real-time\nevents and infer social scientific insights.\n  In this paper, we build a model that can provide a comparable holistic\nindicator of toxicity targeted toward male and female identity and male and\nfemale individuals. Despite previous supervised NLP methods that require\nannotation of toxic comments at the target level (e.g. annotating comments that\nare specifically toxic toward women) to detect targeted toxic comments, our\nindicator uses supervised NLP to detect the presence of toxicity and\nunsupervised word embedding association test to detect the target\nautomatically.\n  We apply our model to gender discourse communities (e.g., r/TheRedPill,\nr/MGTOW, r/FemaleDatingStrategy) to detect the level of toxicity toward genders\n(i.e., sexism). Our results show that our framework accurately and consistently\n(93% correlation) measures the level of sexism in a community. We finally\ndiscuss how our framework can be generalized in the future to measure qualities\nother than toxicity (e.g. sentiment, humor) toward general-purpose targets and\nturn into an indicator of different sorts of polarizations.\n", "date": "2024-04-02", "categories": []}
{"id": "2407.00455", "title": "Polarization and Morality: Lexical Analysis of Abortion Discourse on\n  Reddit", "abstract": "  This study investigates whether division on political topics is mapped with\nthe distinctive patterns of language use. We collect a total 145,832 Reddit\ncomments on the abortion debate and explore the languages of subreddit\ncommunities r/prolife and r/prochoice. With consideration of the Moral\nFoundations Theory, we examine lexical patterns in three ways. First, we\ncompute proportional frequencies of lexical items from the Moral Foundations\nDictionary in order to make inferences about each group's moral considerations\nwhen forming arguments for and against abortion. We then create n-gram models\nto reveal frequent collocations from each stance group and better understand\nhow commonly used words are patterned in their linguistic context and in\nrelation to morality values. Finally, we use Latent Dirichlet Allocation to\nidentify underlying topical structures in the corpus data. Results show that\nthe use of morality words is mapped with the stances on abortion.\n", "date": "2024-06-29", "categories": []}
{"id": "2407.01393", "title": "POLygraph: Polish Fake News Dataset", "abstract": "  This paper presents the POLygraph dataset, a unique resource for fake news\ndetection in Polish. The dataset, created by an interdisciplinary team, is\ncomposed of two parts: the \"fake-or-not\" dataset with 11,360 pairs of news\narticles (identified by their URLs) and corresponding labels, and the\n\"fake-they-say\" dataset with 5,082 news articles (identified by their URLs) and\ntweets commenting on them. Unlike existing datasets, POLygraph encompasses a\nvariety of approaches from source literature, providing a comprehensive\nresource for fake news detection. The data was collected through manual\nannotation by expert and non-expert annotators. The project also developed a\nsoftware tool that uses advanced machine learning techniques to analyze the\ndata and determine content authenticity. The tool and dataset are expected to\nbenefit various entities, from public sector institutions to publishers and\nfact-checking organizations. Further dataset exploration will foster fake news\ndetection and potentially stimulate the implementation of similar models in\nother languages. The paper focuses on the creation and composition of the\ndataset, so it does not include a detailed evaluation of the software tool for\ncontent authenticity analysis, which is planned at a later stage of the\nproject.\n", "date": "2024-07-01", "categories": []}
{"id": "2311.06537", "title": "Is Machine Learning Unsafe and Irresponsible in Social Sciences?\n  Paradoxes and Reconsidering from Recidivism Prediction Tasks", "abstract": "  The paper addresses some fundamental and hotly debated issues for high-stakes\nevent predictions underpinning the computational approach to social sciences.\nWe question several prevalent views against machine learning and outline a new\nparadigm that highlights the promises and promotes the infusion of\ncomputational methods and conventional social science approaches.\n", "date": "2023-11-11", "categories": []}
{"id": "2406.19731", "title": "Message du troisi{\\`e}me type : irruption d'un tiers dans un dialogue en\n  ligne", "abstract": "  Our study focuses on Wikipedia talk pages, from a global perspective\nanalyzing contributors' behaviors in online interactions. Using a corpus\ncomprising all Wikipedia talk pages in French, totaling more than 300,000\ndiscussion threads, we examine how discussions with more than two participants\n(multiparty conversation) unfold and we specifically investigate the role of a\nthird participant's intervention when two Wikipedians have already initiated an\nexchange. In this regard, we concentrate on the sequential structure of these\ninteractions in terms of articulation among different participants and aim to\nspecify this third message by exploring its lexical particularities, while also\nproposing an initial typology of the third participant's message role and how\nit aligns with preceding messages.\n", "date": "2024-06-28", "categories": []}
{"id": "2406.18702", "title": "Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling\n  Legislative Behavior and Bipartisanship", "abstract": "  This study introduces a novel approach to simulating legislative processes\nusing LLM-driven virtual agents, focusing on the U.S. Senate Intelligence\nCommittee. We developed agents representing individual senators and placed them\nin simulated committee discussions. The agents demonstrated the ability to\nengage in realistic debate, provide thoughtful reflections, and find bipartisan\nsolutions under certain conditions. Notably, the simulation also showed promise\nin modeling shifts towards bipartisanship in response to external\nperturbations. Our results indicate that this LLM-driven approach could become\na valuable tool for understanding and potentially improving legislative\nprocesses, supporting a broader pattern of findings highlighting how LLM-based\nagents can usefully model real-world phenomena. Future works will focus on\nenhancing agent complexity, expanding the simulation scope, and exploring\napplications in policy testing and negotiation.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.10289", "title": "VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable\n  Reasoning", "abstract": "  The proliferation of fake news poses a significant threat not only by\ndisseminating misleading information but also by undermining the very\nfoundations of democracy. The recent advance of generative artificial\nintelligence has further exacerbated the challenge of distinguishing genuine\nnews from fabricated stories. In response to this challenge, we introduce\nVeraCT Scan, a novel retrieval-augmented system for fake news detection. This\nsystem operates by extracting the core facts from a given piece of news and\nsubsequently conducting an internet-wide search to identify corroborating or\nconflicting reports. Then sources' credibility is leveraged for information\nverification. Besides determining the veracity of news, we also provide\ntransparent evidence and reasoning to support its conclusions, resulting in the\ninterpretability and trust in the results. In addition to GPT-4 Turbo, Llama-2\n13B is also fine-tuned for news content understanding, information\nverification, and reasoning. Both implementations have demonstrated\nstate-of-the-art accuracy in the realm of fake news detection.\n", "date": "2024-06-12", "categories": []}
{"id": "2406.17213", "title": "Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence\n  Coverage", "abstract": "  News media structure their reporting of events or issues using certain\nperspectives.\n  When describing an incident involving gun violence, for example, some\njournalists may focus on mental health or gun regulation, while others may\nemphasize the discussion of gun rights. Such perspectives are called\n\\say{frames} in communication research. We study, for the first time, the value\nof combining lead images and their contextual information with text to identify\nthe frame of a given news article. We observe that using multiple modes of\ninformation(article- and image-derived features) improves prediction of news\nframes over any single mode of information when the images are relevant to the\nframes of the headlines. We also observe that frame image relevance is related\nto the ease of conveying frames via images, which we call frame concreteness.\nAdditionally, we release the first multimodal news framing dataset related to\ngun violence in the U.S., curated and annotated by communication researchers.\nThe dataset will allow researchers to further examine the use of multiple\ninformation modalities for studying media framing.\n", "date": "2024-06-25", "categories": []}
{"id": "2406.15443", "title": "ExU: AI Models for Examining Multilingual Disinformation Narratives and\n  Understanding their Spread", "abstract": "  Addressing online disinformation requires analysing narratives across\nlanguages to help fact-checkers and journalists sift through large amounts of\ndata. The ExU project focuses on developing AI-based models for multilingual\ndisinformation analysis, addressing the tasks of rumour stance classification\nand claim retrieval. We describe the ExU project proposal and summarise the\nresults of a user requirements survey regarding the design of tools to support\nfact-checking.\n", "date": "2024-05-30", "categories": []}
{"id": "2406.16079", "title": "EERPD: Leveraging Emotion and Emotion Regulation for Improving\n  Personality Detection", "abstract": "  Personality is a fundamental construct in psychology, reflecting an\nindividual's behavior, thinking, and emotional patterns. Previous researches\nhave made some progress in personality detection, primarily by utilizing the\nwhole text to predict personality. However, these studies generally tend to\noverlook psychological knowledge: they rarely apply the well-established\ncorrelations between emotion regulation and personality. Based on this, we\npropose a new personality detection method called EERPD. This method introduces\nthe use of emotion regulation, a psychological concept highly correlated with\npersonality, for personality prediction. By combining this feature with emotion\nfeatures, it retrieves few-shot examples and provides process CoTs for\ninferring labels from text. This approach enhances the understanding of LLM for\npersonality within text and improves the performance in personality detection.\nExperimental results demonstrate that EERPD significantly enhances the accuracy\nand robustness of personality detection, outperforming previous SOTA by\n15.05/4.29 in average F1 on the two benchmark datasets.\n", "date": "2024-06-23", "categories": []}
{"id": "2309.08069", "title": "Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary\n  Disparities in Media Bias and Framing", "abstract": "  The manifestation and effect of bias in news reporting have been central\ntopics in the social sciences for decades, and have received increasing\nattention in the NLP community recently. While NLP can help to scale up\nanalyses or contribute automatic procedures to investigate the impact of biased\nnews in society, we argue that methodologies that are currently dominant fall\nshort of addressing the complex questions and effects addressed in theoretical\nmedia studies. In this survey paper, we review social science approaches and\ndraw a comparison with typical task formulations, methods, and evaluation\nmetrics used in the analysis of media bias in NLP. We discuss open questions\nand suggest possible directions to close identified gaps between theory and\npredictive models, and their evaluation. These include model transparency,\nconsidering document-external information, and cross-document reasoning rather\nthan single-label assignment.\n", "date": "2023-09-14", "categories": []}
{"id": "2406.14012", "title": "Seeing Through AI's Lens: Enhancing Human Skepticism Towards\n  LLM-Generated Fake News", "abstract": "  LLMs offer valuable capabilities, yet they can be utilized by malicious users\nto disseminate deceptive information and generate fake news. The growing\nprevalence of LLMs poses difficulties in crafting detection approaches that\nremain effective across various text domains. Additionally, the absence of\nprecautionary measures for AI-generated news on online social platforms is\nconcerning. Therefore, there is an urgent need to improve people's ability to\ndifferentiate between news articles written by humans and those produced by\nLLMs. By providing cues in human-written and LLM-generated news, we can help\nindividuals increase their skepticism towards fake LLM-generated news. This\npaper aims to elucidate simple markers that help individuals distinguish\nbetween articles penned by humans and those created by LLMs. To achieve this,\nwe initially collected a dataset comprising 39k news articles authored by\nhumans or generated by four distinct LLMs with varying degrees of fake. We then\ndevise a metric named Entropy-Shift Authorship Signature (ESAS) based on the\ninformation theory and entropy principles. The proposed ESAS ranks terms or\nentities, like POS tagging, within news articles based on their relevance in\ndiscerning article authorship. We demonstrate the effectiveness of our metric\nby showing the high accuracy attained by a basic method, i.e., TF-IDF combined\nwith logistic regression classifier, using a small set of terms with the\nhighest ESAS score. Consequently, we introduce and scrutinize these top\nESAS-ranked terms to aid individuals in strengthening their skepticism towards\nLLM-generated fake news.\n", "date": "2024-06-20", "categories": []}
{"id": "2402.16333", "title": "Unveiling the Truth and Facilitating Change: Towards Agent-based\n  Large-scale Social Movement Simulation", "abstract": "  Social media has emerged as a cornerstone of social movements, wielding\nsignificant influence in driving societal change. Simulating the response of\nthe public and forecasting the potential impact has become increasingly\nimportant. However, existing methods for simulating such phenomena encounter\nchallenges concerning their efficacy and efficiency in capturing the behaviors\nof social movement participants. In this paper, we introduce a hybrid framework\nHiSim for social media user simulation, wherein users are categorized into two\ntypes. Core users are driven by Large Language Models, while numerous ordinary\nusers are modeled by deductive agent-based models. We further construct a\nTwitter-like environment to replicate their response dynamics following trigger\nevents. Subsequently, we develop a multi-faceted benchmark SoMoSiMu-Bench for\nevaluation and conduct comprehensive experiments across real-world datasets.\nExperimental results demonstrate the effectiveness and flexibility of our\nmethod.\n", "date": "2024-02-26", "categories": []}
{"id": "2404.00977", "title": "Nonlinear dynamical social and political prediction algorithm for city\n  planning and public participation using the Impulse Pattern Formulation", "abstract": "  A nonlinear-dynamical algorithm for city planning is proposed as an Impulse\nPattern Formulation (IPF) for predicting relevant parameters like health,\nartistic freedom, or financial developments of different social or political\nstakeholders over the cause of a planning process. The IPF has already shown\nhigh predictive precision at low computational cost in musical instrument\nsimulations, brain dynamics, and human-human interactions. The social and\npolitical IPF consists of three basic equations of system state developments,\nself-adaptation of stakeholders, two adaptive interactions, and external impact\nterms suitable for respective planning situations. Typical scenarios of\nstakeholder interactions and developments are modeled by adjusting a set of\nsystem parameters. These include stakeholder reaction to external input,\nenhanced system stability through self-adaptation, stakeholder convergence due\nto adaptive interaction, as well as complex dynamics in terms of fixed\nstakeholder impacts. A workflow for implementing the algorithm in real city\nplanning scenarios is outlined. This workflow includes machine learning of a\nsuitable set of parameters suggesting best-practice planning to aim at the\ndesired development of the planning process and its output.\n", "date": "2024-04-01", "categories": []}
{"id": "2406.10773", "title": "Quantifying Generative Media Bias with a Corpus of Real-world and\n  Generated News Articles", "abstract": "  Large language models (LLMs) are increasingly being utilised across a range\nof tasks and domains, with a burgeoning interest in their application within\nthe field of journalism. This trend raises concerns due to our limited\nunderstanding of LLM behaviour in this domain, especially with respect to\npolitical bias. Existing studies predominantly focus on LLMs undertaking\npolitical questionnaires, which offers only limited insights into their biases\nand operational nuances. To address this gap, our study establishes a new\ncurated dataset that contains 2,100 human-written articles and utilises their\ndescriptions to generate 56,700 synthetic articles using nine LLMs. This\nenables us to analyse shifts in properties between human-authored and\nmachine-generated articles, with this study focusing on political bias,\ndetecting it using both supervised models and LLMs. Our findings reveal\nsignificant disparities between base and instruction-tuned LLMs, with\ninstruction-tuned models exhibiting consistent political bias. Furthermore, we\nare able to study how LLMs behave as classifiers, observing their display of\npolitical bias even in this role. Overall, for the first time within the\njournalistic domain, this study outlines a framework and provides a structured\ndataset for quantifiable experiments, serving as a foundation for further\nresearch into LLM political bias and its implications.\n", "date": "2024-06-16", "categories": []}
{"id": "2401.09851", "title": "Next-Generation Simulation Illuminates Scientific Problems of Organised\n  Complexity", "abstract": "  As artificial intelligence becomes increasingly prevalent in scientific\nresearch, data-driven methodologies appear to overshadow traditional approaches\nin resolving scientific problems. In this Perspective, we revisit a classic\nclassification of scientific problems and acknowledge that a series of\nunresolved problems remain. Throughout the history of researching scientific\nproblems, scientists have continuously formed new paradigms facilitated by\nadvances in data, algorithms, and computational power. To better tackle\nunresolved problems, especially those of organised complexity, a novel paradigm\nis necessitated. While recognising that the strengths of new paradigms have\nexpanded the scope of resolvable scientific problems, we aware that the\ncontinued advancement of data, algorithms, and computational power alone is\nhardly to bring a new paradigm. We posit that the integration of paradigms,\nwhich capitalises on the strengths of each, represents a promising approach.\nSpecifically, we focus on next-generation simulation (NGS), which can serve as\na platform to integrate methods from different paradigms. We propose a\nmethodology, sophisticated behavioural simulation (SBS), to realise it. SBS\nrepresents a higher level of paradigms integration based on foundational models\nto simulate complex systems, such as social systems involving sophisticated\nhuman strategies and behaviours. NGS extends beyond the capabilities of\ntraditional mathematical modelling simulations and agent-based modelling\nsimulations, and therefore, positions itself as a potential solution to\nproblems of organised complexity in complex systems.\n", "date": "2024-01-18", "categories": []}
{"id": "2405.00821", "title": "Uncovering Agendas: A Novel French & English Dataset for Agenda\n  Detection on Social Media", "abstract": "  The behavior and decision making of groups or communities can be dramatically\ninfluenced by individuals pushing particular agendas, e.g., to promote or\ndisparage a person or an activity, to call for action, etc.. In the examination\nof online influence campaigns, particularly those related to important\npolitical and social events, scholars often concentrate on identifying the\nsources responsible for setting and controlling the agenda (e.g., public\nmedia). In this article we present a methodology for detecting specific\ninstances of agenda control through social media where annotated data is\nlimited or non-existent. By using a modest corpus of Twitter messages centered\non the 2022 French Presidential Elections, we carry out a comprehensive\nevaluation of various approaches and techniques that can be applied to this\nproblem. Our findings demonstrate that by treating the task as a textual\nentailment problem, it is possible to overcome the requirement for a large\nannotated training dataset.\n", "date": "2024-05-01", "categories": []}
{"id": "2406.07593", "title": "Modeling Sustainable Resource Management using Active Inference", "abstract": "  Active inference helps us simulate adaptive behavior and decision-making in\nbiological and artificial agents. Building on our previous work exploring the\nrelationship between active inference, well-being, resilience, and\nsustainability, we present a computational model of an agent learning\nsustainable resource management strategies in both static and dynamic\nenvironments. The agent's behavior emerges from optimizing its own well-being,\nrepresented by prior preferences, subject to beliefs about environmental\ndynamics. In a static environment, the agent learns to consistently consume\nresources to satisfy its needs. In a dynamic environment where resources\ndeplete and replenish based on the agent's actions, the agent adapts its\nbehavior to balance immediate needs with long-term resource availability. This\ndemonstrates how active inference can give rise to sustainable and resilient\nbehaviors in the face of changing environmental conditions. We discuss the\nimplications of our model, its limitations, and suggest future directions for\nintegrating more complex agent-environment interactions. Our work highlights\nactive inference's potential for understanding and shaping sustainable\nbehaviors.\n", "date": "2024-06-11", "categories": []}
{"id": "2406.01866", "title": "#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic", "abstract": "  Effective communication during health crises is critical, with social media\nserving as a key platform for public health experts (PHEs) to engage with the\npublic. However, it also amplifies pseudo-experts promoting contrarian views.\nDespite its importance, the role of emotional and moral language in PHEs'\ncommunication during COVID-19 remains under explored. This study examines how\nPHEs and pseudo-experts communicated on Twitter during the pandemic, focusing\non emotional and moral language and their engagement with political elites.\nAnalyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to\nJanuary 2021, alongside public responses, we identified key priorities and\ndifferences in messaging strategy. PHEs prioritize masking, healthcare,\neducation, and vaccines, using positive emotional language like optimism. In\ncontrast, pseudo-experts discuss therapeutics and lockdowns more frequently,\nemploying negative emotions like pessimism and disgust. Negative emotional and\nmoral language tends to drive engagement, but positive language from PHEs\nfosters positivity in public responses. PHEs exhibit liberal partisanship,\nexpressing more positivity towards liberals and negativity towards conservative\nelites, while pseudo-experts show conservative partisanship. These findings\nshed light on the polarization of COVID-19 discourse and underscore the\nimportance of strategic use of emotional and moral language by experts to\nmitigate polarization and enhance public trust.\n", "date": "2024-06-04", "categories": []}
{"id": "2406.02090", "title": "WEIRD ICWSM: How Western, Educated, Industrialized, Rich, and Democratic\n  is Social Computing Research?", "abstract": "  Much of the research in social computing analyzes data from social media\nplatforms, which may inherently carry biases. An overlooked source of such bias\nis the over-representation of WEIRD (Western, Educated, Industrialized, Rich,\nand Democratic) populations, which might not accurately mirror the global\ndemographic diversity. We evaluated the dependence on WEIRD populations in\nresearch presented at the AAAI ICWSM conference; the only venue whose\nproceedings are fully dedicated to social computing research. We did so by\nanalyzing 494 papers published from 2018 to 2022, which included full research\npapers, dataset papers and posters. After filtering out papers that analyze\nsynthetic datasets or those lacking clear country of origin, we were left with\n420 papers from which 188 participants in a crowdsourcing study with full\nmanual validation extracted data for the WEIRD scores computation. This data\nwas then used to adapt existing WEIRD metrics to be applicable for social media\ndata. We found that 37% of these papers focused solely on data from Western\ncountries. This percentage is significantly less than the percentages observed\nin research from CHI (76%) and FAccT (84%) conferences, suggesting a greater\ndiversity of dataset origins within ICWSM. However, the studies at ICWSM still\npredominantly examine populations from countries that are more Educated,\nIndustrialized, and Rich in comparison to those in FAccT, with a special note\non the 'Democratic' variable reflecting political freedoms and rights. This\npoints out the utility of social media data in shedding light on findings from\ncountries with restricted political freedoms. Based on these insights, we\nrecommend extensions of current \"paper checklists\" to include considerations\nabout the WEIRD bias and call for the community to broaden research inclusivity\nby encouraging the use of diverse datasets from underrepresented regions.\n", "date": "2024-06-04", "categories": []}
{"id": "2307.02863", "title": "ValiText -- a unified validation framework for computational text-based\n  measures of social constructs", "abstract": "  Guidance on how to validate computational text-based measures of social\nconstructs is fragmented. While researchers generally acknowledge the\nimportance of validating text-based measures, they often lack a shared\nvocabulary and a unified framework to do so. This paper introduces ValiText, a\nnew validation framework designed to assist scholars in validly measuring\nsocial constructs in textual data. The framework is built on a conceptual\nfoundation of validity in the social sciences, strengthened by an empirical\nreview of validation practices in the social sciences and consultations with\nexperts. Ultimately, ValiText prescribes researchers to demonstrate three types\nof validation evidence: substantive evidence (outlining the theoretical\nunderpinning of the measure), structural evidence (examining the properties of\nthe text model and its output) and external evidence (testing for how the\nmeasure relates to independent information). The framework is further\nsupplemented by a checklist of validation steps, offering practical guidance in\nthe form of documentation sheets that guide researchers in the validation\nprocess.\n", "date": "2023-07-06", "categories": []}
{"id": "2406.06052", "title": "A Multidimensional Framework for Evaluating Lexical Semantic Change with\n  Social Science Applications", "abstract": "  Historical linguists have identified multiple forms of lexical semantic\nchange. We present a three-dimensional framework for integrating these forms\nand a unified computational methodology for evaluating them concurrently. The\ndimensions represent increases or decreases in semantic 1) sentiment, 2)\nbreadth, and 3) intensity. These dimensions can be complemented by the\nevaluation of shifts in the frequency of the target words and the thematic\ncontent of its collocates. This framework enables lexical semantic change to be\nmapped economically and systematically and has applications in computational\nsocial science. We present an illustrative analysis of semantic shifts in\nmental health and mental illness in two corpora, demonstrating patterns of\nsemantic change that illuminate contemporary concerns about pathologization,\nstigma, and concept creep.\n", "date": "2024-06-10", "categories": []}
{"id": "2207.12264", "title": "Dynamics and triggers of misinformation on vaccines", "abstract": "  The Covid-19 pandemic has sparked renewed attention on the prevalence of\nmisinformation online, whether intentional or not, underscoring the potential\nrisks posed to individuals' quality of life associated with the dissemination\nof misconceptions and enduring myths on health-related subjects. In this study,\nwe analyze 6 years (2016-2021) of Italian vaccine debate across diverse social\nmedia platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major\nnews sources - both questionable and reliable. We first use the symbolic\ntransfer entropy analysis of news production time-series to dynamically\ndetermine which category of sources, questionable or reliable, causally drives\nthe agenda on vaccines. Then, leveraging deep learning models capable to\naccurately classify vaccine-related content based on the conveyed stance and\ndiscussed topic, respectively, we evaluate the focus on various topics by news\nsources promoting opposing views and compare the resulting user engagement.\nAside from providing valuable resources for further investigation of\nvaccine-related misinformation, particularly in a language (Italian) that\nreceives less attention in scientific research compared to languages like\nEnglish, our study uncovers misinformation not as a parasite of the news\necosystem that merely opposes the perspectives offered by mainstream media, but\nas an autonomous force capable of even overwhelming the production of\nvaccine-related content from the latter. While the pervasiveness of\nmisinformation is evident in the significantly higher engagement of\nquestionable sources compared to reliable ones, our findings underscore the\nimportance of consistent and thorough pro-vax coverage. This is especially\ncrucial in addressing the most sensitive topics where the risk of\nmisinformation spreading and potentially exacerbating negative attitudes toward\nvaccines among the users involved is higher.\n", "date": "2022-07-25", "categories": []}
{"id": "2309.08631", "title": "Large Language Models Can Infer Psychological Dispositions of Social\n  Media Users", "abstract": "  Large Language Models (LLMs) demonstrate increasingly human-like abilities\nacross a wide variety of tasks. In this paper, we investigate whether LLMs like\nChatGPT can accurately infer the psychological dispositions of social media\nusers and whether their ability to do so varies across socio-demographic\ngroups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five\npersonality traits from users' Facebook status updates in a zero-shot learning\nscenario. Our results show an average correlation of r = .29 (range = [.22,\n.33]) between LLM-inferred and self-reported trait scores - a level of accuracy\nthat is similar to that of supervised machine learning models specifically\ntrained to infer personality. Our findings also highlight heterogeneity in the\naccuracy of personality inferences across different age groups and gender\ncategories: predictions were found to be more accurate for women and younger\nindividuals on several traits, suggesting a potential bias stemming from the\nunderlying training data or differences in online self-expression. The ability\nof LLMs to infer psychological dispositions from user-generated text has the\npotential to democratize access to cheap and scalable psychometric assessments\nfor both researchers and practitioners. On the one hand, this democratization\nmight facilitate large-scale research of high ecological validity and spark\ninnovation in personalized services. On the other hand, it also raises ethical\nconcerns regarding user privacy and self-determination, highlighting the need\nfor stringent ethical frameworks and regulation.\n", "date": "2023-09-13", "categories": []}
{"id": "2405.18613", "title": "GLOCON Database: Design Decisions and User Manual (v1.0)", "abstract": "  GLOCON is a database of contentious events automatically extracted from\nnational news sources from various countries in multiple languages. National\nnews sources are utilized, and complete news archives are processed to create\nan event list for each source. Automation is achieved using a gold standard\ncorpus sampled randomly from complete news archives (Y\\\"or\\\"uk et al. 2022) and\nall annotated by at least two domain experts based on the event definition\nprovided in Duru\\c{s}an et al. (2022).\n", "date": "2024-05-28", "categories": []}
{"id": "2403.08391", "title": "Misinformation is not about Bad Facts: An Analysis of the Production and\n  Consumption of Fringe Content", "abstract": "  What if misinformation is not an information problem at all? To understand\nthe role of news publishers in potentially unintentionally propagating\nmisinformation, we examine how far-right and fringe online groups share and\nleverage established legacy news media articles to advance their narratives.\nOur findings suggest that online fringe ideologies spread through the use of\ncontent that is consensus-based and \"factually correct\". We found that\nAustralian news publishers with both moderate and far-right political leanings\ncontain comparable levels of information completeness and quality; and\nfurthermore, that far-right Twitter users often share from moderate sources.\nHowever, a stark difference emerges when we consider two additional factors: 1)\nthe narrow topic selection of articles by far-right users, suggesting that they\ncherry pick only news articles that engage with their preexisting worldviews\nand specific topics of concern, and 2) the difference between moderate and\nfar-right publishers when we examine the writing style of their articles.\nFurthermore, we can identify users prone to sharing misinformation based on\ntheir communication style. These findings have important implications for\ncountering online misinformation, as they highlight the powerful role that\npersonal biases towards specific topics and publishers' writing styles have in\namplifying fringe ideologies online.\n", "date": "2024-03-13", "categories": []}
{"id": "2311.09733", "title": "MOKA: Moral Knowledge Augmentation for Moral Event Extraction", "abstract": "  News media often strive to minimize explicit moral language in news articles,\nyet most articles are dense with moral values as expressed through the reported\nevents themselves. However, values that are reflected in the intricate dynamics\namong participating entities and moral events are far more challenging for most\nNLP systems to detect, including LLMs. To study this phenomenon, we annotate a\nnew dataset, MORAL EVENTS, consisting of 5,494 structured event annotations on\n474 news articles by diverse US media across the political spectrum. We further\npropose MOKA, a moral event extraction framework with MOral Knowledge\nAugmentation, which leverages knowledge derived from moral words and moral\nscenarios to produce structural representations of morality-bearing events.\nExperiments show that MOKA outperforms competitive baselines across three moral\nevent understanding tasks. Further analysis shows even ostensibly nonpartisan\nmedia engage in the selective reporting of moral events. Our data and codebase\nare available at https://github.com/launchnlp/MOKA.\n", "date": "2023-11-16", "categories": []}
{"id": "2401.13945", "title": "General Automatic Solution Generation of Social Problems", "abstract": "  Given the escalating intricacy and multifaceted nature of contemporary social\nsystems, manually generating solutions to address pertinent social issues has\nbecome a formidable task. In response to this challenge, the rapid development\nof artificial intelligence has spurred the exploration of computational\nmethodologies aimed at automatically generating solutions. However, current\nmethods for auto-generation of solutions mainly concentrate on local social\nregulations that pertain to specific scenarios. Here, we report an automatic\nsocial operating system (ASOS) designed for general social solution generation,\nwhich is built upon agent-based models, enabling both global and local analyses\nand regulations of social problems across spatial and temporal dimensions. ASOS\nadopts a hypergraph with extensible social semantics for a comprehensive and\nstructured representation of social dynamics. It also incorporates a\ngeneralized protocol for standardized hypergraph operations and a symbolic\nhybrid framework that delivers interpretable solutions, yielding a balance\nbetween regulatory efficacy and function viability. To demonstrate the\neffectiveness of ASOS, we apply it to the domain of averting extreme events\nwithin international oil futures markets. By generating a new trading role\nsupplemented by new mechanisms, ASOS can adeptly discern precarious market\nconditions and make front-running interventions for non-profit purposes. This\nstudy demonstrates that ASOS provides an efficient and systematic approach for\ngenerating solutions for enhancing our society.\n", "date": "2024-01-25", "categories": []}
{"id": "2402.01427", "title": "The effect of diversity on group decision-making", "abstract": "  We explore different aspects of cognitive diversity and its effect on the\nsuccess of group deliberation. To evaluate this, we use 500 dialogues from\nsmall, online groups discussing the Wason Card Selection task - the DeliData\ncorpus. Leveraging the corpus, we perform quantitative analysis evaluating\nthree different measures of cognitive diversity. First, we analyse the effect\nof group size as a proxy measure for diversity. Second, we evaluate the effect\nof the size of the initial idea pool. Finally, we look into the content of the\ndiscussion by analysing discussed solutions, discussion patterns, and how\nconversational probing can improve those characteristics. Despite the\nreputation of groups for compounding bias, we show that small groups can,\nthrough dialogue, overcome intuitive biases and improve individual\ndecision-making. Across a large sample and different operationalisations, we\nconsistently find that greater cognitive diversity is associated with more\nsuccessful group deliberation. Code and data used for the analysis are\navailable in the repository:\nhttps://github.com/gkaradzhov/cognitive-diversity-groups-cogsci24.\n", "date": "2024-02-02", "categories": []}
{"id": "2404.10228", "title": "Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural\n  Networks", "abstract": "  The high volume and rapid evolution of content on social media present major\nchallenges for studying the stance of social media users. In this work, we\ndevelop a two stage stance labeling method that utilizes the user-hashtag\nbipartite graph and the user-user interaction graph. In the first stage, a\nsimple and efficient heuristic for stance labeling uses the user-hashtag\nbipartite graph to iteratively update the stance association of user and\nhashtag nodes via a label propagation mechanism. This set of soft labels is\nthen integrated with the user-user interaction graph to train a graph neural\nnetwork (GNN) model using semi-supervised learning. We evaluate this method on\ntwo large-scale datasets containing tweets related to climate change from June\n2021 to June 2022 and gun control from January 2022 to January 2023. Our\nexperiments demonstrate that enriching text-based embeddings of users with\nnetwork information from the user interaction graph using our semi-supervised\nGNN method outperforms both classifiers trained on user textual embeddings and\nzero-shot classification using LLMs such as GPT4. We discuss the need for\nintegrating nuanced understanding from social science with the scalability of\ncomputational methods to better understand how polarization on social media\noccurs for divisive issues such as climate change and gun control.\n", "date": "2024-04-16", "categories": []}
{"id": "2405.08142", "title": "Discursive objection strategies in online comments: Developing a\n  classification schema and validating its training", "abstract": "  Most Americans agree that misinformation, hate speech and harassment are\nharmful and inadequately curbed on social media through current moderation\npractices. In this paper, we aim to understand the discursive strategies\nemployed by people in response to harmful speech in news comments. We conducted\na content analysis of more than 6500 comment replies to trending news videos on\nYouTube and Twitter and identified seven distinct discursive objection\nstrategies (Study 1). We examined the frequency of each strategy's occurrence\nfrom the 6500 comment replies, as well as from a second sample of 2004 replies\n(Study 2). Together, these studies show that people deploy a diversity of\ndiscursive strategies when objecting to speech, and reputational attacks are\nthe most common. The resulting classification scheme accounts for different\ntheoretical approaches for expressing objections and offers a comprehensive\nperspective on grassroots efforts aimed at stopping offensive or problematic\nspeech on campus.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.08331", "title": "Are Generics and Negativity about Social Groups Common on Social Media?\n  A Comparative Analysis of Twitter (X) Data", "abstract": "  Generics (unquantified generalizations) are thought to be pervasive in\ncommunication and when they are about social groups, this may offend and\npolarize people because generics gloss over variations between individuals.\nGenerics about social groups might be particularly common on Twitter (X). This\nremains unexplored, however. Using machine learning (ML) techniques, we\ntherefore developed an automatic classifier for social generics, applied it to\nmore than a million tweets about people, and analyzed the tweets. We found that\nmost tweets (78%) about people contained no generics. However, tweets with\nsocial generics received more 'likes' and retweets. Furthermore, while recent\npsychological research may lead to the prediction that tweets with generics\nabout political groups are more common than tweets with generics about ethnic\ngroups, we found the opposite. However, consistent with recent claims that\npolitical animosity is less constrained by social norms than animosity against\ngender and ethnic groups, negative tweets with generics about political groups\nwere significantly more prevalent and retweeted than negative tweets about\nethnic groups. Our study provides the first ML-based insights into the use and\nimpact of social generics on Twitter.\n", "date": "2024-05-14", "categories": []}
{"id": "2405.07010", "title": "Deciphering public attention to geoengineering and climate issues using\n  machine learning and dynamic analysis", "abstract": "  As the conversation around using geoengineering to combat climate change\nintensifies, it is imperative to engage the public and deeply understand their\nperspectives on geoengineering research, development, and potential deployment.\nThrough a comprehensive data-driven investigation, this paper explores the\ntypes of news that captivate public interest in geoengineering. We delved into\n30,773 English-language news articles from the BBC and the New York Times,\ncombined with Google Trends data spanning 2018 to 2022, to explore how public\ninterest in geoengineering fluctuates in response to news coverage of broader\nclimate issues. Using BERT-based topic modeling, sentiment analysis, and\ntime-series regression models, we found that positive sentiment in\nenergy-related news serves as a good predictor of heightened public interest in\ngeoengineering, a trend that persists over time. Our findings suggest that\npublic engagement with geoengineering and climate action is not uniform, with\nsome topics being more potent in shaping interest over time, such as climate\nnews related to energy, disasters, and politics. Understanding these patterns\nis crucial for scientists, policymakers, and educators aiming to craft\neffective strategies for engaging with the public and fostering dialogue around\nemerging climate technologies.\n", "date": "2024-05-11", "categories": []}
{"id": "2405.07320", "title": "L(u)PIN: LLM-based Political Ideology Nowcasting", "abstract": "  The quantitative analysis of political ideological positions is a difficult\ntask. In the past, various literature focused on parliamentary voting data of\npoliticians, party manifestos and parliamentary speech to estimate political\ndisagreement and polarization in various political systems. However previous\nmethods of quantitative political analysis suffered from a common challenge\nwhich was the amount of data available for analysis. Also previous methods\nfrequently focused on a more general analysis of politics such as overall\npolarization of the parliament or party-wide political ideological positions.\nIn this paper, we present a method to analyze ideological positions of\nindividual parliamentary representatives by leveraging the latent knowledge of\nLLMs. The method allows us to evaluate the stance of politicians on an axis of\nour choice allowing us to flexibly measure the stance of politicians in regards\nto a topic/controversy of our choice. We achieve this by using a fine-tuned\nBERT classifier to extract the opinion-based sentences from the speeches of\nrepresentatives and projecting the average BERT embeddings for each\nrepresentative on a pair of reference seeds. These reference seeds are either\nmanually chosen representatives known to have opposing views on a particular\ntopic or they are generated sentences which where created using the GPT-4 model\nof OpenAI. We created the sentences by prompting the GPT-4 model to generate a\nspeech that would come from a politician defending a particular position.\n", "date": "2024-05-12", "categories": []}
{"id": "2405.07363", "title": "Multilingual Power and Ideology Identification in the Parliament: a\n  Reference Dataset and Simple Baselines", "abstract": "  We introduce a dataset on political orientation and power position\nidentification. The dataset is derived from ParlaMint, a set of comparable\ncorpora of transcribed parliamentary speeches from 29 national and regional\nparliaments. We introduce the dataset, provide the reasoning behind some of the\nchoices during its creation, present statistics on the dataset, and, using a\nsimple classifier, some baseline results on predicting political orientation on\nthe left-to-right axis, and on power position identification, i.e.,\ndistinguishing between the speeches delivered by governing coalition party\nmembers from those of opposition party members.\n", "date": "2024-05-12", "categories": []}
{"id": "2301.06774", "title": "Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes,\n  and Influence", "abstract": "  Large-scale online campaigns, malicious or otherwise, require a significant\ndegree of coordination among participants, which sparked interest in the study\nof coordinated online behavior. State-of-the-art methods for detecting\ncoordinated behavior perform static analyses, disregarding the temporal\ndynamics of coordination. Here, we carry out the first dynamic analysis of\ncoordinated behavior. To reach our goal we build a multiplex temporal network\nand we perform dynamic community detection to identify groups of users that\nexhibited coordinated behaviors in time. Thanks to our novel approach we find\nthat: (i) coordinated communities feature variable degrees of temporal\ninstability; (ii) dynamic analyses are needed to account for such instability,\nand results of static analyses can be unreliable and scarcely representative of\nunstable communities; (iii) some users exhibit distinct archetypal behaviors\nthat have important practical implications; (iv) content and network\ncharacteristics contribute to explaining why users leave and join coordinated\ncommunities. Our results demonstrate the advantages of dynamic analyses and\nopen up new directions of research on the unfolding of online debates, on the\nstrategies of coordinated communities, and on the patterns of online influence.\n", "date": "2023-01-17", "categories": []}
{"id": "2311.10781", "title": "Can Language Model Moderators Improve the Health of Online Discourse?", "abstract": "  Conversational moderation of online communities is crucial to maintaining\ncivility for a constructive environment, but it is challenging to scale and\nharmful to moderators. The inclusion of sophisticated natural language\ngeneration modules as a force multiplier to aid human moderators is a\ntantalizing prospect, but adequate evaluation approaches have so far been\nelusive. In this paper, we establish a systematic definition of conversational\nmoderation effectiveness grounded on moderation literature and establish design\ncriteria for conducting realistic yet safe evaluation. We then propose a\ncomprehensive evaluation framework to assess models' moderation capabilities\nindependently of human intervention. With our framework, we conduct the first\nknown study of language models as conversational moderators, finding that\nappropriately prompted models that incorporate insights from social science can\nprovide specific and fair feedback on toxic behavior but struggle to influence\nusers to increase their levels of respect and cooperation.\n", "date": "2023-11-16", "categories": []}
{"id": "2405.02472", "title": "Semantic Scaling: Bayesian Ideal Point Estimates with Large Language\n  Models", "abstract": "  This paper introduces \"Semantic Scaling,\" a novel method for ideal point\nestimation from text. I leverage large language models to classify documents\nbased on their expressed stances and extract survey-like data. I then use item\nresponse theory to scale subjects from these data. Semantic Scaling\nsignificantly improves on existing text-based scaling methods, and allows\nresearchers to explicitly define the ideological dimensions they measure. This\nrepresents the first scaling approach that allows such flexibility outside of\nsurvey instruments and opens new avenues of inquiry for populations difficult\nto survey. Additionally, it works with documents of varying length, and\nproduces valid estimates of both mass and elite ideology. I demonstrate that\nthe method can differentiate between policy preferences and in-group/out-group\naffect. Among the public, Semantic Scaling out-preforms Tweetscores according\nto human judgement; in Congress, it recaptures the first dimension DW-NOMINATE\nwhile allowing for greater flexibility in resolving construct validity\nchallenges.\n", "date": "2024-05-03", "categories": []}
{"id": "2405.02677", "title": "Evaluating the Ability of Computationally Extracted Narrative Maps to\n  Encode Media Framing", "abstract": "  Narratives serve as fundamental frameworks in our understanding of the world\nand play a crucial role in collaborative sensemaking, providing a versatile\nfoundation for sensemaking. Framing is a subtle yet potent mechanism that\ninfluences public perception through specific word choices, shaping\ninterpretations of reported news events. Despite the recognized importance of\nnarratives and framing, a significant gap exists in the literature with regard\nto the explicit consideration of framing within the context of computational\nextraction and representation. This article explores the capabilities of a\nspecific narrative extraction and representation approach -- narrative maps --\nto capture framing information from news data. The research addresses two key\nquestions: (1) Does the narrative extraction method capture the framing\ndistribution of the data set? (2) Does it produce a representation with\nconsistent framing? Our results indicate that while the algorithm captures\nframing distributions, achieving consistent framing across various starting and\nending events poses challenges. Our results highlight the potential of\nnarrative maps to provide users with insights into the intricate framing\ndynamics within news narratives. However, we note that directly leveraging\nframing information in the computational narrative extraction process remains\nan open challenge.\n", "date": "2024-05-04", "categories": []}
{"id": "2403.05585", "title": "Plasmon Resonance Model: Investigation of Analysis of Fake News\n  Diffusion Model with Third Mover Intervention Using Soliton Solution in\n  Non-Complete Information Game under Repeated Dilemma Condition", "abstract": "  In this research note, we propose a new approach to model the fake news\ndiffusion process within the framework of incomplete information games. In\nparticular, we use nonlinear partial differential equations to represent the\nphenomenon of plasmon resonance, in which the diffusion of fake news is rapidly\namplified within a particular social group or communication network, and\nanalyze its dynamics through a soliton solution approach. In addition, we\nconsider how first mover, second mover, and third mover strategies interact\nwithin this nonlinear system and contribute to the amplification or suppression\nof fake news diffusion. The model aims to understand the mechanisms of fake\nnews proliferation and provide insights into how to prevent or combat it. By\ncombining concepts from the social sciences and the physical sciences, this\nstudy attempts to develop a new theoretical framework for the contemporary\nproblem of fake news.This paper is partially an attempt to utilize \"Generative\nAI\" and was written with educational intent. There are currently no plans for\nit to become a peer-reviewed paper.\n", "date": "2024-03-03", "categories": []}
{"id": "2404.18940", "title": "Conceptual Mapping of Controversies", "abstract": "  With our work, we contribute towards a qualitative analysis of the discourse\non controversies in online news media. For this, we employ Formal Concept\nAnalysis and the economics of conventions to derive conceptual controversy\nmaps. In our experiments, we analyze two maps from different news journals with\nmethods from ordinal data science. We show how these methods can be used to\nassess the diversity, complexity and potential bias of controversies. In\naddition to that, we discuss how the diagrams of concept lattices can be used\nto navigate between news articles.\n", "date": "2024-04-25", "categories": []}
{"id": "2309.07384", "title": "An Interactive Framework for Profiling News Media Sources", "abstract": "  The recent rise of social media has led to the spread of large amounts of\nfake and biased news, content published with the intent to sway beliefs. While\ndetecting and profiling the sources that spread this news is important to\nmaintain a healthy society, it is challenging for automated systems.\n  In this paper, we propose an interactive framework for news media profiling.\nIt combines the strengths of graph based news media profiling models,\nPre-trained Large Language Models, and human insight to characterize the social\ncontext on social media. Experimental results show that with as little as 5\nhuman interactions, our framework can rapidly detect fake and biased news\nmedia, even in the most challenging settings of emerging news events, where\ntest data is unseen.\n", "date": "2023-09-14", "categories": []}
{"id": "2402.17151", "title": "Clustering Document Parts: Detecting and Characterizing Influence\n  Campaigns from Documents", "abstract": "  We propose a novel clustering pipeline to detect and characterize influence\ncampaigns from documents. This approach clusters parts of document, detects\nclusters that likely reflect an influence campaign, and then identifies\ndocuments linked to an influence campaign via their association with the\nhigh-influence clusters. Our approach outperforms both the direct\ndocument-level classification and the direct document-level clustering approach\nin predicting if a document is part of an influence campaign. We propose\nvarious novel techniques to enhance our pipeline, including using an existing\nevent factuality prediction system to obtain document parts, and aggregating\nmultiple clustering experiments to improve the performance of both cluster and\ndocument classification. Classifying documents after clustering not only\naccurately extracts the parts of the documents that are relevant to influence\ncampaigns, but also captures influence campaigns as a coordinated and holistic\nphenomenon. Our approach makes possible more fine-grained and interpretable\ncharacterizations of influence campaigns from documents.\n", "date": "2024-02-27", "categories": []}
{"id": "2404.18726", "title": "The Constant in HATE: Analyzing Toxicity in Reddit across Topics and\n  Languages", "abstract": "  Toxic language remains an ongoing challenge on social media platforms,\npresenting significant issues for users and communities. This paper provides a\ncross-topic and cross-lingual analysis of toxicity in Reddit conversations. We\ncollect 1.5 million comment threads from 481 communities in six languages:\nEnglish, German, Spanish, Turkish,Arabic, and Dutch, covering 80 topics such as\nCulture, Politics, and News. We thoroughly analyze how toxicity spikes within\ndifferent communities in relation to specific topics. We observe consistent\npatterns of increased toxicity across languages for certain topics, while also\nnoting significant variations within specific language communities.\n", "date": "2024-04-29", "categories": []}
{"id": "2404.15673", "title": "Augmented CARDS: A machine learning approach to identifying triggers of\n  climate change misinformation on Twitter", "abstract": "  Misinformation about climate change poses a significant threat to societal\nwell-being, prompting the urgent need for effective mitigation strategies.\nHowever, the rapid proliferation of online misinformation on social media\nplatforms outpaces the ability of fact-checkers to debunk false claims.\nAutomated detection of climate change misinformation offers a promising\nsolution. In this study, we address this gap by developing a two-step\nhierarchical model, the Augmented CARDS model, specifically designed for\ndetecting contrarian climate claims on Twitter. Furthermore, we apply the\nAugmented CARDS model to five million climate-themed tweets over a six-month\nperiod in 2022. We find that over half of contrarian climate claims on Twitter\ninvolve attacks on climate actors or conspiracy theories. Spikes in climate\ncontrarianism coincide with one of four stimuli: political events, natural\nevents, contrarian influencers, or convinced influencers. Implications for\nautomated responses to climate misinformation are discussed.\n", "date": "2024-04-24", "categories": []}
{"id": "2301.06270", "title": "Computational Assessment of Hyperpartisanship in News Titles", "abstract": "  We first adopt a human-guided machine learning framework to develop a new\ndataset for hyperpartisan news title detection with 2,200 manually labeled and\n1.8 million machine-labeled titles that were posted from 2014 to the present by\nnine representative media organizations across three media bias groups - Left,\nCentral, and Right in an active learning manner. A fine-tuned transformer-based\nlanguage model achieves an overall accuracy of 0.84 and an F1 score of 0.78 on\nan external validation set. Next, we conduct a computational analysis to\nquantify the extent and dynamics of partisanship in news titles. While some\naspects are as expected, our study reveals new or nuanced differences between\nthe three media groups. We find that overall the Right media tends to use\nproportionally more hyperpartisan titles. Roughly around the 2016 Presidential\nElection, the proportions of hyperpartisan titles increased across all media\nbias groups, with the Left media exhibiting the most significant relative\nincrease. We identify three major topics including foreign issues, political\nsystems, and societal issues that are suggestive of hyperpartisanship in news\ntitles using logistic regression models and the Shapley values. Through an\nanalysis of the topic distribution, we find that societal issues gradually gain\nmore attention from all media groups. We further apply a lexicon-based language\nanalysis tool to the titles of each topic and quantify the linguistic distance\nbetween any pairs of the three media groups, uncovering three distinct\npatterns.\n", "date": "2023-01-16", "categories": []}
{"id": "2310.19268", "title": "Moral Sparks in Social Media Narratives", "abstract": "  There is increasing interest in building computational models of moral\nreasoning by people to enable effective interaction by Artificial Intelligence\n(AI) agents. We examine interactions on social media to understand human moral\njudgments in real-life ethical scenarios. Specifically, we examine posts from a\npopular Reddit subreddit (i.e., a subcommunity) called r/AmITheAsshole, where\nauthors and commenters share their moral judgments on who (i.e., which\nparticipant of the described scenario) is blameworthy. To investigate the\nunderlying reasoning influencing moral judgments, we focus on excerpts-which we\nterm moral sparks-from original posts that some commenters include to indicate\nwhat motivates their judgments. To this end, we examine how (1) events\nactivating social commonsense and (2) linguistic signals affect the identified\nmoral sparks and their subsequent judgments. By examining over 24672 posts and\n175988 comments, we find that event-related negative character traits (e.g.,\nimmature and rude) attract attention and stimulate blame, implying a dependent\nrelationship between character traits and moral values. Specifically, we focus\non causal graphs involving events (c-events) that activate social commonsense.\nWe observe that c-events are perceived with varying levels of informativeness,\ninfluencing moral spark and judgment assignment in distinct ways. This\nobservation is reinforced by examining linguistic features describing\nsemantically similar c-events. Moreover, language influencing commenters'\ncognitive processes enhances the probability of an excerpt becoming a moral\nspark, while factual and concrete descriptions tend to inhibit this effect.\n", "date": "2023-10-30", "categories": []}
{"id": "2401.10940", "title": "RELIANCE: Reliable Ensemble Learning for Information and News\n  Credibility Evaluation", "abstract": "  In the era of information proliferation, discerning the credibility of news\ncontent poses an ever-growing challenge. This paper introduces RELIANCE, a\npioneering ensemble learning system designed for robust information and fake\nnews credibility evaluation. Comprising five diverse base models, including\nSupport Vector Machine (SVM), naive Bayes, logistic regression, random forest,\nand Bidirectional Long Short Term Memory Networks (BiLSTMs), RELIANCE employs\nan innovative approach to integrate their strengths, harnessing the collective\nintelligence of the ensemble for enhanced accuracy. Experiments demonstrate the\nsuperiority of RELIANCE over individual models, indicating its efficacy in\ndistinguishing between credible and non-credible information sources. RELIANCE,\nalso surpasses baseline models in information and news credibility assessment,\nestablishing itself as an effective solution for evaluating the reliability of\ninformation sources.\n", "date": "2024-01-17", "categories": []}
{"id": "2404.13192", "title": "Heterogeneous Subgraph Transformer for Fake News Detection", "abstract": "  Fake news is pervasive on social media, inflicting substantial harm on public\ndiscourse and societal well-being. We investigate the explicit structural\ninformation and textual features of news pieces by constructing a heterogeneous\ngraph concerning the relations among news topics, entities, and content.\nThrough our study, we reveal that fake news can be effectively detected in\nterms of the atypical heterogeneous subgraphs centered on them, which\nencapsulate the essential semantics and intricate relations between news\nelements. However, suffering from the heterogeneity, exploring such\nheterogeneous subgraphs remains an open problem. To bridge the gap, this work\nproposes a heterogeneous subgraph transformer (HeteroSGT) to exploit subgraphs\nin our constructed heterogeneous graph. In HeteroSGT, we first employ a\npre-trained language model to derive both word-level and sentence-level\nsemantics. Then the random walk with restart (RWR) is applied to extract\nsubgraphs centered on each news, which are further fed to our proposed subgraph\nTransformer to quantify the authenticity. Extensive experiments on five\nreal-world datasets demonstrate the superior performance of HeteroSGT over five\nbaselines. Further case and ablation studies validate our motivation and\ndemonstrate that performance improvement stems from our specially designed\ncomponents.\n", "date": "2024-04-19", "categories": []}
{"id": "2404.02761", "title": "AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation\n  Quality in Online Discussions Using LLMs", "abstract": "  Measuring the quality of contributions in political online discussions is\ncrucial in deliberation research and computer science. Research has identified\nvarious indicators to assess online discussion quality, and with deep learning\nadvancements, automating these measures has become feasible. While some studies\nfocus on analyzing specific quality indicators, a comprehensive quality score\nincorporating various deliberative aspects is often preferred. In this work, we\nintroduce AQuA, an additive score that calculates a unified deliberative\nquality score from multiple indices for each discussion post. Unlike other\nsingular scores, AQuA preserves information on the deliberative aspects present\nin comments, enhancing model transparency. We develop adapter models for 20\ndeliberative indices, and calculate correlation coefficients between experts'\nannotations and the perceived deliberativeness by non-experts to weigh the\nindividual indices into a single deliberative score. We demonstrate that the\nAQuA score can be computed easily from pre-trained adapters and aligns well\nwith annotations on other datasets that have not be seen during training. The\nanalysis of experts' vs. non-experts' annotations confirms theoretical findings\nin the social science literature.\n", "date": "2024-04-03", "categories": []}
{"id": "2306.07117", "title": "Language of Bargaining", "abstract": "  Leveraging an established exercise in negotiation education, we build a novel\ndataset for studying how the use of language shapes bilateral bargaining. Our\ndataset extends existing work in two ways: 1) we recruit participants via\nbehavioral labs instead of crowdsourcing platforms and allow participants to\nnegotiate through audio, enabling more naturalistic interactions; 2) we add a\ncontrol setting where participants negotiate only through alternating, written\nnumeric offers. Despite the two contrasting forms of communication, we find\nthat the average agreed prices of the two treatments are identical. But when\nsubjects can talk, fewer offers are exchanged, negotiations finish faster, the\nlikelihood of reaching agreement rises, and the variance of prices at which\nsubjects agree drops substantially. We further propose a taxonomy of speech\nacts in negotiation and enrich the dataset with annotated speech acts. Our work\nalso reveals linguistic signals that are predictive of negotiation outcomes.\n", "date": "2023-06-12", "categories": []}
{"id": "2403.16099", "title": "A Multi-Label Dataset of French Fake News: Human and Machine Insights", "abstract": "  We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of\nFrench press considered unreliable by expert agencies, annotated using 11\nlabels by 8 annotators. By collecting more labels than usual, by more\nannotators than is typically done, we can identify features that humans\nconsider as characteristic of fake news, and compare them to the predictions of\nautomated classifiers. We present a topic and genre analysis using Gate Cloud,\nindicative of the prevalence of satire-like text in the corpus. We then use the\nsubjectivity analyzer VAGO, and a neural version of it, to clarify the link\nbetween ascriptions of the label Subjective and ascriptions of the label Fake\nNews. The annotated dataset is available online at the following url:\nhttps://github.com/obs-info/obsinfox\n  Keywords: Fake News, Multi-Labels, Subjectivity, Vagueness, Detail, Opinion,\nExaggeration, French Press\n", "date": "2024-03-24", "categories": []}
{"id": "2404.07036", "title": "A Computational Analysis of the Dehumanisation of Migrants from Syria\n  and Ukraine in Slovene News Media", "abstract": "  Dehumanisation involves the perception and or treatment of a social group's\nmembers as less than human. This phenomenon is rarely addressed with\ncomputational linguistic techniques. We adapt a recently proposed approach for\nEnglish, making it easier to transfer to other languages and to evaluate,\nintroducing a new sentiment resource, the use of zero-shot cross-lingual\nvalence and arousal detection, and a new method for statistical significance\ntesting. We then apply it to study attitudes to migration expressed in Slovene\nnewspapers, to examine changes in the Slovene discourse on migration between\nthe 2015-16 migration crisis following the war in Syria and the 2022-23 period\nfollowing the war in Ukraine. We find that while this discourse became more\nnegative and more intense over time, it is less dehumanising when specifically\naddressing Ukrainian migrants compared to others.\n", "date": "2024-04-10", "categories": []}
{"id": "2306.12886", "title": "Unveiling Global Narratives: A Multilingual Twitter Dataset of News\n  Media on the Russo-Ukrainian Conflict", "abstract": "  The ongoing Russo-Ukrainian conflict has been a subject of intense media\ncoverage worldwide. Understanding the global narrative surrounding this topic\nis crucial for researchers that aim to gain insights into its multifaceted\ndimensions. In this paper, we present a novel multimedia dataset that focuses\non this topic by collecting and processing tweets posted by news or media\ncompanies on social media across the globe. We collected tweets from February\n2022 to May 2023 to acquire approximately 1.5 million tweets in 60 different\nlanguages along with their images. Each entry in the dataset is accompanied by\nprocessed tags, allowing for the identification of entities, stances, textual\nor visual concepts, and sentiment. The availability of this multimedia dataset\nserves as a valuable resource for researchers aiming to investigate the global\nnarrative surrounding the ongoing conflict from various aspects such as who are\nthe prominent entities involved, what stances are taken, where do these stances\noriginate from, how are the different textual and visual concepts related to\nthe event portrayed.\n", "date": "2023-06-22", "categories": []}
{"id": "2404.04261", "title": "A Novel BERT-based Classifier to Detect Political Leaning of YouTube\n  Videos based on their Titles", "abstract": "  A quarter of US adults regularly get their news from YouTube. Yet, despite\nthe massive political content available on the platform, to date no classifier\nhas been proposed to identify the political leaning of YouTube videos. To fill\nthis gap, we propose a novel classifier based on Bert -- a language model from\nGoogle -- to classify YouTube videos merely based on their titles into six\ncategories, namely: Far Left, Left, Center, Anti-Woke, Right, and Far Right. We\nused a public dataset of 10 million YouTube video titles (under various\ncategories) to train and validate the proposed classifier. We compare the\nclassifier against several alternatives that we trained on the same dataset,\nrevealing that our classifier achieves the highest accuracy (75%) and the\nhighest F1 score (77%). To further validate the classification performance, we\ncollect videos from YouTube channels of numerous prominent news agencies, such\nas Fox News and New York Times, which have widely known political leanings, and\napply our classifier to their video titles. For the vast majority of cases, the\npredicted political leaning matches that of the news agency.\n", "date": "2024-02-16", "categories": []}
{"id": "2404.04031", "title": "Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the\n  Evaluative Meaning of German Personal Name Compounds", "abstract": "  We present a comprehensive computational study of the under-investigated\nphenomenon of personal name compounds (PNCs) in German such as\nWillkommens-Merkel ('Welcome-Merkel'). Prevalent in news, social media, and\npolitical discourse, PNCs are hypothesized to exhibit an evaluative function\nthat is reflected in a more positive or negative perception as compared to the\nrespective personal full name (such as Angela Merkel). We model 321 PNCs and\ntheir corresponding full names at discourse level, and show that PNCs bear an\nevaluative nature that can be captured through a variety of computational\nmethods. Specifically, we assess through valence information whether a PNC is\nmore positively or negatively evaluative than the person's name, by applying\nand comparing two approaches using (i) valence norms and (ii) pretrained\nlanguage models (PLMs). We further enrich our data with personal,\ndomain-specific, and extra-linguistic information and perform a range of\nregression analyses revealing that factors including compound and modifier\nvalence, domain, and political party membership influence how a PNC is\nevaluated.\n", "date": "2024-04-05", "categories": []}
{"id": "2404.03437", "title": "Knowledge Graph Representation for Political Information Sources", "abstract": "  With the rise of computational social science, many scholars utilize data\nanalysis and natural language processing tools to analyze social media, news\narticles, and other accessible data sources for examining political and social\ndiscourse. Particularly, the study of the emergence of echo-chambers due to the\ndissemination of specific information has become a topic of interest in mixed\nmethods research areas. In this paper, we analyze data collected from two news\nportals, Breitbart News (BN) and New York Times (NYT) to prove the hypothesis\nthat the formation of echo-chambers can be partially explained on the level of\nan individual information consumption rather than a collective topology of\nindividuals' social networks. Our research findings are presented through\nknowledge graphs, utilizing a dataset spanning 11.5 years gathered from BN and\nNYT media portals. We demonstrate that the application of knowledge\nrepresentation techniques to the aforementioned news streams highlights,\ncontrary to common assumptions, shows relative \"internal\" neutrality of both\nsources and polarizing attitude towards a small fraction of entities.\nAdditionally, we argue that such characteristics in information sources lead to\nfundamental disparities in audience worldviews, potentially acting as a\ncatalyst for the formation of echo-chambers.\n", "date": "2024-04-04", "categories": []}
{"id": "2402.18043", "title": "Crisis talk: analysis of the public debate around the energy crisis and\n  cost of living", "abstract": "  A prominent media topic in the UK in the early 2020s is the energy crisis\naffecting the UK and most of Europe. It brings into a single public debate\nissues of energy dependency and sustainability, fair distribution of economic\nburdens and cost of living, as well as climate change, risk, and\nsustainability. In this paper, we investigate the public discourse around the\nenergy crisis and cost of living to identify how these pivotal and\ncontradictory issues are reconciled in this debate and to identify which social\nactors are involved and the role they play. We analyse a document corpus\nretrieved from UK newspapers from January 2014 to March 2023. We apply a\nvariety of natural language processing and data visualisation techniques to\nidentify key topics, novel trends, critical social actors, and the role they\nplay in the debate, along with the sentiment associated with those actors and\ntopics. We combine automated techniques with manual discourse analysis to\nexplore and validate the insights revealed in this study. The findings verify\nthe utility of these techniques by providing a flexible and scalable pipeline\nfor discourse analysis and providing critical insights for cost of living -\nenergy crisis nexus research.\n", "date": "2024-02-28", "categories": []}
{"id": "2403.16206", "title": "Rumor Detection with a novel graph neural network approach", "abstract": "  The wide spread of rumors on social media has caused a negative impact on\npeople's daily life, leading to potential panic, fear, and mental health\nproblems for the public. How to debunk rumors as early as possible remains a\nchallenging problem. Existing studies mainly leverage information propagation\nstructure to detect rumors, while very few works focus on correlation among\nusers that they may coordinate to spread rumors in order to gain large\npopularity. In this paper, we propose a new detection model, that jointly\nlearns both the representations of user correlation and information propagation\nto detect rumors on social media. Specifically, we leverage graph neural\nnetworks to learn the representations of user correlation from a bipartite\ngraph that describes the correlations between users and source tweets, and the\nrepresentations of information propagation with a tree structure. Then we\ncombine the learned representations from these two modules to classify the\nrumors. Since malicious users intend to subvert our model after deployment, we\nfurther develop a greedy attack scheme to analyze the cost of three adversarial\nattacks: graph attack, comment attack, and joint attack. Evaluation results on\ntwo public datasets illustrate that the proposed MODEL outperforms the\nstate-of-the-art rumor detection models. We also demonstrate our method\nperforms well for early rumor detection. Moreover, the proposed detection\nmethod is more robust to adversarial attacks compared to the best existing\nmethod. Importantly, we show that it requires a high cost for attackers to\nsubvert user correlation pattern, demonstrating the importance of considering\nuser correlation for rumor detection.\n", "date": "2024-03-24", "categories": []}
{"id": "2404.01651", "title": "NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but\n  Teaching the Distinction Helps", "abstract": "  The use of words to convey speaker's intent is traditionally distinguished\nfrom the `mention' of words for quoting what someone said, or pointing out\nproperties of a word. Here we show that computationally modeling this\nuse-mention distinction is crucial for dealing with counterspeech online.\nCounterspeech that refutes problematic content often mentions harmful language\nbut is not harmful itself (e.g., calling a vaccine dangerous is not the same as\nexpressing disapproval of someone for calling vaccines dangerous). We show that\neven recent language models fail at distinguishing use from mention, and that\nthis failure propagates to two key downstream tasks: misinformation and hate\nspeech detection, resulting in censorship of counterspeech. We introduce\nprompting mitigations that teach the use-mention distinction, and show they\nreduce these errors. Our work highlights the importance of the use-mention\ndistinction for NLP and CSS and offers ways to address it.\n", "date": "2024-04-02", "categories": []}
{"id": "2404.01715", "title": "EMONA: Event-level Moral Opinions in News Articles", "abstract": "  Most previous research on moral frames has focused on social media short\ntexts, little work has explored moral sentiment within news articles. In news\narticles, authors often express their opinions or political stance through\nmoral judgment towards events, specifically whether the event is right or wrong\naccording to social moral rules. This paper initiates a new task to understand\nmoral opinions towards events in news articles. We have created a new dataset,\nEMONA, and annotated event-level moral opinions in news articles. This dataset\nconsists of 400 news articles containing over 10k sentences and 45k events,\namong which 9,613 events received moral foundation labels. Extracting event\nmorality is a challenging task, as moral judgment towards events can be very\nimplicit. Baseline models were built for event moral identification and\nclassification. In addition, we also conduct extrinsic evaluations to integrate\nevent-level moral opinions into three downstream tasks. The statistical\nanalysis and experiments show that moral opinions of events can serve as\ninformative features for identifying ideological bias or subjective events.\n", "date": "2024-04-02", "categories": []}
{"id": "2404.01722", "title": "Sentence-level Media Bias Analysis with Event Relation Graph", "abstract": "  Media outlets are becoming more partisan and polarized nowadays. In this\npaper, we identify media bias at the sentence level, and pinpoint bias\nsentences that intend to sway readers' opinions. As bias sentences are often\nexpressed in a neutral and factual way, considering broader context outside a\nsentence can help reveal the bias. In particular, we observe that events in a\nbias sentence need to be understood in associations with other events in the\ndocument. Therefore, we propose to construct an event relation graph to\nexplicitly reason about event-event relations for sentence-level bias\nidentification. The designed event relation graph consists of events as nodes\nand four common types of event relations: coreference, temporal, causal, and\nsubevent relations. Then, we incorporate event relation graph for bias\nsentences identification in two steps: an event-aware language model is built\nto inject the events and event relations knowledge into the basic language\nmodel via soft labels; further, a relation-aware graph attention network is\ndesigned to update sentence embedding with events and event relations\ninformation based on hard labels. Experiments on two benchmark datasets\ndemonstrate that our approach with the aid of event relation graph improves\nboth precision and recall of bias sentence identification.\n", "date": "2024-04-02", "categories": []}
{"id": "2403.13362", "title": "Incentivizing News Consumption on Social Media Platforms Using Large\n  Language Models and Realistic Bot Accounts", "abstract": "  Polarization, declining trust, and wavering support for democratic norms are\npressing threats to U.S. democracy. Exposure to verified and quality news may\nlower individual susceptibility to these threats and make citizens more\nresilient to misinformation, populism, and hyperpartisan rhetoric. This project\nexamines how to enhance users' exposure to and engagement with verified and\nideologically balanced news in an ecologically valid setting. We rely on a\nlarge-scale two-week long field experiment (from 1/19/2023 to 2/3/2023) on\n28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users\ntweeting about sports, entertainment, or lifestyle with a contextual reply\ncontaining two hardcoded elements: a URL to the topic-relevant section of\nquality news organization and an encouragement to follow its Twitter account.\nTo further test differential effects by gender of the bots, treated users were\nrandomly assigned to receive responses by bots presented as female or male. We\nexamine whether our over-time intervention enhances the following of news media\norganization, the sharing and the liking of news content and the tweeting about\npolitics and the liking of political content. We find that the treated users\nfollowed more news accounts and the users in the female bot treatment were more\nlikely to like news content than the control. Most of these results, however,\nwere small in magnitude and confined to the already politically interested\nTwitter users, as indicated by their pre-treatment tweeting about politics.\nThese findings have implications for social media and news organizations, and\nalso offer direction for future work on how Large Language Models and other\ncomputational interventions can effectively enhance individual on-platform\nengagement with quality news and public affairs.\n", "date": "2024-03-20", "categories": []}
{"id": "2404.00141", "title": "Classifying Conspiratorial Narratives At Scale: False Alarms and\n  Erroneous Connections", "abstract": "  Online discussions frequently involve conspiracy theories, which can\ncontribute to the proliferation of belief in them. However, not all discussions\nsurrounding conspiracy theories promote them, as some are intended to debunk\nthem. Existing research has relied on simple proxies or focused on a\nconstrained set of signals to identify conspiracy theories, which limits our\nunderstanding of conspiratorial discussions across different topics and online\ncommunities. This work establishes a general scheme for classifying discussions\nrelated to conspiracy theories based on authors' perspectives on the conspiracy\nbelief, which can be expressed explicitly through narrative elements, such as\nthe agent, action, or objective, or implicitly through references to known\ntheories, such as chemtrails or the New World Order. We leverage human-labeled\nground truth to train a BERT-based model for classifying online CTs, which we\nthen compared to the Generative Pre-trained Transformer machine (GPT) for\ndetecting online conspiratorial content. Despite GPT's known strengths in its\nexpressiveness and contextual understanding, our study revealed significant\nflaws in its logical reasoning, while also demonstrating comparable strengths\nfrom our classifiers. We present the first large-scale classification study\nusing posts from the most active conspiracy-related Reddit forums and find that\nonly one-third of the posts are classified as positive. This research sheds\nlight on the potential applications of large language models in tasks demanding\nnuanced contextual comprehension.\n", "date": "2024-03-29", "categories": []}
{"id": "2403.19423", "title": "Echo-chambers and Idea Labs: Communication Styles on Twitter", "abstract": "  This paper investigates the communication styles and structures of Twitter\n(X) communities within the vaccination context. While mainstream research\nprimarily focuses on the echo-chamber phenomenon, wherein certain ideas are\nreinforced and participants are isolated from opposing opinions, this study\nreveals the presence of diverse communication styles across various\ncommunities. In addition to the communities exhibiting echo-chamber behavior,\nthis research uncovers communities with distinct communication patterns. By\nshedding light on the nuanced nature of communication within social networks,\nthis study emphasizes the significance of understanding the diversity of\nperspectives within online communities.\n", "date": "2024-03-28", "categories": []}
{"id": "2208.06620", "title": "Opinion Market Model: Stemming Far-Right Opinion Spread using Positive\n  Interventions", "abstract": "  Online extremism has severe societal consequences, including normalizing hate\nspeech, user radicalization, and increased social divisions. Various mitigation\nstrategies have been explored to address these consequences. One such strategy\nuses positive interventions: controlled signals that add attention to the\nopinion ecosystem to boost certain opinions. To evaluate the effectiveness of\npositive interventions, we introduce the Opinion Market Model (OMM), a two-tier\nonline opinion ecosystem model that considers both inter-opinion interactions\nand the role of positive interventions. The size of the opinion attention\nmarket is modeled in the first tier using the multivariate discrete-time Hawkes\nprocess; in the second tier, opinions cooperate and compete for market share,\ngiven limited attention using the market share attraction model. We demonstrate\nthe convergence of our proposed estimation scheme on a synthetic dataset. Next,\nwe test OMM on two learning tasks, applying to two real-world datasets to\npredict attention market shares and uncover latent relationships between online\nitems. The first dataset comprises Facebook and Twitter discussions containing\nmoderate and far-right opinions about bushfires and climate change. The second\ndataset captures popular VEVO artists' YouTube and Twitter attention volumes.\nOMM outperforms the state-of-the-art predictive models on both datasets and\ncaptures latent cooperation-competition relations. We uncover (1) self- and\ncross-reinforcement between far-right and moderate opinions on the bushfires\nand (2) pairwise artist relations that correlate with real-world interactions\nsuch as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed\nfor positive interventions and show how media coverage modulates the spread of\nfar-right opinions.\n", "date": "2022-08-13", "categories": []}
{"id": "2307.07645", "title": "Othering and low status framing of immigrant cuisines in US restaurant\n  reviews and large language models", "abstract": "  Identifying implicit attitudes toward food can mitigate social prejudice due\nto food's salience as a marker of ethnic identity. Stereotypes about food are\nrepresentational harms that may contribute to racialized discourse and\nnegatively impact economic outcomes for restaurants. Understanding the presence\nof representational harms in online corpora in particular is important, given\nthe increasing use of large language models (LLMs) for text generation and\ntheir tendency to reproduce attitudes in their training data. Through careful\nlinguistic analyses, we evaluate social theories about attitudes toward\nimmigrant cuisine in a large-scale study of framing differences in 2.1M English\nlanguage Yelp reviews. Controlling for factors such as restaurant price and\nneighborhood racial diversity, we find that immigrant cuisines are more likely\nto be othered using socially constructed frames of authenticity (e.g.,\n\"authentic,\" \"traditional\"), and that non-European cuisines (e.g., Indian,\nMexican) in particular are described as more exotic compared to European ones\n(e.g., French). We also find that non-European cuisines are more likely to be\ndescribed as cheap and dirty, even after controlling for price, and even among\nthe most expensive restaurants. Finally, we show that reviews generated by LLMs\nreproduce similar framing tendencies, pointing to the downstream retention of\nthese representational harms. Our results corroborate social theories of\ngastronomic stereotyping, revealing racialized evaluative processes and\nlinguistic strategies through which they manifest.\n", "date": "2023-07-14", "categories": []}
{"id": "2403.15885", "title": "STEntConv: Predicting Disagreement with Stance Detection and a Signed\n  Graph Convolutional Network", "abstract": "  The rise of social media platforms has led to an increase in polarised online\ndiscussions, especially on political and socio-cultural topics such as\nelections and climate change. We propose a simple and novel unsupervised method\nto predict whether the authors of two posts agree or disagree, leveraging user\nstances about named entities obtained from their posts. We present STEntConv, a\nmodel which builds a graph of users and named entities weighted by stance and\ntrains a Signed Graph Convolutional Network (SGCN) to detect disagreement\nbetween comment and reply posts. We run experiments and ablation studies and\nshow that including this information improves disagreement detection\nperformance on a dataset of Reddit posts for a range of controversial subreddit\ntopics, without the need for platform-specific features or user history.\n", "date": "2024-03-23", "categories": []}
{"id": "2403.17158", "title": "Reflecting the Male Gaze: Quantifying Female Objectification in 19th and\n  20th Century Novels", "abstract": "  Inspired by the concept of the male gaze (Mulvey, 1975) in literature and\nmedia studies, this paper proposes a framework for analyzing gender bias in\nterms of female objectification: the extent to which a text portrays female\nindividuals as objects of visual pleasure. Our framework measures female\nobjectification along two axes. First, we compute an agency bias score that\nindicates whether male entities are more likely to appear in the text as\ngrammatical agents than female entities. Next, by analyzing the word embedding\nspace induced by a text (Caliskan et al., 2017), we compute an appearance bias\nscore that indicates whether female entities are more closely associated with\nappearance-related words than male entities. Applying our framework to 19th and\n20th century novels reveals evidence of female objectification in literature:\nwe find that novels written from a male perspective systematically objectify\nfemale characters, while novels written from a female perspective do not\nexhibit statistically significant objectification of any gender.\n", "date": "2024-03-25", "categories": []}
{"id": "2304.04806", "title": "Examining Temporalities on Stance Detection towards COVID-19 Vaccination", "abstract": "  Previous studies have highlighted the importance of vaccination as an\neffective strategy to control the transmission of the COVID-19 virus. It is\ncrucial for policymakers to have a comprehensive understanding of the public's\nstance towards vaccination on a large scale. However, attitudes towards\nCOVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved\nover time on social media. Thus, it is necessary to account for possible\ntemporal shifts when analysing these stances. This study aims to examine the\nimpact of temporal concept drift on stance detection towards COVID-19\nvaccination on Twitter. To this end, we evaluate a range of transformer-based\nmodels using chronological (splitting the training, validation, and test sets\nin order of time) and random splits (randomly splitting these three sets) of\nsocial media data. Our findings reveal significant discrepancies in model\nperformance between random and chronological splits in several existing\nCOVID-19-related datasets; specifically, chronological splits significantly\nreduce the accuracy of stance classification. Therefore, real-world stance\ndetection approaches need to be further refined to incorporate temporal factors\nas a key consideration.\n", "date": "2023-04-10", "categories": []}
{"id": "2309.11576", "title": "Examining the Limitations of Computational Rumor Detection Models\n  Trained on Static Datasets", "abstract": "  A crucial aspect of a rumor detection model is its ability to generalize,\nparticularly its ability to detect emerging, previously unknown rumors. Past\nresearch has indicated that content-based (i.e., using solely source posts as\ninput) rumor detection models tend to perform less effectively on unseen\nrumors. At the same time, the potential of context-based models remains largely\nuntapped. The main contribution of this paper is in the in-depth evaluation of\nthe performance gap between content and context-based models specifically on\ndetecting new, unseen rumors. Our empirical findings demonstrate that\ncontext-based models are still overly dependent on the information derived from\nthe rumors' source post and tend to overlook the significant role that\ncontextual information can play. We also study the effect of data split\nstrategies on classifier performance. Based on our experimental results, the\npaper also offers practical suggestions on how to minimize the effects of\ntemporal concept drift in static datasets during the training of rumor\ndetection methods.\n", "date": "2023-09-20", "categories": []}
{"id": "2403.16668", "title": "Who is bragging more online? A large scale analysis of bragging in\n  social media", "abstract": "  Bragging is the act of uttering statements that are likely to be positively\nviewed by others and it is extensively employed in human communication with the\naim to build a positive self-image of oneself. Social media is a natural\nplatform for users to employ bragging in order to gain admiration, respect,\nattention and followers from their audiences. Yet, little is known about the\nscale of bragging online and its characteristics. This paper employs\ncomputational sociolinguistics methods to conduct the first large scale study\nof bragging behavior on Twitter (U.S.) by focusing on its overall prevalence,\ntemporal dynamics and impact of demographic factors. Our study shows that the\nprevalence of bragging decreases over time within the same population of users.\nIn addition, younger, more educated and popular users in the U.S. are more\nlikely to brag. Finally, we conduct an extensive linguistics analysis to unveil\nspecific bragging themes associated with different user traits.\n", "date": "2024-03-25", "categories": []}
{"id": "2402.00705", "title": "Combining the Strengths of Dutch Survey and Register Data in a Data\n  Challenge to Predict Fertility (PreFer)", "abstract": "  The social sciences have produced an impressive body of research on\ndeterminants of fertility outcomes, or whether and when people have children.\nHowever, the strength of these determinants and underlying theories are rarely\nevaluated on their predictive ability on new data. This prevents us from\nsystematically comparing studies, hindering the evaluation and accumulation of\nknowledge. In this paper, we present two datasets which can be used to study\nthe predictability of fertility outcomes in the Netherlands. One dataset is\nbased on the LISS panel, a longitudinal survey which includes thousands of\nvariables on a wide range of topics, including individual preferences and\nvalues. The other is based on the Dutch register data which lacks attitudinal\ndata but includes detailed information about the life courses of millions of\nDutch residents. We provide information about the datasets and the samples, and\ndescribe the fertility outcome of interest. We also introduce the fertility\nprediction data challenge PreFer which is based on these datasets and will\nstart in Spring 2024. We outline the ways in which measuring the predictability\nof fertility outcomes using these datasets and combining their strengths in the\ndata challenge can advance our understanding of fertility behaviour and\ncomputational social science. We further provide details for participants on\nhow to take part in the data challenge.\n", "date": "2024-02-01", "categories": []}
{"id": "2310.05976", "title": "An evolutionary model of personality traits related to cooperative\n  behavior using a large language model", "abstract": "  This paper aims to shed light on the evolutionary dynamics of diverse and\nsocial populations by introducing the rich expressiveness of generative models\ninto the trait expression of social agent-based evolutionary models.\nSpecifically, we focus on the evolution of personality traits in the context of\na game-theoretic relationship as a situation in which inter-individual\ninterests exert strong selection pressures. We construct an agent model in\nwhich linguistic descriptions of personality traits related to cooperative\nbehavior are used as genes. The deterministic strategies extracted from Large\nLanguage Model (LLM) that make behavioral decisions based on these personality\ntraits are used as behavioral traits. The population is evolved according to\nselection based on average payoff and mutation of genes by asking LLM to\nslightly modify the parent gene toward cooperative or selfish. Through\npreliminary experiments and analyses, we clarify that such a model can indeed\nexhibit the evolution of cooperative behavior based on the diverse and\nhigher-order representation of personality traits. We also observed the\nrepeated intrusion of cooperative and selfish personality traits through\nchanges in the expression of personality traits, and found that the emerging\nwords in the evolved gene well reflected the behavioral tendency of its\npersonality in terms of their semantics.\n", "date": "2023-10-03", "categories": []}
{"id": "2301.08832", "title": "Same Words, Different Meanings: Semantic Polarization in Broadcast Media\n  Language Forecasts Polarization on Social Media Discourse", "abstract": "  With the growth of online news over the past decade, empirical studies on\npolitical discourse and news consumption have focused on the phenomenon of\nfilter bubbles and echo chambers. Yet recently, scholars have revealed limited\nevidence around the impact of such phenomenon, leading some to argue that\npartisan segregation across news audiences cannot be fully explained by online\nnews consumption alone and that the role of traditional legacy media may be as\nsalient in polarizing public discourse around current events. In this work, we\nexpand the scope of analysis to include both online and more traditional media\nby investigating the relationship between broadcast news media language and\nsocial media discourse. By analyzing a decade's worth of closed captions (2\nmillion speaker turns) from CNN and Fox News along with topically corresponding\ndiscourse from Twitter, we provide a novel framework for measuring semantic\npolarization between America's two major broadcast networks to demonstrate how\nsemantic polarization between these outlets has evolved (Study 1), peaked\n(Study 2) and influenced partisan discussions on Twitter (Study 3) across the\nlast decade. Our results demonstrate a sharp increase in polarization in how\ntopically important keywords are discussed between the two channels, especially\nafter 2016, with overall highest peaks occurring in 2020. The two stations\ndiscuss identical topics in drastically distinct contexts in 2020, to the\nextent that there is barely any linguistic overlap in how identical keywords\nare contextually discussed. Further, we demonstrate at scale, how such partisan\ndivision in broadcast media language significantly shapes semantic polarity\ntrends on Twitter (and vice-versa), empirically linking for the first time, how\nonline discussions are influenced by televised media.\n", "date": "2023-01-20", "categories": []}
{"id": "2305.14534", "title": "Detecting Propaganda Techniques in Code-Switched Social Media Text", "abstract": "  Propaganda is a form of communication intended to influence the opinions and\nthe mindset of the public to promote a particular agenda. With the rise of\nsocial media, propaganda has spread rapidly, leading to the need for automatic\npropaganda detection systems. Most work on propaganda detection has focused on\nhigh-resource languages, such as English, and little effort has been made to\ndetect propaganda for low-resource languages. Yet, it is common to find a mix\nof multiple languages in social media communication, a phenomenon known as\ncode-switching. Code-switching combines different languages within the same\ntext, which poses a challenge for automatic systems. With this in mind, here we\npropose the novel task of detecting propaganda techniques in code-switched\ntext. To support this task, we create a corpus of 1,030 texts code-switching\nbetween English and Roman Urdu, annotated with 20 propaganda techniques, which\nwe make publicly available. We perform a number of experiments contrasting\ndifferent experimental setups, and we find that it is important to model the\nmultilinguality directly (rather than using translation) as well as to use the\nright fine-tuning strategy. The code and the dataset are publicly available at\nhttps://github.com/mbzuai-nlp/propaganda-codeswitched-text\n", "date": "2023-05-23", "categories": []}
{"id": "2403.11108", "title": "HarmPot: An Annotation Framework for Evaluating Offline Harm Potential\n  of Social Media Text", "abstract": "  In this paper, we discuss the development of an annotation schema to build\ndatasets for evaluating the offline harm potential of social media texts. We\ndefine \"harm potential\" as the potential for an online public post to cause\nreal-world physical harm (i.e., violence). Understanding that real-world\nviolence is often spurred by a web of triggers, often combining several online\ntactics and pre-existing intersectional fissures in the social milieu, to\nresult in targeted physical violence, we do not focus on any single divisive\naspect (i.e., caste, gender, religion, or other identities of the victim and\nperpetrators) nor do we focus on just hate speech or mis/dis-information.\nRather, our understanding of the intersectional causes of such triggers focuses\nour attempt at measuring the harm potential of online content, irrespective of\nwhether it is hateful or not. In this paper, we discuss the development of a\nframework/annotation schema that allows annotating the data with different\naspects of the text including its socio-political grounding and intent of the\nspeaker (as expressed through mood and modality) that together contribute to it\nbeing a trigger for offline harm. We also give a comparative analysis and\nmapping of our framework with some of the existing frameworks.\n", "date": "2024-03-17", "categories": []}
{"id": "2403.08882", "title": "Cultural evolution in populations of Large Language Models", "abstract": "  Research in cultural evolution aims at providing causal explanations for the\nchange of culture over time. Over the past decades, this field has generated an\nimportant body of knowledge, using experimental, historical, and computational\nmethods. While computational models have been very successful at generating\ntestable hypotheses about the effects of several factors, such as population\nstructure or transmission biases, some phenomena have so far been more complex\nto capture using agent-based and formal models. This is in particular the case\nfor the effect of the transformations of social information induced by evolved\ncognitive mechanisms. We here propose that leveraging the capacity of Large\nLanguage Models (LLMs) to mimic human behavior may be fruitful to address this\ngap. On top of being an useful approximation of human cultural dynamics,\nmulti-agents models featuring generative agents are also important to study for\ntheir own sake. Indeed, as artificial agents are bound to participate more and\nmore to the evolution of culture, it is crucial to better understand the\ndynamics of machine-generated cultural evolution. We here present a framework\nfor simulating cultural evolution in populations of LLMs, allowing the\nmanipulation of variables known to be important in cultural evolution, such as\nnetwork structure, personality, and the way social information is aggregated\nand transformed. The software we developed for conducting these simulations is\nopen-source and features an intuitive user-interface, which we hope will help\nto build bridges between the fields of cultural evolution and generative\nartificial intelligence.\n", "date": "2024-03-13", "categories": []}
{"id": "2403.07090", "title": "Time Series Analysis of Key Societal Events as Reflected in Complex\n  Social Media Data Streams", "abstract": "  Social media platforms hold valuable insights, yet extracting essential\ninformation can be challenging. Traditional top-down approaches often struggle\nto capture critical signals in rapidly changing events. As global events evolve\nswiftly, social media narratives, including instances of disinformation, become\nsignificant sources of insights. To address the need for an inductive strategy,\nwe explore a niche social media platform GAB and an established messaging\nservice Telegram, to develop methodologies applicable on a broader scale. This\nstudy investigates narrative evolution on these platforms using quantitative\ncorpus-based discourse analysis techniques. Our approach is a novel mode to\nstudy multiple social media domains to distil key information which may be\nobscured otherwise, allowing for useful and actionable insights. The paper\ndetails the technical and methodological aspects of gathering and preprocessing\nGAB and Telegram data for a keyness (Log Ratio) metric analysis, identifying\ncrucial nouns and verbs for deeper exploration. Empirically, this approach is\napplied to a case study of a well defined event that had global impact: the\n2023 Wagner mutiny. The main findings are: (1) the time line can be\ndeconstructed to provide useful data features allowing for improved\ninterpretation; (2) a methodology is applied which provides a basis for\ngeneralization. The key contribution is an approach, that in some cases,\nprovides the ability to capture the dynamic narrative shifts over time with\nelevated confidence. The approach can augment near-real-time assessment of key\nsocial movements, allowing for informed governance choices. This research is\nimportant because it lays out a useful methodology for time series relevant\ninfo-culling, which can enable proactive modes for positive social engagement.\n", "date": "2024-03-11", "categories": []}
{"id": "2310.00349", "title": "Visual Political Communication in a Polarized Society: A Longitudinal\n  Study of Brazilian Presidential Elections on Instagram", "abstract": "  In today's digital age, images have emerged as powerful tools for politicians\nto engage with their voters on social media platforms. Visual content possesses\na unique emotional appeal that often leads to increased user engagement.\nHowever, research on visual communication remains relatively limited,\nparticularly in the Global South. This study aims to bridge this gap by\nemploying a combination of computational methods and qualitative approach to\ninvestigate the visual communication strategies employed in a dataset of 11,263\nInstagram posts by 19 Brazilian presidential candidates in 2018 and 2022\nnational elections. Through two studies, we observed consistent patterns across\nthese candidates on their use of visual political communication. Notably, we\nidentify a prevalence of celebratory and positively toned images. They also\nexhibit a strong sense of personalization, portraying candidates connected with\ntheir voters on a more emotional level. Our research also uncovers unique\ncontextual nuances specific to the Brazilian political landscape. We note a\nsubstantial presence of screenshots from news websites and other social media\nplatforms. Furthermore, text-edited images with portrayals emerge as a\nprominent feature. In light of these results, we engage in a discussion\nregarding the implications for the broader field of visual political\ncommunication. This article serves as a testament to the pivotal role that\nInstagram has played in shaping the narrative of two fiercely polarized\nBrazilian elections, casting a revealing light on the ever-evolving dynamics of\nvisual political communication in the digital age. Finally, we propose avenues\nfor future research in the realm of visual political communication.\n", "date": "2023-09-30", "categories": []}
{"id": "2303.16281", "title": "A \"Perspectival\" Mirror of the Elephant: Investigating Language Bias on\n  Google, ChatGPT, YouTube, and Wikipedia", "abstract": "  Contrary to Google Search's mission of delivering information from \"many\nangles so you can form your own understanding of the world,\" we find that\nGoogle and its most prominent returned results - Wikipedia and YouTube - simply\nreflect a narrow set of culturally dominant views tied to the search language\nfor complex topics like \"Buddhism,\" \"Liberalism,\" \"colonization,\" \"Iran\" and\n\"America.\" Simply stated, they present, to varying degrees, distinct\ninformation across the same search in different languages, a phenomenon we call\nlanguage bias. This paper presents evidence and analysis of language bias and\ndiscusses its larger social implications. We find that our online searches and\nemerging tools like ChatGPT turn us into the proverbial blind person touching a\nsmall portion of an elephant, ignorant of the existence of other cultural\nperspectives. Language bias sets a strong yet invisible cultural barrier\nonline, where each language group thinks they can see other groups through\nsearches, but in fact, what they see is their own reflection.\n", "date": "2023-03-28", "categories": []}
{"id": "2403.04009", "title": "Media Bias Matters: Understanding the Impact of Politically Biased News\n  on Vaccine Attitudes in Social Media", "abstract": "  News media has been utilized as a political tool to stray from facts,\npresenting biased claims without evidence. Amid the COVID-19 pandemic,\npolitically biased news (PBN) has significantly undermined public trust in\nvaccines, despite strong medical evidence supporting their efficacy. In this\npaper, we analyze: (i) how inherent vaccine stances subtly influence\nindividuals' selection of news sources and participation in social media\ndiscussions; and (ii) the impact of exposure to PBN on users' attitudes toward\nvaccines. In doing so, we first curate a comprehensive dataset that connects\nPBN with related social media discourse. Utilizing advanced deep learning and\ncausal inference techniques, we reveal distinct user behaviors between social\nmedia groups with various vaccine stances. Moreover, we observe that\nindividuals with moderate stances, particularly the vaccine-hesitant majority,\nare more vulnerable to the influence of PBN compared to those with extreme\nviews. Our findings provide critical insights to foster this line of research.\n", "date": "2024-03-06", "categories": []}
{"id": "2403.04511", "title": "Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video\n  Recommendation", "abstract": "  Filter bubbles have been studied extensively within the context of online\ncontent platforms due to their potential to cause undesirable outcomes such as\nuser dissatisfaction or polarization. With the rise of short-video platforms,\nthe filter bubble has been given extra attention because these platforms rely\non an unprecedented use of the recommender system to provide relevant content.\nIn our work, we investigate the deep filter bubble, which refers to the user\nbeing exposed to narrow content within their broad interests. We accomplish\nthis using one-year interaction data from a top short-video platform in China,\nwhich includes hierarchical data with three levels of categories for each\nvideo. We formalize our definition of a \"deep\" filter bubble within this\ncontext, and then explore various correlations within the data: first\nunderstanding the evolution of the deep filter bubble over time, and later\nrevealing some of the factors that give rise to this phenomenon, such as\nspecific categories, user demographics, and feedback type. We observe that\nwhile the overall proportion of users in a filter bubble remains largely\nconstant over time, the depth composition of their filter bubble changes. In\naddition, we find that some demographic groups that have a higher likelihood of\nseeing narrower content and implicit feedback signals can lead to less bubble\nformation. Finally, we propose some ways in which recommender systems can be\ndesigned to reduce the risk of a user getting caught in a bubble.\n", "date": "2024-03-07", "categories": []}
{"id": "2403.04671", "title": "Greater than the sum of its parts: The role of minority and majority\n  status in collaborative problem-solving communication", "abstract": "  Collaborative problem-solving (CPS) is a vital skill used both in the\nworkplace and in educational environments. CPS is useful in tackling\nincreasingly complex global, economic, and political issues and is considered a\ncentral 21st century skill. The increasingly connected global community\npresents a fruitful opportunity for creative and collaborative problem-solving\ninteractions and solutions that involve diverse perspectives. Unfortunately,\nwomen and underrepresented minorities (URMs) often face obstacles during\ncollaborative interactions that hinder their key participation in these\nproblem-solving conversations. Here, we explored the communication patterns of\nminority and non-minority individuals working together in a CPS task. Group\nCommunication Analysis (GCA), a temporally-sensitive computational linguistic\ntool, was used to examine how URM status impacts individuals' sociocognitive\nlinguistic patterns. Results show differences across racial/ethnic groups in\nkey sociocognitive features that indicate fruitful collaborative interactions.\nWe also investigated how the groups' racial/ethnic composition impacts both\nindividual and group communication patterns. In general, individuals in more\ndemographically diverse groups displayed more productive communication\nbehaviors than individuals who were in majority-dominated groups. We discuss\nthe implications of individual and group diversity on communication patterns\nthat emerge during CPS and how these patterns can impact collaborative\noutcomes.\n", "date": "2024-03-07", "categories": []}
{"id": "2403.02474", "title": "The Emotion Dynamics of Literary Novels", "abstract": "  Stories are rich in the emotions they exhibit in their narratives and evoke\nin the readers. The emotional journeys of the various characters within a story\nare central to their appeal. Computational analysis of the emotions of novels,\nhowever, has rarely examined the variation in the emotional trajectories of the\ndifferent characters within them, instead considering the entire novel to\nrepresent a single story arc. In this work, we use character dialogue to\ndistinguish between the emotion arcs of the narration and the various\ncharacters. We analyze the emotion arcs of the various characters in a dataset\nof English literary novels using the framework of Utterance Emotion Dynamics.\nOur findings show that the narration and the dialogue largely express disparate\nemotions through the course of a novel, and that the commonalities or\ndifferences in the emotional arcs of stories are more accurately captured by\nthose associated with individual characters.\n", "date": "2024-03-04", "categories": []}
{"id": "2403.00994", "title": "Leveraging Prompt-Based Large Language Models: Predicting Pandemic\n  Health Decisions and Outcomes Through Social Media Language", "abstract": "  We introduce a multi-step reasoning framework using prompt-based LLMs to\nexamine the relationship between social media language patterns and trends in\nnational health outcomes. Grounded in fuzzy-trace theory, which emphasizes the\nimportance of gists of causal coherence in effective health communication, we\nintroduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework,\nto identify gists at-scale. Using RBIC, we systematically extract gists from\nsubreddit discussions opposing COVID-19 health measures (Study 1). We then\ntrack how these gists evolve across key events (Study 2) and assess their\ninfluence on online engagement (Study 3). Finally, we investigate how the\nvolume of gists is associated with national health trends like vaccine uptake\nand hospitalizations (Study 4). Our work is the first to empirically link\nsocial media linguistic patterns to real-world public health trends,\nhighlighting the potential of prompt-based LLMs in identifying critical online\ndiscussion patterns that can form the basis of public health communication\nstrategies.\n", "date": "2024-03-01", "categories": []}
{"id": "2402.18222", "title": "HearHere: Mitigating Echo Chambers in News Consumption through an\n  AI-based Web System", "abstract": "  Considerable efforts are currently underway to mitigate the negative impacts\nof echo chambers, such as increased susceptibility to fake news and resistance\ntowards accepting scientific evidence. Prior research has presented the\ndevelopment of computer systems that support the consumption of news\ninformation from diverse political perspectives to mitigate the echo chamber\neffect. However, existing studies still lack the ability to effectively support\nthe key processes of news information consumption and quantitatively identify a\npolitical stance towards the information. In this paper, we present HearHere,\nan AI-based web system designed to help users accommodate information and\nopinions from diverse perspectives. HearHere facilitates the key processes of\nnews information consumption through two visualizations. Visualization 1\nprovides political news with quantitative political stance information, derived\nfrom our graph-based political classification model, and users can experience\ndiverse perspectives (Hear). Visualization 2 allows users to express their\nopinions on specific political issues in a comment form and observe the\nposition of their own opinions relative to pro-liberal and pro-conservative\ncomments presented on a map interface (Here). Through a user study with 94\nparticipants, we demonstrate the feasibility of HearHere in supporting the\nconsumption of information from various perspectives. Our findings highlight\nthe importance of providing political stance information and quantifying users'\npolitical status as a means to mitigate political polarization. In addition, we\npropose design implications for system development, including the consideration\nof demographics such as political interest and providing users with\ninitiatives.\n", "date": "2024-02-28", "categories": []}
{"id": "2305.03514", "title": "Can Large Language Models Transform Computational Social Science?", "abstract": "  Large Language Models (LLMs) are capable of successfully performing many\nlanguage processing tasks zero-shot (without training data). If zero-shot LLMs\ncan also reliably classify and explain social phenomena like persuasiveness and\npolitical ideology, then LLMs could augment the Computational Social Science\n(CSS) pipeline in important ways. This work provides a road map for using LLMs\nas CSS tools. Towards this end, we contribute a set of prompting best practices\nand an extensive evaluation pipeline to measure the zero-shot performance of 13\nlanguage models on 25 representative English CSS benchmarks. On taxonomic\nlabeling tasks (classification), LLMs fail to outperform the best fine-tuned\nmodels but still achieve fair levels of agreement with humans. On free-form\ncoding tasks (generation), LLMs produce explanations that often exceed the\nquality of crowdworkers' gold references. We conclude that the performance of\ntoday's LLMs can augment the CSS research pipeline in two ways: (1) serving as\nzero-shot data annotators on human annotation teams, and (2) bootstrapping\nchallenging creative generation tasks (e.g., explaining the underlying\nattributes of a text). In summary, LLMs are posed to meaningfully participate\nin social science analysis in partnership with humans.\n", "date": "2023-04-12", "categories": []}
{"id": "2402.03780", "title": "Exposing propaganda: an analysis of stylistic cues comparing human\n  annotations and machine classification", "abstract": "  This paper investigates the language of propaganda and its stylistic\nfeatures. It presents the PPN dataset, standing for Propagandist Pseudo-News, a\nmultisource, multilingual, multimodal dataset composed of news articles\nextracted from websites identified as propaganda sources by expert agencies. A\nlimited sample from this set was randomly mixed with papers from the regular\nFrench press, and their URL masked, to conduct an annotation-experiment by\nhumans, using 11 distinct labels. The results show that human annotators were\nable to reliably discriminate between the two types of press across each of the\nlabels. We propose different NLP techniques to identify the cues used by the\nannotators, and to compare them with machine classification. They include the\nanalyzer VAGO to measure discourse vagueness and subjectivity, a TF-IDF to\nserve as a baseline, and four different classifiers: two RoBERTa-based models,\nCATS using syntax, and one XGBoost combining syntactic and semantic features.\n", "date": "2024-02-06", "categories": []}
{"id": "2402.14187", "title": "From Adoption to Adaption: Tracing the Diffusion of New Emojis on\n  Twitter", "abstract": "  In the rapidly evolving landscape of social media, the introduction of new\nemojis in Unicode release versions presents a structured opportunity to explore\ndigital language evolution. Analyzing a large dataset of sampled English\ntweets, we examine how newly released emojis gain traction and evolve in\nmeaning. We find that community size of early adopters and emoji semantics are\ncrucial in determining their popularity. Certain emojis experienced notable\nshifts in the meanings and sentiment associations during the diffusion process.\nAdditionally, we propose a novel framework utilizing language models to extract\nwords and pre-existing emojis with semantically similar contexts, which\nenhances interpretation of new emojis. The framework demonstrates its\neffectiveness in improving sentiment classification performance by substituting\nunknown new emojis with familiar ones. This study offers a new perspective in\nunderstanding how new language units are adopted, adapted, and integrated into\nthe fabric of online communication.\n", "date": "2024-02-22", "categories": []}
{"id": "2310.02529", "title": "MIDDAG: Where Does Our News Go? Investigating Information Diffusion via\n  Community-Level Information Pathways", "abstract": "  We present MIDDAG, an intuitive, interactive system that visualizes the\ninformation propagation paths on social media triggered by COVID-19-related\nnews articles accompanied by comprehensive insights, including user/community\nsusceptibility level, as well as events and popular opinions raised by the\ncrowd while propagating the information. Besides discovering information flow\npatterns among users, we construct communities among users and develop the\npropagation forecasting capability, enabling tracing and understanding of how\ninformation is disseminated at a higher level.\n", "date": "2023-10-04", "categories": []}
{"id": "2402.13800", "title": "The Geography of Information Diffusion in Online Discourse on Europe and\n  Migration", "abstract": "  The online diffusion of information related to Europe and migration has been\nlittle investigated from an external point of view. However, this is a very\nrelevant topic, especially if users have had no direct contact with Europe and\nits perception depends solely on information retrieved online. In this work we\nanalyse the information circulating online about Europe and migration after\nretrieving a large amount of data from social media (Twitter), to gain new\ninsights into topics, magnitude, and dynamics of their diffusion. We combine\nretweets and hashtags network analysis with geolocation of users, linking thus\ndata to geography and allowing analysis from an \"outside Europe\" perspective,\nwith a special focus on Africa. We also introduce a novel approach based on\ncross-lingual quotes, i.e. when content in a language is commented and\nretweeted in another language, assuming these interactions are a proxy for\nconnections between very distant communities. Results show how the majority of\nonline discussions occurs at a national level, especially when discussing\nmigration. Language (English) is pivotal for information to become\ntransnational and reach far. Transnational information flow is strongly\nunbalanced, with content mainly produced in Europe and amplified outside.\nConversely Europe-based accounts tend to be self-referential when they discuss\nmigration-related topics. Football is the most exported topic from Europe\nworldwide. Moreover, important nodes in the communities discussing\nmigration-related topics include accounts of official institutions and\ninternational agencies, together with journalists, news, commentators and\nactivists.\n", "date": "2024-02-21", "categories": []}
{"id": "2310.00267", "title": "The Physics of Preference: Unravelling Imprecision of Human Preferences\n  through Magnetisation Dynamics", "abstract": "  Paradoxical decision-making behaviours such as preference reversal often\narise from imprecise or noisy human preferences. Harnessing the physical\nprinciple of magnetisation reversal in ferromagnetic nanostructures, we\ndeveloped a model that closely reflects human decision-making dynamics. Tested\nagainst a spectrum of psychological data, our model adeptly captures the\ncomplexities inherent in individual choices. This blend of physics and\npsychology paves the way for fresh perspectives on understanding the\nimprecision of human decision-making processes, extending the reach of the\ncurrent classical and quantum physical models of human behaviour and\ndecision-making.\n", "date": "2023-09-30", "categories": []}
{"id": "2402.10938", "title": "News Source Credibility Assessment: A Reddit Case Study", "abstract": "  In the era of social media platforms, identifying the credibility of online\ncontent is crucial to combat misinformation. We present the CREDiBERT\n(CREDibility assessment using Bi-directional Encoder Representations from\nTransformers), a source credibility assessment model fine-tuned for Reddit\nsubmissions focusing on political discourse as the main contribution. We adopt\na semi-supervised training approach for CREDiBERT, leveraging Reddit's\ncommunity-based structure. By encoding submission content using CREDiBERT and\nintegrating it into a Siamese neural network, we significantly improve the\nbinary classification of submission credibility, achieving a 9% increase in F1\nscore compared to existing methods. Additionally, we introduce a new version of\nthe post-to-post network in Reddit that efficiently encodes user interactions\nto enhance the binary classification task by nearly 8% in F1 score. Finally, we\nemploy CREDiBERT to evaluate the susceptibility of subreddits with respect to\ndifferent topics.\n", "date": "2024-02-07", "categories": []}
{"id": "2402.12212", "title": "Polarization of Autonomous Generative AI Agents Under Echo Chambers", "abstract": "  Online social networks often create echo chambers where people only hear\nopinions reinforcing their beliefs. An echo chamber often generates\npolarization, leading to conflicts caused by people with radical opinions, such\nas the January 6, 2021, attack on the US Capitol. The echo chamber has been\nviewed as a human-specific problem, but this implicit assumption is becoming\nless reasonable as large language models, such as ChatGPT, acquire social\nabilities. In response to this situation, we investigated the potential for\npolarization to occur among a group of autonomous AI agents based on generative\nlanguage models in an echo chamber environment. We had AI agents discuss\nspecific topics and analyzed how the group's opinions changed as the discussion\nprogressed. As a result, we found that the group of agents based on ChatGPT\ntended to become polarized in echo chamber environments. The analysis of\nopinion transitions shows that this result is caused by ChatGPT's high prompt\nunderstanding ability to update its opinion by considering its own and\nsurrounding agents' opinions. We conducted additional experiments to\ninvestigate under what specific conditions AI agents tended to polarize. As a\nresult, we identified factors that strongly influence polarization, such as the\nagent's persona. These factors should be monitored to prevent the polarization\nof AI agents.\n", "date": "2024-02-19", "categories": []}
{"id": "2402.10243", "title": "Understanding team collapse via probabilistic graphical models", "abstract": "  In this work, we develop a graphical model to capture team dynamics. We\nanalyze the model and show how to learn its parameters from data. Using our\nmodel we study the phenomenon of team collapse from a computational\nperspective. We use simulations and real-world experiments to find the main\ncauses of team collapse. We also provide the principles of building resilient\nteams, i.e., teams that avoid collapsing. Finally, we use our model to analyze\nthe structure of NBA teams and dive deeper into games of interest.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.10436", "title": "I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large\n  Language Models", "abstract": "  We explored cultural biases-individualism vs. collectivism-in ChatGPT across\nthree Western languages (i.e., English, German, and French) and three Eastern\nlanguages (i.e., Chinese, Japanese, and Korean). When ChatGPT adopted an\nindividualistic persona in Western languages, its collectivism scores (i.e.,\nout-group values) exhibited a more negative trend, surpassing their positive\norientation towards individualism (i.e., in-group values). Conversely, when a\ncollectivistic persona was assigned to ChatGPT in Eastern languages, a similar\npattern emerged with more negative responses toward individualism (i.e.,\nout-group values) as compared to collectivism (i.e., in-group values). The\nresults indicate that when imbued with a particular social identity, ChatGPT\ndiscerns in-group and out-group, embracing in-group values while eschewing\nout-group values. Notably, the negativity towards the out-group, from which\nprejudices and discrimination arise, exceeded the positivity towards the\nin-group. The experiment was replicated in the political domain, and the\nresults remained consistent. Furthermore, this replication unveiled an\nintrinsic Democratic bias in Large Language Models (LLMs), aligning with\nearlier findings and providing integral insights into mitigating such bias\nthrough prompt engineering. Extensive robustness checks were performed using\nvarying hyperparameter and persona setup methods, with or without social\nidentity labels, across other popular language models.\n", "date": "2024-02-16", "categories": []}
{"id": "2307.13912", "title": "Embedding Democratic Values into Social Media AIs via Societal Objective\n  Functions", "abstract": "  Can we design artificial intelligence (AI) systems that rank our social media\nfeeds to consider democratic values such as mitigating partisan animosity as\npart of their objective functions? We introduce a method for translating\nestablished, vetted social scientific constructs into AI objective functions,\nwhich we term societal objective functions, and demonstrate the method with\napplication to the political science construct of anti-democratic attitudes.\nTraditionally, we have lacked observable outcomes to use to train such models,\nhowever, the social sciences have developed survey instruments and qualitative\ncodebooks for these constructs, and their precision facilitates translation\ninto detailed prompts for large language models. We apply this method to create\na democratic attitude model that estimates the extent to which a social media\npost promotes anti-democratic attitudes, and test this democratic attitude\nmodel across three studies. In Study 1, we first test the attitudinal and\nbehavioral effectiveness of the intervention among US partisans (N=1,380) by\nmanually annotating (alpha=.895) social media posts with anti-democratic\nattitude scores and testing several feed ranking conditions based on these\nscores. Removal (d=.20) and downranking feeds (d=.25) reduced participants'\npartisan animosity without compromising their experience and engagement. In\nStudy 2, we scale up the manual labels by creating the democratic attitude\nmodel, finding strong agreement with manual labels (rho=.75). Finally, in Study\n3, we replicate Study 1 using the democratic attitude model instead of manual\nlabels to test its attitudinal and behavioral impact (N=558), and again find\nthat the feed downranking using the societal objective function reduced\npartisan animosity (d=.25). This method presents a novel strategy to draw on\nsocial science theory and methods to mitigate societal harms in social media\nAIs.\n", "date": "2023-07-26", "categories": []}
{"id": "2306.12951", "title": "Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and\n  Occupations", "abstract": "  ChatGPT sets a new record with the fastest-growing user base, as a chatbot\npowered by a large language model (LLM). While it demonstrates state-of-the-art\ncapabilities in a variety of language-generation tasks, it also raises\nwidespread public concerns regarding its societal impact. In this paper, we\ninvestigated public attitudes towards ChatGPT by applying natural language\nprocessing techniques such as sentiment analysis and topic modeling to Twitter\ndata from December 5, 2022 to June 10, 2023. Our sentiment analysis result\nindicates that the overall sentiment was largely neutral to positive, and\nnegative sentiments were decreasing over time. Our topic model reveals that the\nmost popular topics discussed were Education, Bard, Search Engines, OpenAI,\nMarketing, and Cybersecurity, but the ranking varies by month. We also analyzed\nthe occupations of Twitter users and found that those with occupations in arts\nand entertainment tweeted aboutChatGPT most frequently. Additionally, people\ntended to tweet about topics relevant to their occupation. For instance,\nCybersecurity is the most discussed topic among those with occupations related\nto computer and math, and Education is the most discussed topic among those in\nacademic and research. Overall, our exploratory study provides insights into\nthe public perception of ChatGPT, which could be valuable to both the general\npublic and developers of this technology.\n", "date": "2023-06-22", "categories": []}
{"id": "2311.00671", "title": "Emotion Detection for Misinformation: A Review", "abstract": "  With the advent of social media, an increasing number of netizens are sharing\nand reading posts and news online. However, the huge volumes of misinformation\n(e.g., fake news and rumors) that flood the internet can adversely affect\npeople's lives, and have resulted in the emergence of rumor and fake news\ndetection as a hot research topic. The emotions and sentiments of netizens, as\nexpressed in social media posts and news, constitute important factors that can\nhelp to distinguish fake news from genuine news and to understand the spread of\nrumors. This article comprehensively reviews emotion-based methods for\nmisinformation detection. We begin by explaining the strong links between\nemotions and misinformation. We subsequently provide a detailed analysis of a\nrange of misinformation detection methods that employ a variety of emotion,\nsentiment and stance-based features, and describe their strengths and\nweaknesses. Finally, we discuss a number of ongoing challenges in emotion-based\nmisinformation detection based on large language models and suggest future\nresearch directions, including data collection (multi-platform, multilingual),\nannotation, benchmark, multimodality, and interpretability.\n", "date": "2023-11-01", "categories": []}
{"id": "2402.08764", "title": "A Dataset for the Detection of Dehumanizing Language", "abstract": "  Dehumanization is a mental process that enables the exclusion and ill\ntreatment of a group of people. In this paper, we present two data sets of\ndehumanizing text, a large, automatically collected corpus and a smaller,\nmanually annotated data set. Both data sets include a combination of political\ndiscourse and dialogue from movie subtitles. Our methods give us a broad and\nvaried amount of dehumanization data to work with, enabling further exploratory\nanalysis and automatic classification of dehumanization patterns. Both data\nsets will be publicly released.\n", "date": "2024-02-13", "categories": []}
{"id": "2305.08383", "title": "The Impact of Incumbent/Opposition Status and Ideological Similitude on\n  Emotions in Political Manifestos", "abstract": "  The study involved the analysis of emotion-associated language in the UK\nConservative and Labour party general election manifestos between 2000 to 2019.\nWhile previous research have shown a general correlation between ideological\npositioning and overlap of public policies, there are still conflicting results\nin matters of sentiments in such manifestos. Using new data, we present how\nvalence level can be swayed by party status within government with incumbent\nparties presenting a higher frequency in positive emotion-associated words\nwhile negative emotion-associated words are more prevalent in opposition\nparties. We also demonstrate that parties with ideological similitude use\npositive language prominently further adding to the literature on the\nrelationship between sentiments and party status.\n", "date": "2023-05-15", "categories": []}
{"id": "2305.18004", "title": "The Effects of Political Martyrdom on Election Results: The\n  Assassination of Abe", "abstract": "  In developed nations assassinations are rare and thus the impact of such acts\non the electoral and political landscape is understudied. In this paper, we\nfocus on Twitter data to examine the effects of Japan's former Primer Minister\nAbe's assassination on the Japanese House of Councillors elections in 2022. We\nutilize sentiment analysis and emotion detection together with topic modeling\non over 2 million tweets and compare them against tweets during previous\nelection cycles. Our findings indicate that Twitter sentiments were negatively\nimpacted by the event in the short term and that social media attention span\nhas shortened. We also discuss how \"necropolitics\" affected the outcome of the\nelections in favor of the deceased's party meaning that there seems to have\nbeen an effect of Abe's death on the election outcome though the findings\nwarrant further investigation for conclusive results.\n", "date": "2023-05-29", "categories": []}
{"id": "2402.00345", "title": "IndiVec: An Exploration of Leveraging Large Language Models for Media\n  Bias Detection with Fine-Grained Bias Indicators", "abstract": "  This study focuses on media bias detection, crucial in today's era of\ninfluential social media platforms shaping individual attitudes and opinions.\nIn contrast to prior work that primarily relies on training specific models\ntailored to particular datasets, resulting in limited adaptability and subpar\nperformance on out-of-domain data, we introduce a general bias detection\nframework, IndiVec, built upon large language models. IndiVec begins by\nconstructing a fine-grained media bias database, leveraging the robust\ninstruction-following capabilities of large language models and vector database\ntechniques. When confronted with new input for bias detection, our framework\nautomatically selects the most relevant indicator from the vector database and\nemploys majority voting to determine the input's bias label. IndiVec excels\ncompared to previous methods due to its adaptability (demonstrating consistent\nperformance across diverse datasets from various sources) and explainability\n(providing explicit top-k indicators to interpret bias predictions).\nExperimental results on four political bias datasets highlight IndiVec's\nsignificant superiority over baselines. Furthermore, additional experiments and\nanalysis provide profound insights into the framework's effectiveness.\n", "date": "2024-02-01", "categories": []}
{"id": "2402.03642", "title": "Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish\n  Misinformation", "abstract": "  The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide\nhigh-quality, annotated, 5-way stance data extracted from Twitter, suitable for\nanalyzing cross-cultural and cross-lingual misinformation. In the Stanceosaurus\n2.0 iteration, we extend this framework to encompass Russian and Spanish. The\nformer is of current significance due to prevalent misinformation amid\nescalating tensions with the West and the violent incursion into Ukraine. The\nlatter, meanwhile, represents an enormous community that has been largely\noverlooked on major social media platforms. By incorporating an additional\n3,874 Spanish and Russian tweets over 41 misinformation claims, our objective\nis to support research focused on these issues. To demonstrate the value of\nthis data, we employed zero-shot cross-lingual transfer on multilingual BERT,\nyielding results on par with the initial Stanceosaurus study with a macro F1\nscore of 43 for both languages. This underlines the viability of stance\nclassification as an effective tool for identifying multicultural\nmisinformation.\n", "date": "2024-02-06", "categories": []}
{"id": "2304.13861", "title": "The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in\n  Classification Tasks", "abstract": "  In the realm of Computational Social Science (CSS), practitioners often\nnavigate complex, low-resource domains and face the costly and time-intensive\nchallenges of acquiring and annotating data. We aim to establish a set of\nguidelines to address such challenges, comparing the use of human-labeled data\nwith synthetically generated data from GPT-4 and Llama-2 in ten distinct CSS\nclassification tasks of varying complexity. Additionally, we examine the impact\nof training data sizes on performance. Our findings reveal that models trained\non human-labeled data consistently exhibit superior or comparable performance\ncompared to their synthetically augmented counterparts. Nevertheless, synthetic\naugmentation proves beneficial, particularly in improving performance on rare\nclasses within multi-class tasks. Furthermore, we leverage GPT-4 and Llama-2\nfor zero-shot classification and find that, while they generally display strong\nperformance, they often fall short when compared to specialized classifiers\ntrained on moderately sized training sets.\n", "date": "2023-04-26", "categories": []}
{"id": "2308.02068", "title": "Specious Sites: Tracking the Spread and Sway of Spurious News Stories at\n  Scale", "abstract": "  Misinformation, propaganda, and outright lies proliferate on the web, with\nsome narratives having dangerous real-world consequences on public health,\nelections, and individual safety. However, despite the impact of\nmisinformation, the research community largely lacks automated and programmatic\napproaches for tracking news narratives across online platforms. In this work,\nutilizing daily scrapes of 1,334 unreliable news websites, the large-language\nmodel MPNet, and DP-Means clustering, we introduce a system to automatically\nidentify and track the narratives spread within online ecosystems. Identifying\n52,036 narratives on these 1,334 websites, we describe the most prevalent\nnarratives spread in 2022 and identify the most influential websites that\noriginate and amplify narratives. Finally, we show how our system can be\nutilized to detect new narratives originating from unreliable news websites and\nto aid fact-checkers in more quickly addressing misinformation. We release code\nand data at https://github.com/hanshanley/specious-sites.\n", "date": "2023-08-03", "categories": []}
{"id": "2402.02056", "title": "AnthroScore: A Computational Linguistic Measure of Anthropomorphism", "abstract": "  Anthropomorphism, or the attribution of human-like characteristics to\nnon-human entities, has shaped conversations about the impacts and\npossibilities of technology. We present AnthroScore, an automatic metric of\nimplicit anthropomorphism in language. We use a masked language model to\nquantify how non-human entities are implicitly framed as human by the\nsurrounding context. We show that AnthroScore corresponds with human judgments\nof anthropomorphism and dimensions of anthropomorphism described in social\nscience literature. Motivated by concerns of misleading anthropomorphism in\ncomputer science discourse, we use AnthroScore to analyze 15 years of research\npapers and downstream news articles. In research papers, we find that\nanthropomorphism has steadily increased over time, and that papers related to\nlanguage models have the most anthropomorphism. Within ACL papers, temporal\nincreases in anthropomorphism are correlated with key neural advancements.\nBuilding upon concerns of scientific misinformation in mass media, we identify\nhigher levels of anthropomorphism in news headlines compared to the research\npapers they cite. Since AnthroScore is lexicon-free, it can be directly applied\nto a wide range of text sources.\n", "date": "2024-02-03", "categories": []}
{"id": "2402.02572", "title": "A Quantitative Discourse Analysis of Asian Workers in the US Historical\n  Newspapers", "abstract": "  Warning: This paper contains examples of offensive language targetting\nmarginalized population. The digitization of historical texts invites\nresearchers to explore the large-scale corpus of historical texts with\ncomputational methods. In this study, we present computational text analysis on\na relatively understudied topic of how Asian workers are represented in\nhistorical newspapers in the United States. We found that the word \"coolie\" was\nsemantically different in some States (e.g., Massachusetts, Rhode Island,\nWyoming, Oklahoma, and Arkansas) with the different discourses around coolie.\nWe also found that then-Confederate newspapers and then-Union newspapers formed\ndistinctive discourses by measuring over-represented words. Newspapers from\nthen-Confederate States associated coolie with slavery-related words. In\naddition, we found Asians were perceived to be inferior to European immigrants\nand subjected to the target of racism. This study contributes to supplementing\nthe qualitative analysis of racism in the United States with quantitative\ndiscourse analysis.\n", "date": "2024-02-04", "categories": []}
{"id": "2402.00029", "title": "Exploring Public Opinion on Responsible AI Through The Lens of Cultural\n  Consensus Theory", "abstract": "  As the societal implications of Artificial Intelligence (AI) continue to\ngrow, the pursuit of responsible AI necessitates public engagement in its\ndevelopment and governance processes. This involvement is crucial for capturing\ndiverse perspectives and promoting equitable practices and outcomes. We applied\nCultural Consensus Theory (CCT) to a nationally representative survey dataset\non various aspects of AI to discern beliefs and attitudes about responsible AI\nin the United States. Our results offer valuable insights by identifying shared\nand contrasting views on responsible AI. Furthermore, these findings serve as\ncritical reference points for developers and policymakers, enabling them to\nmore effectively consider individual variances and group-level cultural\nperspectives when making significant decisions and addressing the public's\nconcerns.\n", "date": "2024-01-06", "categories": []}
{"id": "2401.15509", "title": "Style-News: Incorporating Stylized News Generation and Adversarial\n  Verification for Neural Fake News Detection", "abstract": "  With the improvements in generative models, the issues of producing\nhallucinations in various domains (e.g., law, writing) have been brought to\npeople's attention due to concerns about misinformation. In this paper, we\nfocus on neural fake news, which refers to content generated by neural networks\naiming to mimic the style of real news to deceive people. To prevent harmful\ndisinformation spreading fallaciously from malicious social media (e.g.,\ncontent farms), we propose a novel verification framework, Style-News, using\npublisher metadata to imply a publisher's template with the corresponding text\ntypes, political stance, and credibility. Based on threat modeling aspects, a\nstyle-aware neural news generator is introduced as an adversary for generating\nnews content conditioning for a specific publisher, and style and source\ndiscriminators are trained to defend against this attack by identifying which\npublisher the style corresponds with, and discriminating whether the source of\nthe given news is human-written or machine-generated. To evaluate the quality\nof the generated content, we integrate various dimensional metrics (language\nfluency, content preservation, and style adherence) and demonstrate that\nStyle-News significantly outperforms the previous approaches by a margin of\n0.35 for fluency, 15.24 for content, and 0.38 for style at most. Moreover, our\ndiscriminative model outperforms state-of-the-art baselines in terms of\npublisher prediction (up to 4.64%) and neural fake news detection (+6.94%\n$\\sim$ 31.72%).\n", "date": "2024-01-27", "categories": []}
{"id": "2401.15717", "title": "Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection", "abstract": "  Many European citizens become targets of the Kremlin propaganda campaigns,\naiming to minimise public support for Ukraine, foster a climate of mistrust and\ndisunity, and shape elections (Meister, 2022). To address this challenge, we\ndeveloped ''Check News in 1 Click'', the first NLP-empowered pro-Kremlin\npropaganda detection application available in 7 languages, which provides the\nlay user with feedback on their news, and explains manipulative linguistic\nfeatures and keywords. We conducted a user study, analysed user entries and\nmodels' behaviour paired with questionnaire answers, and investigated the\nadvantages and disadvantages of the proposed interpretative solution.\n", "date": "2024-01-28", "categories": []}
{"id": "2401.10841", "title": "Using LLMs to discover emerging coded antisemitic hate-speech in\n  extremist social media", "abstract": "  Online hate speech proliferation has created a difficult problem for social\nmedia platforms. A particular challenge relates to the use of coded language by\ngroups interested in both creating a sense of belonging for its users and\nevading detection. Coded language evolves quickly and its use varies over time.\nThis paper proposes a methodology for detecting emerging coded hate-laden\nterminology. The methodology is tested in the context of online antisemitic\ndiscourse. The approach considers posts scraped from social media platforms,\noften used by extremist users. The posts are scraped using seed expressions\nrelated to previously known discourse of hatred towards Jews. The method begins\nby identifying the expressions most representative of each post and calculating\ntheir frequency in the whole corpus. It filters out grammatically incoherent\nexpressions as well as previously encountered ones so as to focus on emergent\nwell-formed terminology. This is followed by an assessment of semantic\nsimilarity to known antisemitic terminology using a fine-tuned large language\nmodel, and subsequent filtering out of the expressions that are too distant\nfrom known expressions of hatred. Emergent antisemitic expressions containing\nterms clearly relating to Jewish topics are then removed to return only coded\nexpressions of hatred.\n", "date": "2024-01-19", "categories": []}
{"id": "2401.12509", "title": "Digital cloning of online social networks for language-sensitive\n  agent-based modeling of misinformation spread", "abstract": "  We develop a simulation framework for studying misinformation spread within\nonline social networks that blends agent-based modeling and natural language\nprocessing techniques. While many other agent-based simulations exist in this\nspace, questions over their fidelity and generalization to existing networks in\npart hinders their ability to provide actionable insights. To partially address\nthese concerns, we create a 'digital clone' of a known misinformation sharing\nnetwork by downloading social media histories for over ten thousand of its\nusers. We parse these histories to both extract the structure of the network\nand model the nuanced ways in which information is shared and spread among its\nmembers. Unlike many other agent-based methods in this space, information\nsharing between users in our framework is sensitive to topic of discussion,\nuser preferences, and online community dynamics. To evaluate the fidelity of\nour method, we seed our cloned network with a set of posts recorded in the base\nnetwork and compare propagation dynamics between the two, observing reasonable\nagreement across the twin networks over a variety of metrics. Lastly, we\nexplore how the cloned network may serve as a flexible, low-cost testbed for\nmisinformation countermeasure evaluation and red teaming analysis. We hope the\ntools explored here augment existing efforts in the space and unlock new\nopportunities for misinformation countermeasure evaluation, a field that may\nbecome increasingly important to consider with the anticipated rise of\nmisinformation campaigns fueled by generative artificial intelligence.\n", "date": "2024-01-23", "categories": []}
{"id": "2401.11839", "title": "AI for social science and social science of AI: A Survey", "abstract": "  Recent advancements in artificial intelligence, particularly with the\nemergence of large language models (LLMs), have sparked a rethinking of\nartificial general intelligence possibilities. The increasing human-like\ncapabilities of AI are also attracting attention in social science research,\nleading to various studies exploring the combination of these two fields. In\nthis survey, we systematically categorize previous explorations in the\ncombination of AI and social science into two directions that share common\ntechnical approaches but differ in their research objectives. The first\ndirection is focused on AI for social science, where AI is utilized as a\npowerful tool to enhance various stages of social science research. While the\nsecond direction is the social science of AI, which examines AI agents as\nsocial entities with their human-like cognitive and linguistic capabilities. By\nconducting a thorough review, particularly on the substantial progress\nfacilitated by recent advancements in large language models, this paper\nintroduces a fresh perspective to reassess the relationship between AI and\nsocial science, provides a cohesive framework that allows researchers to\nunderstand the distinctions and connections between AI for social science and\nsocial science of AI, and also summarized state-of-art experiment simulation\nplatforms to facilitate research in these two directions. We believe that as AI\ntechnology continues to advance and intelligent agents find increasing\napplications in our daily lives, the significance of the combination of AI and\nsocial science will become even more prominent.\n", "date": "2024-01-22", "categories": []}
{"id": "2310.08483", "title": "Understanding the Humans Behind Online Misinformation: An Observational\n  Study Through the Lens of the COVID-19 Pandemic", "abstract": "  The proliferation of online misinformation has emerged as one of the biggest\nthreats to society. Considerable efforts have focused on building\nmisinformation detection models, still the perils of misinformation remain\nabound. Mitigating online misinformation and its ramifications requires a\nholistic approach that encompasses not only an understanding of its intricate\nlandscape in relation to the complex issue and topic-rich information ecosystem\nonline, but also the psychological drivers of individuals behind it. Adopting a\ntime series analytic technique and robust causal inference-based design, we\nconduct a large-scale observational study analyzing over 32 million COVID-19\ntweets and 16 million historical timeline tweets. We focus on understanding the\nbehavior and psychology of users disseminating misinformation during COVID-19\nand its relationship with the historical inclinations towards sharing\nmisinformation on Non-COVID domains before the pandemic. Our analysis\nunderscores the intricacies inherent to cross-domain misinformation, and\nhighlights that users' historical inclination toward sharing misinformation is\npositively associated with their present behavior pertaining to misinformation\nsharing on emergent topics and beyond. This work may serve as a valuable\nfoundation for designing user-centric inoculation strategies and\necologically-grounded agile interventions for effectively tackling online\nmisinformation.\n", "date": "2023-10-12", "categories": []}
{"id": "2401.09724", "title": "Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance", "abstract": "  In the age of the infodemic, it is crucial to have tools for effectively\nmonitoring the spread of rampant rumors that can quickly go viral, as well as\nidentifying vulnerable users who may be more susceptible to spreading such\nmisinformation. This proactive approach allows for timely preventive measures\nto be taken, mitigating the negative impact of false information on society. We\npropose a novel approach to predict viral rumors and vulnerable users using a\nunified graph neural network model. We pre-train network-based user embeddings\nand leverage a cross-attention mechanism between users and posts, together with\na community-enhanced vulnerability propagation (CVP) method to improve user and\npropagation graph representations. Furthermore, we employ two multi-task\ntraining strategies to mitigate negative transfer effects among tasks in\ndifferent settings, enhancing the overall performance of our approach. We also\nconstruct two datasets with ground-truth annotations on information virality\nand user vulnerability in rumor and non-rumor events, which are automatically\nderived from existing rumor detection datasets. Extensive evaluation results of\nour joint learning model confirm its superiority over strong baselines in all\nthree tasks: rumor detection, virality prediction, and user vulnerability\nscoring. For instance, compared to the best baselines based on the Weibo\ndataset, our model makes 3.8\\% and 3.0\\% improvements on Accuracy and MacF1 for\nrumor detection, and reduces mean squared error (MSE) by 23.9\\% and 16.5\\% for\nvirality prediction and user vulnerability scoring, respectively. Our findings\nsuggest that our approach effectively captures the correlation between rumor\nvirality and user vulnerability, leveraging this information to improve\nprediction performance and provide a valuable tool for infodemic surveillance.\n", "date": "2024-01-18", "categories": []}
{"id": "2401.10030", "title": "Framing Analysis of Health-Related Narratives: Conspiracy versus\n  Mainstream Media", "abstract": "  Understanding how online media frame issues is crucial due to their impact on\npublic opinion. Research on framing using natural language processing\ntechniques mainly focuses on specific content features in messages and neglects\ntheir narrative elements. Also, the distinction between framing in different\nsources remains an understudied problem. We address those issues and\ninvestigate how the framing of health-related topics, such as COVID-19 and\nother diseases, differs between conspiracy and mainstream websites. We\nincorporate narrative information into the framing analysis by introducing a\nnovel frame extraction approach based on semantic graphs. We find that\nhealth-related narratives in conspiracy media are predominantly framed in terms\nof beliefs, while mainstream media tend to present them in terms of science. We\nhope our work offers new ways for a more nuanced frame analysis.\n", "date": "2024-01-18", "categories": []}
{"id": "2401.07928", "title": "A Lexicon for Studying Radicalization in Incel Communities", "abstract": "  Incels are an extremist online community of men who believe in an ideology\nrooted in misogyny, racism, the glorification of violence, and dehumanization.\nIn their online forums, they use an extensive, evolving cryptolect - a set of\ningroup terms that have meaning within the group, reflect the ideology,\ndemonstrate membership in the community, and are difficult for outsiders to\nunderstand. This paper presents a lexicon with terms and definitions for common\nincel root words, prefixes, and affixes. The lexicon is text-based for use in\nautomated analysis and is derived via a Qualitative Content Analysis of the\nmost frequent incel words, their structure, and their meaning on five of the\nmost active incel communities from 2016 to 2023. This lexicon will support\nfuture work examining radicalization and deradicalization/disengagement within\nthe community.\n", "date": "2024-01-15", "categories": []}
{"id": "2312.16148", "title": "The Media Bias Taxonomy: A Systematic Literature Review on the Forms and\n  Automated Detection of Media Bias", "abstract": "  The way the media presents events can significantly affect public perception,\nwhich in turn can alter people's beliefs and views. Media bias describes a\none-sided or polarizing perspective on a topic. This article summarizes the\nresearch on computational methods to detect media bias by systematically\nreviewing 3140 research papers published between 2019 and 2022. To structure\nour review and support a mutual understanding of bias across research domains,\nwe introduce the Media Bias Taxonomy, which provides a coherent overview of the\ncurrent state of research on media bias from different perspectives. We show\nthat media bias detection is a highly active research field, in which\ntransformer-based classification approaches have led to significant\nimprovements in recent years. These improvements include higher classification\naccuracy and the ability to detect more fine-granular types of bias. However,\nwe have identified a lack of interdisciplinarity in existing projects, and a\nneed for more awareness of the various types of media bias to support\nmethodologically thorough performance evaluations of media bias detection\nsystems. Concluding from our analysis, we see the integration of recent machine\nlearning advancements with reliable and diverse bias assessment strategies from\nother research areas as the most promising area for future research\ncontributions in the field.\n", "date": "2023-12-26", "categories": []}
{"id": "2306.02052", "title": "Conflicts, Villains, Resolutions: Towards models of Narrative Media\n  Framing", "abstract": "  Despite increasing interest in the automatic detection of media frames in\nNLP, the problem is typically simplified as single-label classification and\nadopts a topic-like view on frames, evading modelling the broader\ndocument-level narrative. In this work, we revisit a widely used\nconceptualization of framing from the communication sciences which explicitly\ncaptures elements of narratives, including conflict and its resolution, and\nintegrate it with the narrative framing of key entities in the story as heroes,\nvictims or villains. We adapt an effective annotation paradigm that breaks a\ncomplex annotation task into a series of simpler binary questions, and present\nan annotated data set of English news articles, and a case study on the framing\nof climate change in articles from news outlets across the political spectrum.\nFinally, we explore automatic multi-label prediction of our frames with\nsupervised and semi-supervised approaches, and present a novel retrieval-based\nmethod which is both effective and transparent in its predictions. We conclude\nwith a discussion of opportunities and challenges for future work on\ndocument-level models of narrative framing.\n", "date": "2023-06-03", "categories": []}
{"id": "2310.11097", "title": "Experimenting AI Technologies for Disinformation Combat: the IDMO\n  Project", "abstract": "  The Italian Digital Media Observatory (IDMO) project, part of a European\ninitiative, focuses on countering disinformation and fake news. This report\noutlines contributions from Rai-CRITS to the project, including: (i) the\ncreation of novel datasets for testing technologies (ii) development of an\nautomatic model for categorizing Pagella Politica verdicts to facilitate\nbroader analysis (iii) creation of an automatic model for recognizing textual\nentailment with exceptional accuracy on the FEVER dataset (iv) assessment using\nGPT-4 to detecting content treatment style (v) a game to raise awareness about\nfake news at national events.\n", "date": "2023-10-17", "categories": []}
{"id": "2312.15040", "title": "Towards Detecting Cascades of Biased Medical Claims on Twitter", "abstract": "  Social media may disseminate medical claims that highlight misleading\ncorrelations between social identifiers and diseases due to not accounting for\nstructural determinants of health. Our research aims to identify biased medical\nclaims on Twitter and measure their spread. We propose a machine learning\nframework that uses two models in tandem: RoBERTa to detect medical claims and\nDistilBERT to classify bias. After identifying original biased medical claims,\nwe conducted a retweet cascade analysis, computing their individual reach and\nrate of spread. Tweets containing biased claims were found to circulate faster\nand further than unbiased claims.\n", "date": "2023-12-22", "categories": []}
{"id": "2312.08995", "title": "FrameFinder: Explorative Multi-Perspective Framing Extraction from News\n  Headlines", "abstract": "  Revealing the framing of news articles is an important yet neglected task in\ninformation seeking and retrieval. In the present work, we present FrameFinder,\nan open tool for extracting and analyzing frames in textual data. FrameFinder\nvisually represents the frames of text from three perspectives, i.e., (i) frame\nlabels, (ii) frame dimensions, and (iii) frame structure. By analyzing the\nwell-established gun violence frame corpus, we demonstrate the merits of our\nproposed solution to support social science research and call for subsequent\nintegration into information interactions.\n", "date": "2023-12-14", "categories": []}
{"id": "2312.10095", "title": "Casual Social Media Use among the Youth: Effects on Online and Offline\n  Political Participation", "abstract": "  Background: Previous studies suggest that social media use among the youth is\ncorrelated with online and offline political participation. There is also a\nmixed and inconclusive debate on whether more online political participation in\nthe youth increases their offline political participation. Methods: This study\nuses three models of OLS, two-way fixed effects, and an instrumental variable\napproach to make causal inferences about social media use, online, and offline\npolitical participation of the youth. Findings: The analyses provide evidence\nof a large effect of casual social media use on online political participation,\nand no effect or negligible effect on offline political participation and\nvoting behavior. The results from fixed effects and instrumental variable\nmodels provide strong evidence of elasticity between online and offline\npolitical participation in young individuals. On average, a one percent\nincrease in online political participation increases the offline political\nactivity index by 0.12 percent.\n", "date": "2023-12-14", "categories": []}
{"id": "2312.10694", "title": "Discretionary Trees: Understanding Street-Level Bureaucracy via Machine\n  Learning", "abstract": "  Street-level bureaucrats interact directly with people on behalf of\ngovernment agencies to perform a wide range of functions, including, for\nexample, administering social services and policing. A key feature of\nstreet-level bureaucracy is that the civil servants, while tasked with\nimplementing agency policy, are also granted significant discretion in how they\nchoose to apply that policy in individual cases. Using that discretion could be\nbeneficial, as it allows for exceptions to policies based on human interactions\nand evaluations, but it could also allow biases and inequities to seep into\nimportant domains of societal resource allocation. In this paper, we use\nmachine learning techniques to understand street-level bureaucrats' behavior.\nWe leverage a rich dataset that combines demographic and other information on\nhouseholds with information on which homelessness interventions they were\nassigned during a period when assignments were not formulaic. We find that\ncaseworker decisions in this time are highly predictable overall, and some, but\nnot all of this predictivity can be captured by simple decision rules. We\ntheorize that the decisions not captured by the simple decision rules can be\nconsidered applications of caseworker discretion. These discretionary decisions\nare far from random in both the characteristics of such households and in terms\nof the outcomes of the decisions. Caseworkers typically only apply discretion\nto households that would be considered less vulnerable. When they do apply\ndiscretion to assign households to more intensive interventions, the marginal\nbenefits to those households are significantly higher than would be expected if\nthe households were chosen at random; there is no similar reduction in marginal\nbenefit to households that are discretionarily allocated less intensive\ninterventions, suggesting that caseworkers are improving outcomes using their\nknowledge.\n", "date": "2023-12-17", "categories": []}
{"id": "2312.09536", "title": "Riveter: Measuring Power and Social Dynamics Between Entities", "abstract": "  Riveter provides a complete easy-to-use pipeline for analyzing verb\nconnotations associated with entities in text corpora. We prepopulate the\npackage with connotation frames of sentiment, power, and agency, which have\ndemonstrated usefulness for capturing social phenomena, such as gender bias, in\na broad range of corpora. For decades, lexical frameworks have been\nfoundational tools in computational social science, digital humanities, and\nnatural language processing, facilitating multifaceted analysis of text\ncorpora. But working with verb-centric lexica specifically requires natural\nlanguage processing skills, reducing their accessibility to other researchers.\nBy organizing the language processing pipeline, providing complete lexicon\nscores and visualizations for all entities in a corpus, and providing\nfunctionality for users to target specific research questions, Riveter greatly\nimproves the accessibility of verb lexica and can facilitate a broad range of\nfuture research.\n", "date": "2023-12-15", "categories": []}
{"id": "2310.14032", "title": "Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic\n  Study", "abstract": "  This paper analyses two hitherto unstudied sites sharing state-backed\ndisinformation, Reliable Recent News (rrn.world) and WarOnFakes\n(waronfakes.com), which publish content in Arabic, Chinese, English, French,\nGerman, and Spanish. We describe our content acquisition methodology and\nperform cross-site unsupervised topic clustering on the resulting multilingual\ndataset. We also perform linguistic and temporal analysis of the web page\ntranslations and topics over time, and investigate articles with false\npublication dates. We make publicly available this new dataset of 14,053\narticles, annotated with each language version, and additional metadata such as\nlinks and images. The main contribution of this paper for the NLP community is\nin the novel dataset which enables studies of disinformation networks, and the\ntraining of NLP tools for disinformation detection.\n", "date": "2023-10-21", "categories": []}
{"id": "2312.07194", "title": "Verbreitungsmechanismen sch\\\"adigender Sprache im Netz: Anatomie zweier\n  Shitstorms", "abstract": "  In this working paper, we turn our attention to two exemplary, cross-media\nshitstorms directed against well-known individuals from the business world.\nBoth have in common, first, the trigger, a controversial statement by the\nperson who thereby becomes the target of the shitstorm, and second, the\nidentity of this target as relatively privileged: cis-male, white, successful.\nWe examine the spread of the outrage wave across two media at a time and test\nthe applicability of computational linguistic methods for analyzing its time\ncourse. Assuming that harmful language spreads like a virus in digital space,\nwe are primarily interested in the events and constellations that lead to the\nuse of harmful language, and whether and how a linguistic formation of \"tribes\"\noccurs. Our research therefore focuses, first, on the distribution of\nlinguistic features within the overall shitstorm: are individual words or\nphrases increasingly used after their introduction, and through which pathways\nthey spread. Second, we ask whether \"tribes,\" for example, one group of\nsupporters and one of opponents of the target, have a distinguished linguistic\nform. Our hypothesis is that supporters remain equally active over time, while\nthe dynamic \"ripple\" effect of the shitstorm is based on the varying\nparticipation of opponents.\n", "date": "2023-12-12", "categories": []}
{"id": "2312.03730", "title": "FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News\n  for Credible US Elections", "abstract": "  In today's technologically driven world, the spread of fake news,\nparticularly during crucial events such as elections, presents an increasing\nchallenge to the integrity of information. To address this challenge, we\nintroduce FakeWatch ElectionShield, an innovative framework carefully designed\nto detect fake news. We have created a novel dataset of North American\nelection-related news articles through a blend of advanced language models\n(LMs) and thorough human verification, for precision and relevance. We propose\na model hub of LMs for identifying fake news. Our goal is to provide the\nresearch community with adaptable and accurate classification models in\nrecognizing the dynamic nature of misinformation. Extensive evaluation of fake\nnews classifiers on our dataset and a benchmark dataset shows our that while\nstate-of-the-art LMs slightly outperform the traditional ML models, classical\nmodels are still competitive with their balance of accuracy, explainability,\nand computational efficiency. This research sets the foundation for future\nstudies to address misinformation related to elections.\n", "date": "2023-11-27", "categories": []}
{"id": "2312.04804", "title": "Hate Cannot Drive out Hate: Forecasting Conversation Incivility\n  following Replies to Hate Speech", "abstract": "  User-generated replies to hate speech are promising means to combat hatred,\nbut questions about whether they can stop incivility in follow-up conversations\nlinger. We argue that effective replies stop incivility from emerging in\nfollow-up conversations - replies that elicit more incivility are\ncounterproductive. This study introduces the task of predicting the incivility\nof conversations following replies to hate speech. We first propose a metric to\nmeasure conversation incivility based on the number of civil and uncivil\ncomments as well as the unique authors involved in the discourse. Our metric\napproximates human judgments more accurately than previous metrics. We then use\nthe metric to evaluate the outcomes of replies to hate speech. A linguistic\nanalysis uncovers the differences in the language of replies that elicit\nfollow-up conversations with high and low incivility. Experimental results show\nthat forecasting incivility is challenging. We close with a qualitative\nanalysis shedding light into the most common errors made by the best model.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.00168", "title": "Navigating News Narratives: A Media Bias Analysis Dataset", "abstract": "  The proliferation of biased news narratives across various media platforms\nhas become a prominent challenge, influencing public opinion on critical topics\nlike politics, health, and climate change. This paper introduces the\n\"Navigating News Narratives: A Media Bias Analysis Dataset\", a comprehensive\ndataset to address the urgent need for tools to detect and analyze media bias.\nThis dataset encompasses a broad spectrum of biases, making it a unique and\nvaluable asset in the field of media studies and artificial intelligence. The\ndataset is available at\nhttps://huggingface.co/datasets/newsmediabias/news-bias-full-data.\n", "date": "2023-11-30", "categories": []}
{"id": "2312.03751", "title": "Which linguistic cues make people fall for fake news? A comparison of\n  cognitive and affective processing", "abstract": "  Fake news on social media has large, negative implications for society.\nHowever, little is known about what linguistic cues make people fall for fake\nnews and, hence, how to design effective countermeasures for social media. In\nthis study, we seek to understand which linguistic cues make people fall for\nfake news. Linguistic cues (e.g., adverbs, personal pronouns, positive emotion\nwords, negative emotion words) are important characteristics of any text and\nalso affect how people process real vs. fake news. Specifically, we compare the\nrole of linguistic cues across both cognitive processing (related to careful\nthinking) and affective processing (related to unconscious automatic\nevaluations). To this end, we performed a within-subject experiment where we\ncollected neurophysiological measurements of 42 subjects while these read a\nsample of 40 real and fake news articles. During our experiment, we measured\ncognitive processing through eye fixations, and affective processing in situ\nthrough heart rate variability. We find that users engage more in cognitive\nprocessing for longer fake news articles, while affective processing is more\npronounced for fake news written in analytic words. To the best of our\nknowledge, this is the first work studying the role of linguistic cues in fake\nnews processing. Altogether, our findings have important implications for\ndesigning online platforms that encourage users to engage in careful thinking\nand thus prevent them from falling for fake news.\n", "date": "2023-12-02", "categories": []}
{"id": "2312.04052", "title": "Multimodal Misinformation Detection in a South African Social Media\n  Environment", "abstract": "  With the constant spread of misinformation on social media networks, a need\nhas arisen to continuously assess the veracity of digital content. This need\nhas inspired numerous research efforts on the development of misinformation\ndetection (MD) models. However, many models do not use all information\navailable to them and existing research contains a lack of relevant datasets to\ntrain the models, specifically within the South African social media\nenvironment. The aim of this paper is to investigate the transferability of\nknowledge of a MD model between different contextual environments. This\nresearch contributes a multimodal MD model capable of functioning in the South\nAfrican social media environment, as well as introduces a South African\nmisinformation dataset. The model makes use of multiple sources of information\nfor misinformation detection, namely: textual and visual elements. It uses\nbidirectional encoder representations from transformers (BERT) as the textual\nencoder and a residual network (ResNet) as the visual encoder. The model is\ntrained and evaluated on the Fakeddit dataset and a South African\nmisinformation dataset. Results show that using South African samples in the\ntraining of the model increases model performance, in a South African\ncontextual environment, and that a multimodal model retains significantly more\nknowledge than both the textual and visual unimodal models. Our study suggests\nthat the performance of a misinformation detection model is influenced by the\ncultural nuances of its operating environment and multimodal models assist in\nthe transferability of knowledge between different contextual environments.\nTherefore, local data should be incorporated into the training process of a\nmisinformation detection model in order to optimize model performance.\n", "date": "2023-12-07", "categories": []}
{"id": "2312.03195", "title": "Detecting Rumor Veracity with Only Textual Information by Double-Channel\n  Structure", "abstract": "  Kyle (1985) proposes two types of rumors: informed rumors which are based on\nsome private information and uninformed rumors which are not based on any\ninformation (i.e. bluffing). Also, prior studies find that when people have\ncredible source of information, they are likely to use a more confident textual\ntone in their spreading of rumors. Motivated by these theoretical findings, we\npropose a double-channel structure to determine the ex-ante veracity of rumors\non social media. Our ultimate goal is to classify each rumor into true, false,\nor unverifiable category. We first assign each text into either certain\n(informed rumor) or uncertain (uninformed rumor) category. Then, we apply lie\ndetection algorithm to informed rumors and thread-reply agreement detection\nalgorithm to uninformed rumors. Using the dataset of SemEval 2019 Task 7, which\nrequires ex-ante threefold classification (true, false, or unverifiable) of\nsocial media rumors, our model yields a macro-F1 score of 0.4027, outperforming\nall the baseline models and the second-place winner (Gorrell et al., 2019).\nFurthermore, we empirically validate that the double-channel structure\noutperforms single-channel structures which use either lie detection or\nagreement detection algorithm to all posts.\n", "date": "2023-12-06", "categories": []}
{"id": "2312.01217", "title": "Understanding Opinions Towards Climate Change on Social Media", "abstract": "  Social media platforms such as Twitter (now known as X) have revolutionized\nhow the public engage with important societal and political topics. Recently,\nclimate change discussions on social media became a catalyst for political\npolarization and the spreading of misinformation. In this work, we aim to\nunderstand how real world events influence the opinions of individuals towards\nclimate change related topics on social media. To this end, we extracted and\nanalyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006\nto 2019. Then, we construct a temporal graph from the user-user mentions\nnetwork and utilize the Louvain community detection algorithm to analyze the\nchanges in community structure around Conference of the Parties on Climate\nChange~(COP) events. Next, we also apply tools from the Natural Language\nProcessing literature to perform sentiment analysis and topic modeling on the\ntweets. Our work acts as a first step towards understanding the evolution of\npro-climate change communities around COP events. Answering these questions\nhelps us understand how to raise people's awareness towards climate change thus\nhopefully calling on more individuals to join the collaborative effort in\nslowing down climate change.\n", "date": "2023-12-02", "categories": []}
{"id": "2312.02118", "title": "When it Rains, it Pours: Modeling Media Storms and the News Ecosystem", "abstract": "  Most events in the world receive at most brief coverage by the news media.\nOccasionally, however, an event will trigger a media storm, with voluminous and\nwidespread coverage lasting for weeks instead of days. In this work, we develop\nand apply a pairwise article similarity model, allowing us to identify story\nclusters in corpora covering local and national online news, and thereby create\na comprehensive corpus of media storms over a nearly two year period. Using\nthis corpus, we investigate media storms at a new level of granularity,\nallowing us to validate claims about storm evolution and topical distribution,\nand provide empirical support for previously hypothesized patterns of influence\nof storms on media coverage and intermedia agenda setting.\n", "date": "2023-12-04", "categories": []}
{"id": "2311.18466", "title": "Use of explicit replies as coordination mechanisms in online student\n  debate", "abstract": "  People in conversation entrain their linguistic behaviours through\nspontaneous alignment mechanisms [7] - both in face-to-face and\ncomputer-mediated communication (CMC) [8]. In CMC, one of the mechanisms\nthrough which linguistic entrainment happens is through explicit replies.\nIndeed, the use of explicit replies influences the structure of conversations,\nfavouring the formation of reply-trees typically delineated by topic shifts\n[5]. The interpersonal coordination mechanisms realized by how actors address\neach other have been studied using a probabilistic framework proposed by David\nGibson [2,3]. Other recent approaches use computational methods and information\ntheory to quantify changes in text. We explore coordination mechanisms\nconcerned with some of the roles utterances play in dialogues - specifically in\nexplicit replies. We identify these roles by finding community structure in the\nconversation's vocabulary using a non-parametric, hierarchical topic model.\nSome conversations may always stay on the ground, remaining at the level of\ngeneral introductory chatter. Some others may develop a specific sub-topic in\nsignificant depth and detail. Even others may jump between general chatter,\nout-of-topic remarks and people agreeing or disagreeing without further\nelaboration.\n", "date": "2023-11-30", "categories": []}
{"id": "2311.14677", "title": "Filter bubbles and affective polarization in user-personalized large\n  language model outputs", "abstract": "  Echoing the history of search engines and social media content rankings, the\nadvent of large language models (LLMs) has led to a push for increased\npersonalization of model outputs to individual users. In the past, personalized\nrecommendations and ranking systems have been linked to the development of\nfilter bubbles (serving content that may confirm a user's existing biases) and\naffective polarization (strong negative sentiment towards those with differing\nviews). In this work, we explore how prompting a leading large language model,\nChatGPT-3.5, with a user's political affiliation prior to asking factual\nquestions about public figures and organizations leads to differing results. We\nobserve that left-leaning users tend to receive more positive statements about\nleft-leaning political figures and media outlets, while right-leaning users see\nmore positive statements about right-leaning entities. This pattern holds\nacross presidential candidates, members of the U.S. Senate, and media\norganizations with ratings from AllSides. When qualitatively evaluating some of\nthese outputs, there is evidence that particular facts are included or excluded\nbased on the user's political affiliation. These results illustrate that\npersonalizing LLMs based on user demographics carry the same risks of affective\npolarization and filter bubbles that have been seen in other personalized\ninternet technologies. This ``failure mode\" should be monitored closely as\nthere are more attempts to monetize and personalize these models.\n", "date": "2023-10-31", "categories": []}
{"id": "2311.10784", "title": "ExFake: Towards an Explainable Fake News Detection Based on Content and\n  Social Context Information", "abstract": "  ExFake is an explainable fake news detection system based on content and\ncontext-level information. It is concerned with the veracity analysis of online\nposts based on their content, social context (i.e., online users' credibility\nand historical behaviour), and data coming from trusted entities such as\nfact-checking websites and named entities. Unlike state-of-the-art systems, an\nExplainable AI (XAI) assistant is also adopted to help online social networks\n(OSN) users develop good reflexes when faced with any doubted information that\nspreads on social networks. The trustworthiness of OSN users is also addressed\nby assigning a credibility score to OSN users, as OSN users are one of the main\nculprits for spreading fake news. Experimental analysis on a real-world dataset\ndemonstrates that ExFake significantly outperforms other baseline methods for\nfake news detection.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.10587", "title": "Countering Misinformation via Emotional Response Generation", "abstract": "  The proliferation of misinformation on social media platforms (SMPs) poses a\nsignificant danger to public health, social cohesion and ultimately democracy.\nPrevious research has shown how social correction can be an effective way to\ncurb misinformation, by engaging directly in a constructive dialogue with users\nwho spread -- often in good faith -- misleading messages. Although professional\nfact-checkers are crucial to debunking viral claims, they usually do not engage\nin conversations on social media. Thereby, significant effort has been made to\nautomate the use of fact-checker material in social correction; however, no\nprevious work has tried to integrate it with the style and pragmatics that are\ncommonly employed in social media communication. To fill this gap, we present\nVerMouth, the first large-scale dataset comprising roughly 12 thousand\nclaim-response pairs (linked to debunking articles), accounting for both\nSMP-style and basic emotions, two factors which have a significant role in\nmisinformation credibility and spreading. To collect this dataset we used a\ntechnique based on an author-reviewer pipeline, which efficiently combines LLMs\nand human annotators to obtain high-quality data. We also provide comprehensive\nexperiments showing how models trained on our proposed dataset have significant\nimprovements in terms of output quality and generalization capabilities.\n", "date": "2023-11-17", "categories": []}
{"id": "2308.16328", "title": "Debunking Disinformation: Revolutionizing Truth with NLP in Fake News\n  Detection", "abstract": "  The Internet and social media have altered how individuals access news in the\nage of instantaneous information distribution. While this development has\nincreased access to information, it has also created a significant problem: the\nspread of fake news and information. Fake news is rapidly spreading on digital\nplatforms, which has a negative impact on the media ecosystem, public opinion,\ndecision-making, and social cohesion. Natural Language Processing(NLP), which\noffers a variety of approaches to identify content as authentic, has emerged as\na potent weapon in the growing war against disinformation. This paper takes an\nin-depth look at how NLP technology can be used to detect fake news and reveals\nthe challenges and opportunities it presents.\n", "date": "2023-08-30", "categories": []}
{"id": "2311.09687", "title": "Inducing Political Bias Allows Language Models Anticipate Partisan\n  Reactions to Controversies", "abstract": "  Social media platforms are rife with politically charged discussions.\nTherefore, accurately deciphering and predicting partisan biases using Large\nLanguage Models (LLMs) is increasingly critical. In this study, we address the\nchallenge of understanding political bias in digitized discourse using LLMs.\nWhile traditional approaches often rely on finetuning separate models for each\npolitical faction, our work innovates by employing a singular,\ninstruction-tuned LLM to reflect a spectrum of political ideologies. We present\na comprehensive analytical framework, consisting of Partisan Bias Divergence\nAssessment and Partisan Class Tendency Prediction, to evaluate the model's\nalignment with real-world political ideologies in terms of stances, emotions,\nand moral foundations. Our findings reveal the model's effectiveness in\ncapturing emotional and moral nuances, albeit with some challenges in stance\ndetection, highlighting the intricacies and potential for refinement in NLP\ntools for politically sensitive contexts. This research contributes\nsignificantly to the field by demonstrating the feasibility and importance of\nnuanced political understanding in LLMs, particularly for applications\nrequiring acute awareness of political bias.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.09734", "title": "Tracking the Newsworthiness of Public Documents", "abstract": "  Journalists must find stories in huge amounts of textual data (e.g. leaks,\nbills, press releases) as part of their jobs: determining when and why text\nbecomes news can help us understand coverage patterns and help us build\nassistive tools. Yet, this is challenging because very few labelled links\nexist, language use between corpora is very different, and text may be covered\nfor a variety of reasons. In this work we focus on news coverage of local\npublic policy in the San Francisco Bay Area by the San Francisco Chronicle.\nFirst, we gather news articles, public policy documents and meeting recordings\nand link them using probabilistic relational modeling, which we show is a\nlow-annotation linking methodology that outperforms other retrieval-based\nbaselines. Second, we define a new task: newsworthiness prediction, to predict\nif a policy item will get covered. We show that different aspects of public\npolicy discussion yield different newsworthiness signals. Finally we perform\nhuman evaluation with expert journalists and show our systems identify policies\nthey consider newsworthy with 68% F1 and our coverage recommendations are\nhelpful with an 84% win-rate.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.08666", "title": "It Takes Two to Negotiate: Modeling Social Exchange in Online\n  Multiplayer Games", "abstract": "  Online games are dynamic environments where players interact with each other,\nwhich offers a rich setting for understanding how players negotiate their way\nthrough the game to an ultimate victory. This work studies online player\ninteractions during the turn-based strategy game, Diplomacy. We annotated a\ndataset of over 10,000 chat messages for different negotiation strategies and\nempirically examined their importance in predicting long- and short-term game\noutcomes. Although negotiation strategies can be predicted reasonably\naccurately through the linguistic modeling of the chat messages, more is needed\nfor predicting short-term outcomes such as trustworthiness. On the other hand,\nthey are essential in graph-aware reinforcement learning approaches to predict\nlong-term outcomes, such as a player's success, based on their prior\nnegotiation history. We close with a discussion of the implications and impact\nof our work. The dataset is available at\nhttps://github.com/kj2013/claff-diplomacy.\n", "date": "2023-11-15", "categories": []}
{"id": "2306.08698", "title": "Phase Transitions of Civil Unrest across Countries and Time", "abstract": "  Phase transitions, characterized by abrupt shifts between macroscopic\npatterns of organization, are ubiquitous in complex systems. Despite\nconsiderable research in the physical and natural sciences, the empirical study\nof this phenomenon in societal systems is relatively underdeveloped. The goal\nof this study is to explore whether the dynamics of collective civil unrest can\nbe plausibly characterized as a sequence of recurrent phase shifts, with each\nphase having measurable and identifiable latent characteristics. Building on\nprevious efforts to characterize civil unrest as a self-organized critical\nsystem, we introduce a macro-level statistical model of civil unrest and\nevaluate its plausibility using a comprehensive dataset of civil unrest events\nin 170 countries from 1946 to 2017. Our findings demonstrate that the\nmacro-level phase model effectively captures the characteristics of civil\nunrest data from diverse countries globally and that universal mechanisms may\nunderlie certain aspects of the dynamics of civil unrest. We also introduce a\nscale to quantify a country's long-term unrest per unit of time and show that\ncivil unrest events tend to cluster geographically, with the magnitude of civil\nunrest concentrated in specific regions. Our approach has the potential to\nidentify and measure phase transitions in various collective human phenomena\nbeyond civil unrest, contributing to a better understanding of complex social\nsystems.\n", "date": "2023-06-14", "categories": []}
{"id": "2311.08001", "title": "A Comparative Analysis of the COVID-19 Infodemic in English and Chinese:\n  Insights from Social Media Textual Data", "abstract": "  The COVID-19 infodemic, characterized by the rapid spread of misinformation\nand unverified claims related to the pandemic, presents a significant\nchallenge. This paper presents a comparative analysis of the COVID-19 infodemic\nin the English and Chinese languages, utilizing textual data extracted from\nsocial media platforms. To ensure a balanced representation, two infodemic\ndatasets were created by augmenting previously collected social media textual\ndata. Through word frequency analysis, the thirty-five most frequently\noccurring infodemic words are identified, shedding light on prevalent\ndiscussions surrounding the infodemic. Moreover, topic clustering analysis\nuncovers thematic structures and provides a deeper understanding of primary\ntopics within each language context. Additionally, sentiment analysis enables\ncomprehension of the emotional tone associated with COVID-19 information on\nsocial media platforms in English and Chinese. This research contributes to a\nbetter understanding of the COVID-19 infodemic phenomenon and can guide the\ndevelopment of strategies to combat misinformation during public health crises\nacross different languages.\n", "date": "2023-11-14", "categories": []}
{"id": "2311.06729", "title": "Comprehending Lexical and Affective Ontologies in the Demographically\n  Diverse Spatial Social Media Discourse", "abstract": "  This study aims to comprehend linguistic and socio-demographic features,\nencompassing English language styles, conveyed sentiments, and lexical\ndiversity within spatial online social media review data. To this end, we\nundertake a case study that scrutinizes reviews composed by two distinct and\ndemographically diverse groups. Our analysis entails the extraction and\nexamination of various statistical, grammatical, and sentimental features from\nthese two groups. Subsequently, we leverage these features with machine\nlearning (ML) classifiers to discern their potential in effectively\ndifferentiating between the groups. Our investigation unveils substantial\ndisparities in certain linguistic attributes between the two groups. When\nintegrated into ML classifiers, these attributes exhibit a marked efficacy in\ndistinguishing the groups, yielding a macro F1 score of approximately 0.85.\nFurthermore, we conduct a comparative evaluation of these linguistic features\nwith word n-gram-based lexical features in discerning demographically diverse\nreview data. As expected, the n-gram lexical features, coupled with fine-tuned\ntransformer-based models, show superior performance, attaining accuracies\nsurpassing 95\\% and macro F1 scores exceeding 0.96. Our meticulous analysis and\ncomprehensive evaluations substantiate the efficacy of linguistic and\nsentimental features in effectively discerning demographically diverse review\ndata. The findings of this study provide valuable guidelines for future\nresearch endeavors concerning the analysis of demographic patterns in textual\ncontent across various social media platforms.\n", "date": "2023-11-12", "categories": []}
{"id": "2311.05791", "title": "Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec", "abstract": "  YouTube, a widely popular online platform, has transformed the dynamics of\ncon-tent consumption and interaction for users worldwide. With its extensive\nrange of content crea-tors and viewers, YouTube serves as a hub for video\nsharing, entertainment, and information dissemination. However, the exponential\ngrowth of users and their active engagement on the platform has raised concerns\nregarding suspicious commenter behaviors, particularly in the com-ment section.\nThis paper presents a social network analysis-based methodology for detecting\nsuspicious commenter mob-like behaviors among YouTube channels and the\nsimilarities therein. The method aims to characterize channels based on the\nlevel of such behavior and identify com-mon patterns across them. To evaluate\nthe effectiveness of the proposed model, we conducted an analysis of 20 YouTube\nchannels, consisting of 7,782 videos, 294,199 commenters, and 596,982 comments.\nThese channels were specifically selected for propagating false views about the\nU.S. Military. The analysis revealed significant similarities among the\nchannels, shedding light on the prevalence of suspicious commenter behavior. By\nunderstanding these similarities, we contribute to a better understanding of\nthe dynamics of suspicious behavior on YouTube channels, which can inform\nstrategies for addressing and mitigating such behavior.\n", "date": "2023-11-09", "categories": []}
{"id": "2311.04479", "title": "Twitter Sentiment Analysis of Covid Vacciness", "abstract": "  In this paper, we look at a database of tweets sorted by various keywords\nthat could indicate the users sentiment towards covid vaccines. With social\nmedia becoming such a prevalent source of opinion, sorting and ranking tweets\nthat hold important information such as opinions on covid vaccines is of utmost\nimportance. Two different ranking scales were used, and ranking a tweet in this\nway could represent the difference between an opinion being lost and an opinion\nbeing featured on the site, which affects the decisions and behavior of people,\nand why researchers were interested in it. Using natural language processing\ntechniques, our aim is to determine and categorize opinions about covid\nvaccines with the highest accuracy possible.\n", "date": "2023-11-08", "categories": []}
{"id": "2311.03584", "title": "Dimensions of Online Conflict: Towards Modeling Agonism", "abstract": "  Agonism plays a vital role in democratic dialogue by fostering diverse\nperspectives and robust discussions. Within the realm of online conflict there\nis another type: hateful antagonism, which undermines constructive dialogue.\nDetecting conflict online is central to platform moderation and monetization.\nIt is also vital for democratic dialogue, but only when it takes the form of\nagonism. To model these two types of conflict, we collected Twitter\nconversations related to trending controversial topics. We introduce a\ncomprehensive annotation schema for labelling different dimensions of conflict\nin the conversations, such as the source of conflict, the target, and the\nrhetorical strategies deployed. Using this schema, we annotated approximately\n4,000 conversations with multiple labels. We then trained both logistic\nregression and transformer-based models on the dataset, incorporating context\nfrom the conversation, including the number of participants and the structure\nof the interactions. Results show that contextual labels are helpful in\nidentifying conflict and make the models robust to variations in topic. Our\nresearch contributes a conceptualization of different dimensions of conflict, a\nrichly annotated dataset, and promising results that can contribute to content\nmoderation.\n", "date": "2023-11-06", "categories": []}
{"id": "2311.03969", "title": "Factoring Hate Speech: A New Annotation Framework to Study Hate Speech\n  in Social Media", "abstract": "  In this work we propose a novel annotation scheme which factors hate speech\ninto five separate discursive categories. To evaluate our scheme, we construct\na corpus of over 2.9M Twitter posts containing hateful expressions directed at\nJews, and annotate a sample dataset of 1,050 tweets. We present a statistical\nanalysis of the annotated dataset as well as discuss annotation examples, and\nconclude by discussing promising directions for future work.\n", "date": "2023-11-07", "categories": []}
{"id": "2311.02570", "title": "BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla", "abstract": "  Initial work has been done to address fake news detection and\nmisrepresentation of news in the Bengali language. However, no work in Bengali\nyet addresses the identification of specific claims in social media news that\nfalsely manipulates a related news article. At this point, this problem has\nbeen tackled in English and a few other languages, but not in the Bengali\nlanguage. In this paper, we curate a dataset of social media content labeled\nwith information manipulation relative to reference articles, called BanMANI.\nThe dataset collection method we describe works around the limitations of the\navailable NLP tools in Bangla. We expect these techniques will carry over to\nbuilding similar datasets in other low-resource languages. BanMANI forms the\nbasis both for evaluating the capabilities of existing NLP systems and for\ntraining or fine-tuning new models specifically on this task. In our analysis,\nwe find that this task challenges current LLMs both under zero-shot and\nfine-tuned settings.\n", "date": "2023-11-05", "categories": []}
{"id": "2311.01817", "title": "Mitigating Framing Bias with Polarity Minimization Loss", "abstract": "  Framing bias plays a significant role in exacerbating political polarization\nby distorting the perception of actual events. Media outlets with divergent\npolitical stances often use polarized language in their reporting of the same\nevent. We propose a new loss function that encourages the model to minimize the\npolarity difference between the polarized input articles to reduce framing\nbias. Specifically, our loss is designed to jointly optimize the model to map\npolarity ends bidirectionally. Our experimental results demonstrate that\nincorporating the proposed polarity minimization loss leads to a substantial\nreduction in framing bias when compared to a BART-based multi-document\nsummarization model. Notably, we find that the effectiveness of this approach\nis most pronounced when the model is trained to minimize the polarity loss\nassociated with informational framing bias (i.e., skewed selection of\ninformation to report).\n", "date": "2023-11-03", "categories": []}
{"id": "2311.00143", "title": "Two-Stage Classifier for Campaign Negativity Detection using Axis\n  Embeddings: A Case Study on Tweets of Political Users during 2021\n  Presidential Election in Iran", "abstract": "  In elections around the world, the candidates may turn their campaigns toward\nnegativity due to the prospect of failure and time pressure. In the digital\nage, social media platforms such as Twitter are rich sources of political\ndiscourse. Therefore, despite the large amount of data that is published on\nTwitter, the automatic system for campaign negativity detection can play an\nessential role in understanding the strategy of candidates and parties in their\ncampaigns. In this paper, we propose a hybrid model for detecting campaign\nnegativity consisting of a two-stage classifier that combines the strengths of\ntwo machine learning models. Here, we have collected Persian tweets from 50\npolitical users, including candidates and government officials. Then we\nannotated 5,100 of them that were published during the year before the 2021\npresidential election in Iran. In the proposed model, first, the required\ndatasets of two classifiers based on the cosine similarity of tweet embeddings\nwith axis embeddings (which are the average of embedding in positive and\nnegative classes of tweets) from the training set (85\\%) are made, and then\nthese datasets are considered the training set of the two classifiers in the\nhybrid model. Finally, our best model (RF-RF) was able to achieve 79\\% for the\nmacro F1 score and 82\\% for the weighted F1 score. By running the best model on\nthe rest of the tweets of 50 political users that were published one year\nbefore the election and with the help of statistical models, we find that the\npublication of a tweet by a candidate has nothing to do with the negativity of\nthat tweet, and the presence of the names of political persons and political\norganizations in the tweet is directly related to its negativity.\n", "date": "2023-10-31", "categories": []}
{"id": "2305.19194", "title": "FakeSwarm: Improving Fake News Detection with Swarming Characteristics", "abstract": "  The proliferation of fake news poses a serious threat to society, as it can\nmisinform and manipulate the public, erode trust in institutions, and undermine\ndemocratic processes. To address this issue, we present FakeSwarm, a fake news\nidentification system that leverages the swarming characteristics of fake news.\nTo extract the swarm behavior, we propose a novel concept of fake news swarming\ncharacteristics and design three types of swarm features, including principal\ncomponent analysis, metric representation, and position encoding. We evaluate\nour system on a public dataset and demonstrate the effectiveness of\nincorporating swarm features in fake news identification, achieving an f1-score\nand accuracy of over 97% by combining all three types of swarm features.\nFurthermore, we design an online learning pipeline based on the hypothesis of\nthe temporal distribution pattern of fake news emergence, validated on a topic\nwith early emerging fake news and a shortage of text samples, showing that\nswarm features can significantly improve recall rates in such cases. Our work\nprovides a new perspective and approach to fake news detection and highlights\nthe importance of considering swarming characteristics in detecting fake news.\n", "date": "2023-05-30", "categories": []}
{"id": "2310.18544", "title": "Discourse Structures Guided Fine-grained Propaganda Identification", "abstract": "  Propaganda is a form of deceptive narratives that instigate or mislead the\npublic, usually with a political purpose. In this paper, we aim to identify\npropaganda in political news at two fine-grained levels: sentence-level and\ntoken-level. We observe that propaganda content is more likely to be embedded\nin sentences that attribute causality or assert contrast to nearby sentences,\nas well as seen in opinionated evaluation, speculation and discussions of\nfuture expectation. Hence, we propose to incorporate both local and global\ndiscourse structures for propaganda discovery and construct two teacher models\nfor identifying PDTB-style discourse relations between nearby sentences and\ncommon discourse roles of sentences in a news article respectively. We further\ndevise two methods to incorporate the two types of discourse structures for\npropaganda identification by either using teacher predicted probabilities as\nadditional features or soliciting guidance in a knowledge distillation\nframework. Experiments on the benchmark dataset demonstrate that leveraging\nguidance from discourse structures can significantly improve both precision and\nrecall of propaganda content identification.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.18768", "title": "Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in\n  News Reporting", "abstract": "  News media is expected to uphold unbiased reporting. Yet they may still\naffect public opinion by selectively including or omitting events that support\nor contradict their ideological positions. Prior work in NLP has only studied\nmedia bias via linguistic style and word usage. In this paper, we study to\nwhich degree media balances news reporting and affects consumers through event\ninclusion or omission. We first introduce the task of detecting both partisan\nand counter-partisan events: events that support or oppose the author's\npolitical ideology. To conduct our study, we annotate a high-quality dataset,\nPAC, containing 8,511 (counter-)partisan event annotations in 304 news articles\nfrom ideologically diverse media outlets. We benchmark PAC to highlight the\nchallenges of this task. Our findings highlight both the ways in which the news\nsubtly shapes opinion and the need for large language models that better\nunderstand events within a broader context. Our dataset can be found at\nhttps://github.com/launchnlp/Partisan-Event-Dataset.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.18827", "title": "All Things Considered: Detecting Partisan Events from News Media with\n  Cross-Article Comparison", "abstract": "  Public opinion is shaped by the information news media provide, and that\ninformation in turn may be shaped by the ideological preferences of media\noutlets. But while much attention has been devoted to media bias via overt\nideological language or topic selection, a more unobtrusive way in which the\nmedia shape opinion is via the strategic inclusion or omission of partisan\nevents that may support one side or the other. We develop a latent\nvariable-based framework to predict the ideology of news articles by comparing\nmultiple articles on the same story and identifying partisan events whose\ninclusion or omission reveals ideology. Our experiments first validate the\nexistence of partisan event selection, and then show that article alignment and\ncross-document comparison detect partisan events and article ideology better\nthan competitive baselines. Our results reveal the high-level form of media\nbias, which is present even among mainstream media with strong norms of\nobjectivity and nonpartisanship. Our codebase and dataset are available at\nhttps://github.com/launchnlp/ATC.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.19267", "title": "Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through\n  Claim Detection and Identification of Claim Spans", "abstract": "  A significant increase in content creation and information exchange has been\nmade possible by the quick development of online social media platforms, which\nhas been very advantageous. However, these platforms have also become a haven\nfor those who disseminate false information, propaganda, and fake news. Claims\nare essential in forming our perceptions of the world, but sadly, they are\nfrequently used to trick people by those who spread false information. To\naddress this problem, social media giants employ content moderators to filter\nout fake news from the actual world. However, the sheer volume of information\nmakes it difficult to identify fake news effectively. Therefore, it has become\ncrucial to automatically identify social media posts that make such claims,\ncheck their veracity, and differentiate between credible and false claims. In\nresponse, we presented CLAIMSCAN in the 2023 Forum for Information Retrieval\nEvaluation (FIRE'2023). The primary objectives centered on two crucial tasks:\nTask A, determining whether a social media post constitutes a claim, and Task\nB, precisely identifying the words or phrases within the post that form the\nclaim. Task A received 40 registrations, demonstrating a strong interest and\nengagement in this timely challenge. Meanwhile, Task B attracted participation\nfrom 28 teams, highlighting its significance in the digital era of\nmisinformation.\n", "date": "2023-10-30", "categories": []}
{"id": "2304.02247", "title": "Disentangling Structure and Style: Political Bias Detection in News by\n  Inducing Document Hierarchy", "abstract": "  We address an important gap in detecting political bias in news articles.\nPrevious works that perform document classification can be influenced by the\nwriting style of each news outlet, leading to overfitting and limited\ngeneralizability. Our approach overcomes this limitation by considering both\nthe sentence-level semantics and the document-level rhetorical structure,\nresulting in a more robust and style-agnostic approach to detecting political\nbias in news articles. We introduce a novel multi-head hierarchical attention\nmodel that effectively encodes the structure of long documents through a\ndiverse ensemble of attention heads. While journalism follows a formalized\nrhetorical structure, the writing style may vary by news outlet. We demonstrate\nthat our method overcomes this domain dependency and outperforms previous\napproaches for robustness and accuracy. Further analysis and human evaluation\ndemonstrate the ability of our model to capture common discourse structures in\njournalism. Our code is available at:\nhttps://github.com/xfactlab/emnlp2023-Document-Hierarchy\n", "date": "2023-04-05", "categories": []}
{"id": "2208.05568", "title": "The emergence of division of labor through decentralized social\n  sanctioning", "abstract": "  Human ecological success relies on our characteristic ability to flexibly\nself-organize into cooperative social groups, the most successful of which\nemploy substantial specialization and division of labor. Unlike most other\nanimals, humans learn by trial and error during their lives what role to take\non. However, when some critical roles are more attractive than others, and\nindividuals are self-interested, then there is a social dilemma: each\nindividual would prefer others take on the critical but unremunerative roles so\nthey may remain free to take one that pays better. But disaster occurs if all\nact thusly and a critical role goes unfilled. In such situations learning an\noptimum role distribution may not be possible. Consequently, a fundamental\nquestion is: how can division of labor emerge in groups of self-interested\nlifetime-learning individuals? Here we show that by introducing a model of\nsocial norms, which we regard as emergent patterns of decentralized social\nsanctioning, it becomes possible for groups of self-interested individuals to\nlearn a productive division of labor involving all critical roles. Such social\nnorms work by redistributing rewards within the population to disincentivize\nantisocial roles while incentivizing prosocial roles that do not intrinsically\npay as well as others.\n", "date": "2022-08-10", "categories": []}
{"id": "2310.16579", "title": "WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming\n  Sentences with Contextualized Social Wisdom", "abstract": "  In recent years, we witness the explosion of false and unconfirmed\ninformation (i.e., rumors) that went viral on social media and shocked the\npublic. Rumors can trigger versatile, mostly controversial stance expressions\namong social media users. Rumor verification and stance detection are different\nyet relevant tasks. Fake news debunking primarily focuses on determining the\ntruthfulness of news articles, which oversimplifies the issue as fake news\noften combines elements of both truth and falsehood. Thus, it becomes crucial\nto identify specific instances of misinformation within the articles. In this\nresearch, we investigate a novel task in the field of fake news debunking,\nwhich involves detecting sentence-level misinformation. One of the major\nchallenges in this task is the absence of a training dataset with\nsentence-level annotations regarding veracity. Inspired by the Multiple\nInstance Learning (MIL) approach, we propose a model called Weakly Supervised\nDetection of Misinforming Sentences (WSDMS). This model only requires bag-level\nlabels for training but is capable of inferring both sentence-level\nmisinformation and article-level veracity, aided by relevant social media\nconversations that are attentively contextualized with news sentences. We\nevaluate WSDMS on three real-world benchmarks and demonstrate that it\noutperforms existing state-of-the-art baselines in debunking fake news at both\nthe sentence and article levels.\n", "date": "2023-10-25", "categories": []}
{"id": "2209.09803", "title": "Spillover of Antisocial Behavior from Fringe Platforms: The Unintended\n  Consequences of Community Banning", "abstract": "  Online platforms face pressure to keep their communities civil and\nrespectful. Thus, the bannings of problematic online communities from\nmainstream platforms like Reddit and Facebook are often met with enthusiastic\npublic reactions. However, this policy can lead users to migrate to alternative\nfringe platforms with lower moderation standards and where antisocial behaviors\nlike trolling and harassment are widely accepted. As users of these communities\noften remain co-active across mainstream and fringe platforms, antisocial\nbehaviors may spill over onto the mainstream platform. We study this possible\nspillover by analyzing around 70,000 users from three banned communities that\nmigrated to fringe platforms: r/The_Donald, r/GenderCritical, and r/Incels.\nUsing a difference-in-differences design, we contrast co-active users with\nmatched counterparts to estimate the causal effect of fringe platform\nparticipation on users' antisocial behavior on Reddit. Our results show that\nparticipating in the fringe communities increases users' toxicity on Reddit (as\nmeasured by Perspective API) and involvement with subreddits similar to the\nbanned community -- which often also breach platform norms. The effect\nintensifies with time and exposure to the fringe platform. In short, we find\nevidence for a spillover of antisocial behavior from fringe platforms onto\nReddit via co-participation.\n", "date": "2022-09-20", "categories": []}
{"id": "2310.15429", "title": "Beyond Sentiment: Leveraging Topic Metrics for Political Stance\n  Classification", "abstract": "  Sentiment analysis, widely critiqued for capturing merely the overall tone of\na corpus, falls short in accurately reflecting the latent structures and\npolitical stances within texts. This study introduces topic metrics, dummy\nvariables converted from extracted topics, as both an alternative and\ncomplement to sentiment metrics in stance classification. By employing three\ndatasets identified by Bestvater and Monroe (2023), this study demonstrates\nBERTopic's proficiency in extracting coherent topics and the effectiveness of\ntopic metrics in stance classification. The experiment results show that\nBERTopic improves coherence scores by 17.07% to 54.20% when compared to\ntraditional approaches such as Dirichlet Allocation (LDA) and Non-negative\nMatrix Factorization (NMF), prevalent in earlier political science research.\nAdditionally, our results indicate topic metrics outperform sentiment metrics\nin stance classification, increasing performance by as much as 18.95%. Our\nfindings suggest topic metrics are especially effective for context-rich texts\nand corpus where stance and sentiment correlations are weak. The combination of\nsentiment and topic metrics achieve an optimal performance in most of the\nscenarios and can further address the limitations of relying solely on\nsentiment as well as the low coherence score of topic metrics.\n", "date": "2023-10-24", "categories": []}
{"id": "2302.07268", "title": "AI Chat Assistants can Improve Conversations about Divisive Topics", "abstract": "  A rapidly increasing amount of human conversation occurs online. But\ndivisiveness and conflict can fester in text-based interactions on social media\nplatforms, in messaging apps, and on other digital forums. Such toxicity\nincreases polarization and, importantly, corrodes the capacity of diverse\nsocieties to develop efficient solutions to complex social problems that impact\neveryone. Scholars and civil society groups promote interventions that can make\ninterpersonal conversations less divisive or more productive in offline\nsettings, but scaling these efforts to the amount of discourse that occurs\nonline is extremely challenging. We present results of a large-scale experiment\nthat demonstrates how online conversations about divisive topics can be\nimproved with artificial intelligence tools. Specifically, we employ a large\nlanguage model to make real-time, evidence-based recommendations intended to\nimprove participants' perception of feeling understood in conversations. We\nfind that these interventions improve the reported quality of the conversation,\nreduce political divisiveness, and improve the tone, without systematically\nchanging the content of the conversation or moving people's policy attitudes.\nThese findings have important implications for future research on social media,\npolitical deliberation, and the growing community of scholars interested in the\nplace of artificial intelligence within computational social science.\n", "date": "2023-02-14", "categories": []}
{"id": "2305.14492", "title": "Sociocultural Norm Similarities and Differences via Situational\n  Alignment and Explainable Textual Entailment", "abstract": "  Designing systems that can reason across cultures requires that they are\ngrounded in the norms of the contexts in which they operate. However, current\nresearch on developing computational models of social norms has primarily\nfocused on American society. Here, we propose a novel approach to discover and\ncompare descriptive social norms across Chinese and American cultures. We\ndemonstrate our approach by leveraging discussions on a Chinese Q&A platform\n(Zhihu) and the existing SocialChemistry dataset as proxies for contrasting\ncultural axes, align social situations cross-culturally, and extract social\nnorms from texts using in-context learning. Embedding Chain-of-Thought\nprompting in a human-AI collaborative framework, we build a high-quality\ndataset of 3,069 social norms aligned with social situations across Chinese and\nAmerican cultures alongside corresponding free-text explanations. To test the\nability of models to reason about social norms across cultures, we introduce\nthe task of explainable social norm entailment, showing that existing models\nunder 3B parameters have significant room for improvement in both automatic and\nhuman evaluation. Further analysis of cross-cultural norm differences based on\nour dataset shows empirical alignment with the social orientations framework,\nrevealing several situational and descriptive nuances in norms across these\ncultures.\n", "date": "2023-05-23", "categories": []}
{"id": "2310.14595", "title": "Online Auditing of Information Flow", "abstract": "  Modern social media platforms play an important role in facilitating rapid\ndissemination of information through their massive user networks. Fake news,\nmisinformation, and unverifiable facts on social media platforms propagate\ndisharmony and affect society. In this paper, we consider the problem of online\nauditing of information flow/propagation with the goal of classifying news\nitems as fake or genuine. Specifically, driven by experiential studies on\nreal-world social media platforms, we propose a probabilistic Markovian\ninformation spread model over networks modeled by graphs. We then formulate our\ninference task as a certain sequential detection problem with the goal of\nminimizing the combination of the error probability and the time it takes to\nachieve correct decision. For this model, we find the optimal detection\nalgorithm minimizing the aforementioned risk and prove several statistical\nguarantees. We then test our algorithm over real-world datasets. To that end,\nwe first construct an offline algorithm for learning the probabilistic\ninformation spreading model, and then apply our optimal detection algorithm.\nExperimental study show that our algorithm outperforms state-of-the-art\nmisinformation detection algorithms in terms of accuracy and detection time.\n", "date": "2023-10-23", "categories": []}
{"id": "2305.02321", "title": "Entity-Based Evaluation of Political Bias in Automatic Summarization", "abstract": "  Growing literature has shown that NLP systems may encode social biases;\nhowever, the political bias of summarization models remains relatively unknown.\nIn this work, we use an entity replacement method to investigate the portrayal\nof politicians in automatically generated summaries of news articles. We\ndevelop an entity-based computational framework to assess the sensitivities of\nseveral extractive and abstractive summarizers to the politicians Donald Trump\nand Joe Biden. We find consistent differences in these summaries upon entity\nreplacement, such as reduced emphasis of Trump's presence in the context of the\nsame article and a more individualistic representation of Trump with respect to\nthe collective US government (i.e., administration). These summary\ndissimilarities are most prominent when the entity is heavily featured in the\nsource article. Our characterization provides a foundation for future studies\nof bias in summarization and for normative discussions on the ideal qualities\nof automatic summaries.\n", "date": "2023-05-03", "categories": []}
{"id": "2310.12186", "title": "Stranger Danger! Cross-Community Interactions with Fringe Users Increase\n  the Growth of Fringe Communities on Reddit", "abstract": "  Fringe communities promoting conspiracy theories and extremist ideologies\nhave thrived on mainstream platforms, raising questions about the mechanisms\ndriving their growth. Here, we hypothesize and study a possible mechanism: new\nmembers may be recruited through fringe-interactions: the exchange of comments\nbetween members and non-members of fringe communities. We apply text-based\ncausal inference techniques to study the impact of fringe-interactions on the\ngrowth of three prominent fringe communities on Reddit: r/Incel,\nr/GenderCritical, and r/The_Donald. Our results indicate that\nfringe-interactions attract new members to fringe communities. Users who\nreceive these interactions are up to 4.2 percentage points (pp) more likely to\njoin fringe communities than similar, matched users who do not.\n  This effect is influenced by 1) the characteristics of communities where the\ninteraction happens (e.g., left vs. right-leaning communities) and 2) the\nlanguage used in the interactions. Interactions using toxic language have a 5pp\nhigher chance of attracting newcomers to fringe communities than non-toxic\ninteractions. We find no effect when repeating this analysis by replacing\nfringe (r/Incel, r/GenderCritical, and r/The_Donald) with non-fringe\ncommunities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth\nmechanism is specific to fringe communities. Overall, our findings suggest that\ncurtailing fringe-interactions may reduce the growth of fringe communities on\nmainstream platforms.\n", "date": "2023-10-18", "categories": []}
{"id": "2310.12575", "title": "Multilingual estimation of political-party positioning: From label\n  aggregation to long-input Transformers", "abstract": "  Scaling analysis is a technique in computational political science that\nassigns a political actor (e.g. politician or party) a score on a predefined\nscale based on a (typically long) body of text (e.g. a parliamentary speech or\nan election manifesto). For example, political scientists have often used the\nleft--right scale to systematically analyse political landscapes of different\ncountries. NLP methods for automatic scaling analysis can find broad\napplication provided they (i) are able to deal with long texts and (ii) work\nrobustly across domains and languages. In this work, we implement and compare\ntwo approaches to automatic scaling analysis of political-party manifestos:\nlabel aggregation, a pipeline strategy relying on annotations of individual\nstatements from the manifestos, and long-input-Transformer-based models, which\ncompute scaling values directly from raw text. We carry out the analysis of the\nComparative Manifestos Project dataset across 41 countries and 27 languages and\nfind that the task can be efficiently solved by state-of-the-art models, with\nlabel aggregation producing the best results.\n", "date": "2023-10-19", "categories": []}
{"id": "2310.09166", "title": "Developing a Natural Language Understanding Model to Characterize Cable\n  News Bias", "abstract": "  Media bias has been extensively studied by both social and computational\nsciences. However, current work still has a large reliance on human input and\nsubjective assessment to label biases. This is especially true for cable news\nresearch. To address these issues, we develop an unsupervised machine learning\nmethod to characterize the bias of cable news programs without any human input.\nThis method relies on the analysis of what topics are mentioned through Named\nEntity Recognition and how those topics are discussed through Stance Analysis\nin order to cluster programs with similar biases together. Applying our method\nto 2020 cable news transcripts, we find that program clusters are consistent\nover time and roughly correspond to the cable news network of the program. This\nmethod reveals the potential for future tools to objectively assess media bias\nand characterize unfamiliar media environments.\n", "date": "2023-10-13", "categories": []}
{"id": "2310.09618", "title": "Moral consensus and divergence in partisan language use", "abstract": "  Polarization has increased substantially in political discourse, contributing\nto a widening partisan divide. In this paper, we analyzed large-scale,\nreal-world language use in Reddit communities (294,476,146 comments) and in\nnews outlets (6,749,781 articles) to uncover psychological dimensions along\nwhich partisan language is divided. Using word embedding models that captured\nsemantic associations based on co-occurrences of words in vast textual corpora,\nwe identified patterns of affective polarization present in natural political\ndiscourse. We then probed the semantic associations of words related to seven\npolitical topics (e.g., abortion, immigration) along the dimensions of morality\n(moral-to-immoral), threat (threatening-to-safe), and valence\n(pleasant-to-unpleasant). Across both Reddit communities and news outlets, we\nidentified a small but systematic divergence in the moral associations of words\nbetween text sources with different partisan leanings. Moral associations of\nwords were highly correlated between conservative and liberal text sources\n(average $\\rho$ = 0.96), but the differences remained reliable to enable us to\ndistinguish text sources along partisan lines with above 85% classification\naccuracy. These findings underscore that despite a shared moral understanding\nacross the political spectrum, there are consistent differences that shape\npartisan language and potentially exacerbate political polarization. Our\nresults, drawn from both informal interactions on social media and curated\nnarratives in news outlets, indicate that these trends are widespread.\nLeveraging advanced computational techniques, this research offers a fresh\nperspective that complements traditional methods in political attitudes.\n", "date": "2023-10-14", "categories": []}
{"id": "2210.08786", "title": "Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI\n  Approach to Detecting State-Sponsored Trolls", "abstract": "  The detection of state-sponsored trolls operating in influence campaigns on\nsocial media is a critical and unsolved challenge for the research community,\nwhich has significant implications beyond the online realm. To address this\nchallenge, we propose a new AI-based solution that identifies troll accounts\nsolely through behavioral cues associated with their sequences of sharing\nactivity, encompassing both their actions and the feedback they receive from\nothers. Our approach does not incorporate any textual content shared and\nconsists of two steps: First, we leverage an LSTM-based classifier to determine\nwhether account sequences belong to a state-sponsored troll or an organic,\nlegitimate user. Second, we employ the classified sequences to calculate a\nmetric named the \"Troll Score\", quantifying the degree to which an account\nexhibits troll-like behavior. To assess the effectiveness of our method, we\nexamine its performance in the context of the 2016 Russian interference\ncampaign during the U.S. Presidential election. Our experiments yield\ncompelling results, demonstrating that our approach can identify account\nsequences with an AUC close to 99% and accurately differentiate between Russian\ntrolls and organic users with an AUC of 91%. Notably, our behavioral-based\napproach holds a significant advantage in the ever-evolving landscape, where\ntextual and linguistic properties can be easily mimicked by Large Language\nModels (LLMs): In contrast to existing language-based techniques, it relies on\nmore challenging-to-replicate behavioral cues, ensuring greater resilience in\nidentifying influence campaigns, especially given the potential increase in the\nusage of LLMs for generating inauthentic content. Finally, we assessed the\ngeneralizability of our solution to various entities driving different\ninformation operations and found promising results that will guide future\nresearch.\n", "date": "2022-10-17", "categories": []}
{"id": "2307.10025", "title": "An Empirical Study on Fertility Proposals Using Multi-Grained Topic\n  Analysis Methods", "abstract": "  Fertility issues are closely related to population security, in 60 years\nChina's population for the first time in a negative growth trend, the change of\nfertility policy is of great concern to the community. 2023 \"two sessions\"\nproposal \"suggests that the country in the form of legislation, the birth of\nthe registration of the cancellation of the marriage restriction\" This topic\nwas once a hot topic on the Internet, and \"unbundling\" the relationship between\nbirth registration and marriage has become the focus of social debate. In this\npaper, we adopt co-occurrence semantic analysis, topic analysis and sentiment\nanalysis to conduct multi-granularity semantic analysis of microblog comments.\nIt is found that the discussion on the proposal of \"removing marriage\nrestrictions from birth registration\" involves the individual, society and the\nstate at three dimensions, and is detailed into social issues such as personal\nbehaviour, social ethics and law, and national policy, with people's sentiment\ninclined to be negative in most of the topics. Based on this, eight proposals\nwere made to provide a reference for governmental decision making and to form a\nreference method for researching public opinion on political issues.\n", "date": "2023-07-19", "categories": []}
{"id": "2309.15827", "title": "How We Define Harm Impacts Data Annotations: Explaining How Annotators\n  Distinguish Hateful, Offensive, and Toxic Comments", "abstract": "  Computational social science research has made advances in machine learning\nand natural language processing that support content moderators in detecting\nharmful content. These advances often rely on training datasets annotated by\ncrowdworkers for harmful content. In designing instructions for annotation\ntasks to generate training data for these algorithms, researchers often treat\nthe harm concepts that we train algorithms to detect - 'hateful', 'offensive',\n'toxic', 'racist', 'sexist', etc. - as interchangeable. In this work, we\nstudied whether the way that researchers define 'harm' affects annotation\noutcomes. Using Venn diagrams, information gain comparisons, and content\nanalyses, we reveal that annotators do not use the concepts 'hateful',\n'offensive', and 'toxic' interchangeably. We identify that features of harm\ndefinitions and annotators' individual characteristics explain much of how\nannotators use these terms differently. Our results offer empirical evidence\ndiscouraging the common practice of using harm concepts interchangeably in\ncontent moderation research. Instead, researchers should make specific choices\nabout which harm concepts to analyze based on their research goals. Recognizing\nthat researchers are often resource constrained, we also encourage researchers\nto provide information to bound their findings when their concepts of interest\ndiffer from concepts that off-the-shelf harmful content detection algorithms\nidentify. Finally, we encourage algorithm providers to ensure their instruments\ncan adapt to contextually-specific content detection goals (e.g., soliciting\ninstrument users' feedback).\n", "date": "2023-09-12", "categories": []}
{"id": "2309.12357", "title": "Mapping AI Arguments in Journalism Studies", "abstract": "  This study investigates and suggests typologies for examining Artificial\nIntelligence (AI) within the domains of journalism and mass communication\nresearch. We aim to elucidate the seven distinct subfields of AI, which\nencompass machine learning, natural language processing (NLP), speech\nrecognition, expert systems, planning, scheduling, optimization, robotics, and\ncomputer vision, through the provision of concrete examples and practical\napplications. The primary objective is to devise a structured framework that\ncan help AI researchers in the field of journalism. By comprehending the\noperational principles of each subfield, scholars can enhance their ability to\nfocus on a specific facet when analyzing a particular research topic.\n", "date": "2023-09-03", "categories": []}
{"id": "2301.08104", "title": "Author as Character and Narrator: Deconstructing Personal Narratives\n  from the r/AmITheAsshole Reddit Community", "abstract": "  In the r/AmITheAsshole subreddit, people anonymously share first person\nnarratives that contain some moral dilemma or conflict and ask the community to\njudge who is at fault (i.e., who is \"the asshole\"). In general, first person\nnarratives are a unique storytelling domain where the author is the narrator\n(the person telling the story) but can also be a character (the person living\nthe story) and, thus, the author has two distinct voices presented in the\nstory. In this study, we identify linguistic and narrative features associated\nwith the author as the character or as a narrator. We use these features to\nanswer the following questions: (1) what makes an asshole character and (2)\nwhat makes an asshole narrator? We extract both Author-as-Character features\n(e.g., demographics, narrative event chain, and emotional arc) and\nAuthor-as-Narrator features (i.e., the style and emotion of the story as a\nwhole) in order to identify which aspects of the narrative are correlated with\nthe final moral judgment. Our work shows that \"assholes\" as Characters frame\nthemselves as lacking agency with a more positive personal arc, while\n\"assholes\" as Narrators will tell emotional and opinionated stories.\n", "date": "2023-01-19", "categories": []}
{"id": "2309.12117", "title": "How-to Guides for Specific Audiences: A Corpus and Initial Findings", "abstract": "  Instructional texts for specific target groups should ideally take into\naccount the prior knowledge and needs of the readers in order to guide them\nefficiently to their desired goals. However, targeting specific groups also\ncarries the risk of reflecting disparate social norms and subtle stereotypes.\nIn this paper, we investigate the extent to which how-to guides from one\nparticular platform, wikiHow, differ in practice depending on the intended\naudience. We conduct two case studies in which we examine qualitative features\nof texts written for specific audiences. In a generalization study, we\ninvestigate which differences can also be systematically demonstrated using\ncomputational methods. The results of our studies show that guides from\nwikiHow, like other text genres, are subject to subtle biases. We aim to raise\nawareness of these inequalities as a first step to addressing them in future\nwork.\n", "date": "2023-09-21", "categories": []}
{"id": "2309.09787", "title": "Harnessing Collective Intelligence Under a Lack of Cultural Consensus", "abstract": "  Harnessing collective intelligence to drive effective decision-making and\ncollaboration benefits from the ability to detect and characterize\nheterogeneity in consensus beliefs. This is particularly true in domains such\nas technology acceptance or leadership perception, where a consensus defines an\nintersubjective truth, leading to the possibility of multiple \"ground truths\"\nwhen subsets of respondents sustain mutually incompatible consensuses. Cultural\nConsensus Theory (CCT) provides a statistical framework for detecting and\ncharacterizing these divergent consensus beliefs. However, it is unworkable in\nmodern applications because it lacks the ability to generalize across even\nhighly similar beliefs, is ineffective with sparse data, and can leverage\nneither external knowledge bases nor learned machine representations. Here, we\novercome these limitations through Infinite Deep Latent Construct Cultural\nConsensus Theory (iDLC-CCT), a nonparametric Bayesian model that extends CCT\nwith a latent construct that maps between pretrained deep neural network\nembeddings of entities and the consensus beliefs regarding those entities among\none or more subsets of respondents. We validate the method across domains\nincluding perceptions of risk sources, food healthiness, leadership, first\nimpressions, and humor. We find that iDLC-CCT better predicts the degree of\nconsensus, generalizes well to out-of-sample entities, and is effective even\nwith sparse data. To improve scalability, we introduce an efficient\nhard-clustering variant of the iDLC-CCT using an algorithm derived from a\nsmall-variance asymptotic analysis of the model. The iDLC-CCT, therefore,\nprovides a workable computational foundation for harnessing collective\nintelligence under a lack of cultural consensus and may potentially form the\nbasis of consensus-aware information technologies.\n", "date": "2023-09-18", "categories": []}
{"id": "2309.08845", "title": "Has Sentiment Returned to the Pre-pandemic Level? A Sentiment Analysis\n  Using U.S. College Subreddit Data from 2019 to 2022", "abstract": "  As impact of COVID-19 pandemic winds down, both individuals and society\ngradually return to pre-pandemic activities. This study aims to explore how\npeople's emotions have changed from the pre-pandemic during the pandemic to\npost-emergency period and whether it has returned to pre-pandemic level. We\ncollected Reddit data in 2019 (pre-pandemic), 2020 (peak pandemic), 2021, and\n2022 (late stages of pandemic, transitioning period to post-emergency period)\nfrom subreddits in 128 universities/colleges in the U.S., and a set of\nschool-level characteristics. We predicted two sets of sentiments from a\npre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and graph\nattention network (GAT) that leverages both rich semantic and relational\ninformation among posted messages and then applied a logistic stacking method\nto obtain the final sentiment classification. After obtaining sentiment label\nfor each message, we used a generalized linear mixed-effects model to estimate\ntemporal trend in sentiment from 2019 to 2022 and how school-level factors may\naffect sentiment. Compared to the year 2019, the odds of negative sentiment in\nyears 2020, 2021, and 2022 are 24%, 4.3%, and 10.3% higher, respectively, which\nare all statistically significant(adjusted $p$<0.05). Our study findings\nsuggest a partial recovery in the sentiment composition in the\npost-pandemic-emergency era. The results align with common expectations and\nprovide a detailed quantification of how sentiments have evolved from 2019 to\n2022.\n", "date": "2023-09-16", "categories": []}
{"id": "2309.09120", "title": "Public Perceptions of Gender Bias in Large Language Models: Cases of\n  ChatGPT and Ernie", "abstract": "  Large language models are quickly gaining momentum, yet are found to\ndemonstrate gender bias in their responses. In this paper, we conducted a\ncontent analysis of social media discussions to gauge public perceptions of\ngender bias in LLMs which are trained in different cultural contexts, i.e.,\nChatGPT, a US-based LLM, or Ernie, a China-based LLM. People shared both\nobservations of gender bias in their personal use and scientific findings about\ngender bias in LLMs. A difference between the two LLMs was seen -- ChatGPT was\nmore often found to carry implicit gender bias, e.g., associating men and women\nwith different profession titles, while explicit gender bias was found in\nErnie's responses, e.g., overly promoting women's pursuit of marriage over\ncareer. Based on the findings, we reflect on the impact of culture on gender\nbias and propose governance recommendations to regulate gender bias in LLMs.\n", "date": "2023-09-17", "categories": []}
{"id": "2309.09274", "title": "Leveraging Social Discourse to Measure Check-worthiness of Claims for\n  Fact-checking", "abstract": "  The expansion of online social media platforms has led to a surge in online\ncontent consumption. However, this has also paved the way for disseminating\nfalse claims and misinformation. As a result, there is an escalating demand for\na substantial workforce to sift through and validate such unverified claims.\nCurrently, these claims are manually verified by fact-checkers. Still, the\nvolume of online content often outweighs their potency, making it difficult for\nthem to validate every single claim in a timely manner. Thus, it is critical to\ndetermine which assertions are worth fact-checking and prioritize claims that\nrequire immediate attention. Multiple factors contribute to determining whether\na claim necessitates fact-checking, encompassing factors such as its factual\ncorrectness, potential impact on the public, the probability of inciting\nhatred, and more. Despite several efforts to address claim check-worthiness, a\nsystematic approach to identify these factors remains an open challenge. To\nthis end, we introduce a new task of fine-grained claim check-worthiness, which\nunderpins all of these factors and provides probable human grounds for\nidentifying a claim as check-worthy. We present CheckIt, a manually annotated\nlarge Twitter dataset for fine-grained claim check-worthiness. We benchmark our\ndataset against a unified approach, CheckMate, that jointly determines whether\na claim is check-worthy and the factors that led to that conclusion. We compare\nour suggested system with several baseline systems. Finally, we report a\nthorough analysis of results and human assessment, validating the efficacy of\nintegrating check-worthiness factors in detecting claims worth fact-checking.\n", "date": "2023-09-17", "categories": []}
{"id": "2309.09324", "title": "How People Perceive The Dynamic Zero-COVID Policy: A Retrospective\n  Analysis From The Perspective of Appraisal Theory", "abstract": "  The Dynamic Zero-COVID Policy in China spanned three years and diverse\nemotional responses have been observed at different times. In this paper, we\nretrospectively analyzed public sentiments and perceptions of the policy,\nespecially regarding how they evolved over time, and how they related to\npeople's lived experiences. Through sentiment analysis of 2,358 collected Weibo\nposts, we identified four representative points, i.e., policy initialization,\nsharp sentiment change, lowest sentiment score, and policy termination, for an\nin-depth discourse analysis through the lens of appraisal theory. In the end,\nwe reflected on the evolving public sentiments toward the Dynamic Zero-COVID\nPolicy and proposed implications for effective epidemic prevention and control\nmeasures for future crises.\n", "date": "2023-09-17", "categories": []}
{"id": "2309.09508", "title": "Understanding Divergent Framing of the Supreme Court Controversies:\n  Social Media vs. News Outlets", "abstract": "  Understanding the framing of political issues is of paramount importance as\nit significantly shapes how individuals perceive, interpret, and engage with\nthese matters. While prior research has independently explored framing within\nnews media and by social media users, there remains a notable gap in our\ncomprehension of the disparities in framing political issues between these two\ndistinct groups. To address this gap, we conduct a comprehensive investigation,\nfocusing on the nuanced distinctions both qualitatively and quantitatively in\nthe framing of social media and traditional media outlets concerning a series\nof American Supreme Court rulings on affirmative action, student loans, and\nabortion rights. Our findings reveal that, while some overlap in framing exists\nbetween social media and traditional media outlets, substantial differences\nemerge both across various topics and within specific framing categories.\nCompared to traditional news media, social media platforms tend to present more\npolarized stances across all framing categories. Further, we observe\nsignificant polarization in the news media's treatment (i.e., Left vs. Right\nleaning media) of affirmative action and abortion rights, whereas the topic of\nstudent loans tends to exhibit a greater degree of consensus. The disparities\nin framing between traditional and social media platforms carry significant\nimplications for the formation of public opinion, policy decision-making, and\nthe broader political landscape.\n", "date": "2023-09-18", "categories": []}
{"id": "2307.10475", "title": "Findings of Factify 2: Multimodal Fake News Detection", "abstract": "  With social media usage growing exponentially in the past few years, fake\nnews has also become extremely prevalent. The detrimental impact of fake news\nemphasizes the need for research focused on automating the detection of false\ninformation and verifying its accuracy. In this work, we present the outcome of\nthe Factify 2 shared task, which provides a multi-modal fact verification and\nsatire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data\ncalls for a comparison based approach to the task by pairing social media\nclaims with supporting documents, with both text and image, divided into 5\nclasses based on multi-modal relations. In the second iteration of this task we\nhad over 60 participants and 9 final test-set submissions. The best\nperformances came from the use of DeBERTa for text and Swinv2 and CLIP for\nimage. The highest F1 score averaged for all five classes was 81.82%.\n", "date": "2023-07-19", "categories": []}
{"id": "2309.06557", "title": "Unsupervised Bias Detection in College Student Newspapers", "abstract": "  This paper presents a pipeline with minimal human influence for scraping and\ndetecting bias on college newspaper archives. This paper introduces a framework\nfor scraping complex archive sites that automated tools fail to grab data from,\nand subsequently generates a dataset of 14 student papers with 23,154 entries.\nThis data can also then be queried by keyword to calculate bias by comparing\nthe sentiment of a large language model summary to the original article. The\nadvantages of this approach are that it is less comparative than reconstruction\nbias and requires less labelled data than generating keyword sentiment. Results\nare calculated on politically charged words as well as control words to show\nhow conclusions can be drawn. The complete method facilitates the extraction of\nnuanced insights with minimal assumptions and categorizations, paving the way\nfor a more objective understanding of bias within student newspaper sources.\n", "date": "2023-09-11", "categories": []}
{"id": "2309.05981", "title": "Learning Unbiased News Article Representations: A Knowledge-Infused\n  Approach", "abstract": "  Quantification of the political leaning of online news articles can aid in\nunderstanding the dynamics of political ideology in social groups and measures\nto mitigating them. However, predicting the accurate political leaning of a\nnews article with machine learning models is a challenging task. This is due to\n(i) the political ideology of a news article is defined by several factors, and\n(ii) the innate nature of existing learning models to be biased with the\npolitical bias of the news publisher during the model training. There is only a\nlimited number of methods to study the political leaning of news articles which\nalso do not consider the algorithmic political bias which lowers the\ngeneralization of machine learning models to predict the political leaning of\nnews articles published by any new news publishers. In this work, we propose a\nknowledge-infused deep learning model that utilizes relatively reliable\nexternal data resources to learn unbiased representations of news articles\nusing their global and local contexts. We evaluate the proposed model by\nsetting the data in such a way that news domains or news publishers in the test\nset are completely unseen during the training phase. With this setup we show\nthat the proposed model mitigates algorithmic political bias and outperforms\nbaseline methods to predict the political leaning of news articles with up to\n73% accuracy.\n", "date": "2023-09-12", "categories": []}
{"id": "2309.06112", "title": "Characterizing Latent Perspectives of Media Houses Towards Public\n  Figures", "abstract": "  Media houses reporting on public figures, often come with their own biases\nstemming from their respective worldviews. A characterization of these\nunderlying patterns helps us in better understanding and interpreting news\nstories. For this, we need diverse or subjective summarizations, which may not\nbe amenable for classifying into predefined class labels. This work proposes a\nzero-shot approach for non-extractive or generative characterizations of person\nentities from a corpus using GPT-2. We use well-articulated articles from\nseveral well-known news media houses as a corpus to build a sound argument for\nthis approach. First, we fine-tune a GPT-2 pre-trained language model with a\ncorpus where specific person entities are characterized. Second, we further\nfine-tune this with demonstrations of person entity characterizations, created\nfrom a corpus of programmatically constructed characterizations. This twice\nfine-tuned model is primed with manual prompts consisting of entity names that\nwere not previously encountered in the second fine-tuning, to generate a simple\nsentence about the entity. The results were encouraging, when compared against\nactual characterizations from the corpus.\n", "date": "2023-09-12", "categories": []}
{"id": "2307.06954", "title": "ACTI at EVALITA 2023: Overview of the Conspiracy Theory Identification\n  Task", "abstract": "  Conspiracy Theory Identication task is a new shared task proposed for the\nfirst time at the Evalita 2023. The ACTI challenge, based exclusively on\ncomments published on conspiratorial channels of telegram, is divided into two\nsubtasks: (i) Conspiratorial Content Classification: identifying conspiratorial\ncontent and (ii) Conspiratorial Category Classification about specific\nconspiracy theory classification. A total of fifteen teams participated in the\ntask for a total of 81 submissions. We illustrate the best performing\napproaches were based on the utilization of large language models. We finally\ndraw conclusions about the utilization of these models for counteracting the\nspreading of misinformation in online platforms.\n", "date": "2023-07-12", "categories": []}
{"id": "2309.00639", "title": "Misinformation Concierge: A Proof-of-Concept with Curated Twitter\n  Dataset on COVID-19 Vaccination", "abstract": "  We demonstrate the Misinformation Concierge, a proof-of-concept that provides\nactionable intelligence on misinformation prevalent in social media.\nSpecifically, it uses language processing and machine learning tools to\nidentify subtopics of discourse and discern non/misleading posts; presents\nstatistical reports for policy-makers to understand the big picture of\nprevalent misinformation in a timely manner; and recommends rebuttal messages\nfor specific pieces of misinformation, identified from within the corpus of\ndata - providing means to intervene and counter misinformation promptly. The\nMisinformation Concierge proof-of-concept using a curated dataset is accessible\nat: https://demo-frontend-uy34.onrender.com/\n", "date": "2023-08-25", "categories": []}
{"id": "2309.01659", "title": "Evolving linguistic divergence on polarizing social media", "abstract": "  Language change is influenced by many factors, but often starts from\nsynchronic variation, where multiple linguistic patterns or forms coexist, or\nwhere different speech communities use language in increasingly different ways.\nBesides regional or economic reasons, communities may form and segregate based\non political alignment. The latter, referred to as political polarization, is\nof growing societal concern across the world. Here we map and quantify\nlinguistic divergence across the partisan left-right divide in the United\nStates, using social media data. We develop a general methodology to delineate\n(social) media users by their political preference, based on which (potentially\nbiased) news media accounts they do and do not follow on a given platform. Our\ndata consists of 1.5M short posts by 10k users (about 20M words) from the\nsocial media platform Twitter (now \"X\"). Delineating this sample involved\nmining the platform for the lists of followers (n=422M) of 72 large news media\naccounts. We quantify divergence in topics of conversation and word\nfrequencies, messaging sentiment, and lexical semantics of words and emoji. We\nfind signs of linguistic divergence across all these aspects, especially in\ntopics and themes of conversation, in line with previous research. While US\nAmerican English remains largely intelligible within its large speech\ncommunity, our findings point at areas where miscommunication may eventually\narise given ongoing polarization and therefore potential linguistic divergence.\nOur methodology - combining data mining, lexicostatistics, machine learning,\nlarge language models and a systematic human annotation approach - is largely\nlanguage and platform agnostic. In other words, while we focus here on US\npolitical divides and US English, the same approach is applicable to other\ncountries, languages, and social media platforms.\n", "date": "2023-09-04", "categories": []}
{"id": "2308.15491", "title": "Detecting Inactive Cyberwarriors from Online Forums", "abstract": "  The proliferation of misinformation has emerged as a new form of warfare in\nthe information age. This type of warfare involves cyberwarriors, who\ndeliberately propagate messages aimed at defaming opponents or fostering unity\namong allies. In this study, we investigate the level of activity exhibited by\ncyberwarriors within a large online forum, and remarkably, we discover that\nonly a minute fraction of cyberwarriors are active users. Surprisingly, despite\ntheir expected role of actively disseminating misinformation, cyberwarriors\nremain predominantly silent during peacetime and only spring into action when\nnecessary. Moreover, we analyze the challenges associated with identifying\ncyberwarriors and provide evidence that detecting inactive cyberwarriors is\nconsiderably more challenging than identifying their active counterparts.\nFinally, we discuss potential methodologies to more effectively identify\ncyberwarriors during their inactive phases, offering insights into better\ncapturing their presence and actions. The experimental code is released for\nreproducibility:\n\\url{https://github.com/Ryaninthegame/Detect-Inactive-Spammers-on-PTT}.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.14873", "title": "CommunityFish: A Poisson-based Document Scaling With Hierarchical\n  Clustering", "abstract": "  Document scaling has been a key component in text-as-data applications for\nsocial scientists and a major field of interest for political researchers, who\naim at uncovering differences between speakers or parties with the help of\ndifferent probabilistic and non-probabilistic approaches. Yet, most of these\ntechniques are either built upon the agnostically bag-of-word hypothesis or use\nprior information borrowed from external sources that might embed the results\nwith a significant bias. If the corpus has long been considered as a collection\nof documents, it can also be seen as a dense network of connected words whose\nstructure could be clustered to differentiate independent groups of words,\nbased on their co-occurrences in documents, known as communities. This paper\nintroduces CommunityFish as an augmented version of Wordfish based on a\nhierarchical clustering, namely the Louvain algorithm, on the word space to\nyield communities as semantic and independent n-grams emerging from the corpus\nand use them as an input to Wordfish method, instead of considering the word\nspace. This strategy emphasizes the interpretability of the results, since\ncommunities have a non-overlapping structure, hence a crucial informative power\nin discriminating parties or speakers, in addition to allowing a faster\nexecution of the Poisson scaling model. Aside from yielding communities,\nassumed to be subtopic proxies, the application of this technique outperforms\nthe classic Wordfish model by highlighting historical developments in the U.S.\nState of the Union addresses and was found to replicate the prevailing\npolitical stance in Germany when using the corpus of parties' legislative\nmanifestos.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.15352", "title": "Historical patterns of rice farming explain modern-day language use in\n  China and Japan more than modernization and urbanization", "abstract": "  We used natural language processing to analyze a billion words to study\ncultural differences on Weibo, one of China's largest social media platforms.\nWe compared predictions from two common explanations about cultural differences\nin China (economic development and urban-rural differences) against the\nless-obvious legacy of rice versus wheat farming. Rice farmers had to\ncoordinate shared irrigation networks and exchange labor to cope with higher\nlabor requirements. In contrast, wheat relied on rainfall and required half as\nmuch labor. We test whether this legacy made southern China more\ninterdependent. Across all word categories, rice explained twice as much\nvariance as economic development and urbanization. Rice areas used more words\nreflecting tight social ties, holistic thought, and a cautious, prevention\norientation. We then used Twitter data comparing prefectures in Japan, which\nlargely replicated the results from China. This provides crucial evidence of\nthe rice theory in a different nation, language, and platform.\n", "date": "2023-08-29", "categories": []}
{"id": "2307.02758", "title": "Exploring Linguistic Style Matching in Online Communities: The Role of\n  Social Context and Conversation Dynamics", "abstract": "  Linguistic style matching (LSM) in conversations can be reflective of several\naspects of social influence such as power or persuasion. However, how LSM\nrelates to the outcomes of online communication on platforms such as Reddit is\nan unknown question. In this study, we analyze a large corpus of two-party\nconversation threads in Reddit where we identify all occurrences of LSM using\ntwo types of style: the use of function words and formality. Using this\nframework, we examine how levels of LSM differ in conversations depending on\nseveral social factors within Reddit: post and subreddit features, conversation\ndepth, user tenure, and the controversiality of a comment. Finally, we measure\nthe change of LSM following loss of status after community banning. Our\nfindings reveal the interplay of LSM in Reddit conversations with several\ncommunity metrics, suggesting the importance of understanding conversation\nengagement when understanding community dynamics.\n", "date": "2023-07-06", "categories": []}
{"id": "2308.14486", "title": "Rebalancing Social Feed to Minimize Polarization and Disagreement", "abstract": "  Social media have great potential for enabling public discourse on important\nsocietal issues. However, adverse effects, such as polarization and echo\nchambers, greatly impact the benefits of social media and call for algorithms\nthat mitigate these effects. In this paper, we propose a novel problem\nformulation aimed at slightly nudging users' social feeds in order to strike a\nbalance between relevance and diversity, thus mitigating the emergence of\npolarization, without lowering the quality of the feed. Our approach is based\non re-weighting the relative importance of the accounts that a user follows, so\nas to calibrate the frequency with which the content produced by various\naccounts is shown to the user. We analyze the convexity properties of the\nproblem, demonstrating the non-matrix convexity of the objective function and\nthe convexity of the feasible set. To efficiently address the problem, we\ndevelop a scalable algorithm based on projected gradient descent. We also prove\nthat our problem statement is a proper generalization of the undirected-case\nproblem so that our method can also be adopted for undirected social networks.\nAs a baseline for comparison in the undirected case, we develop a semidefinite\nprogramming approach, which provides the optimal solution. Through extensive\nexperiments on synthetic and real-world datasets, we validate the effectiveness\nof our approach, which outperforms non-trivial baselines, underscoring its\nability to foster healthier and more cohesive online communities.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.12497", "title": "False Information, Bots and Malicious Campaigns: Demystifying Elements\n  of Social Media Manipulations", "abstract": "  The rapid spread of false information and persistent manipulation attacks on\nonline social networks (OSNs), often for political, ideological, or financial\ngain, has affected the openness of OSNs. While researchers from various\ndisciplines have investigated different manipulation-triggering elements of\nOSNs (such as understanding information diffusion on OSNs or detecting\nautomated behavior of accounts), these works have not been consolidated to\npresent a comprehensive overview of the interconnections among these elements.\nNotably, user psychology, the prevalence of bots, and their tactics in relation\nto false information detection have been overlooked in previous research. To\naddress this research gap, this paper synthesizes insights from various\ndisciplines to provide a comprehensive analysis of the manipulation landscape.\nBy integrating the primary elements of social media manipulation (SMM),\nincluding false information, bots, and malicious campaigns, we extensively\nexamine each SMM element. Through a systematic investigation of prior research,\nwe identify commonalities, highlight existing gaps, and extract valuable\ninsights in the field. Our findings underscore the urgent need for\ninterdisciplinary research to effectively combat social media manipulations,\nand our systematization can guide future research efforts and assist OSN\nproviders in ensuring the safety and integrity of their platforms.\n", "date": "2023-08-24", "categories": []}
{"id": "2301.00626", "title": "Design and analysis of tweet-based election models for the 2021 Mexican\n  legislative election", "abstract": "  Modelling and forecasting real-life human behaviour using online social media\nis an active endeavour of interest in politics, government, academia, and\nindustry. Since its creation in 2006, Twitter has been proposed as a potential\nlaboratory that could be used to gauge and predict social behaviour. During the\nlast decade, the user base of Twitter has been growing and becoming more\nrepresentative of the general population. Here we analyse this user base in the\ncontext of the 2021 Mexican Legislative Election. To do so, we use a dataset of\n15 million election-related tweets in the six months preceding election day. We\nexplore different election models that assign political preference to either\nthe ruling parties or the opposition. We find that models using data with\ngeographical attributes determine the results of the election with better\nprecision and accuracy than conventional polling methods. These results\ndemonstrate that analysis of public online data can outperform conventional\npolling methods, and that political analysis and general forecasting would\nlikely benefit from incorporating such data in the immediate future. Moreover,\nthe same Twitter dataset with geographical attributes is positively correlated\nwith results from official census data on population and internet usage in\nMexico. These findings suggest that we have reached a period in time when\nonline activity, appropriately curated, can provide an accurate representation\nof offline behaviour.\n", "date": "2023-01-02", "categories": []}
{"id": "2308.04444", "title": "Changes in Policy Preferences in German Tweets during the COVID Pandemic", "abstract": "  Online social media have become an important forum for exchanging political\nopinions. In response to COVID measures citizens expressed their policy\npreferences directly on these platforms. Quantifying political preferences in\nonline social media remains challenging: The vast amount of content requires\nscalable automated extraction of political preferences -- however fine grained\npolitical preference extraction is difficult with current machine learning (ML)\ntechnology, due to the lack of data sets. Here we present a novel data set of\ntweets with fine grained political preference annotations. A text\nclassification model trained on this data is used to extract policy preferences\nin a German Twitter corpus ranging from 2019 to 2022. Our results indicate that\nin response to the COVID pandemic, expression of political opinions increased.\nUsing a well established taxonomy of policy preferences we analyse fine grained\npolitical views and highlight changes in distinct political categories. These\nanalyses suggest that the increase in policy preference expression is dominated\nby the categories pro-welfare, pro-education and pro-governmental\nadministration efficiency. All training data and code used in this study are\nmade publicly available to encourage other researchers to further improve\nautomated policy preference extraction methods. We hope that our findings\ncontribute to a better understanding of political statements in online social\nmedia and to a better assessment of how COVID measures impact political\npreferences.\n", "date": "2023-07-31", "categories": []}
{"id": "2308.02727", "title": "How Good Are SOTA Fake News Detectors", "abstract": "  Automatic fake news detection with machine learning can prevent the\ndissemination of false statements before they gain many views. Several datasets\nlabeling statements as legitimate or false have been created since the 2016\nUnited States presidential election for the prospect of training machine\nlearning models. We evaluate the robustness of both traditional and deep\nstate-of-the-art models to gauge how well they may perform in the real world.\nWe find that traditional models tend to generalize better to data outside the\ndistribution it was trained on compared to more recently-developed large\nlanguage models, though the best model to use may depend on the specific task\nat hand.\n", "date": "2023-08-04", "categories": []}
{"id": "2308.03531", "title": "Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage\n  of the 2021 German Federal Election", "abstract": "  Determining and measuring diversity in news articles is important for a\nnumber of reasons, including preventing filter bubbles and fueling public\ndiscourse, especially before elections. So far, the identification and analysis\nof diversity have been illuminated in a variety of ways, such as measuring the\noverlap of words or topics between news articles related to US elections.\nHowever, the question of how diversity in news articles can be measured\nholistically, i.e., with respect to (1) variety, (2) balance, and (3)\ndisparity, considering individuals, parties, and topics, has not been\naddressed. In this paper, we present a framework for determining diversity in\nnews articles according to these dimensions. Furthermore, we create and provide\na dataset of Google Top Stories, encompassing more than 26,000 unique headlines\nfrom more than 900 news outlets collected within two weeks before and after the\n2021 German federal election. While we observe high diversity for more general\nsearch terms (e.g., \"election\"), a range of search terms (\"education,\"\n\"Europe,\" \"climate protection,\" \"government\") resulted in news articles with\nhigh diversity in two out of three dimensions. This reflects a more subjective,\ndedicated discussion on rather future-oriented topics.\n", "date": "2023-08-07", "categories": []}
{"id": "2305.18340", "title": "Mapping ChatGPT in Mainstream Media to Unravel Jobs and Diversity\n  Challenges: Early Quantitative Insights through Sentiment Analysis and Word\n  Frequency Analysis", "abstract": "  The exponential growth in user acquisition and popularity of OpenAIs ChatGPT,\nan artificial intelligence(AI) powered chatbot, was accompanied by widespread\nmainstream media coverage. This article presents a quantitative data analysis\nof the early trends and sentiments revealed by conducting text mining and NLP\nmethods onto a corpus of 10,902 mainstream news headlines related to the\nsubject of ChatGPT and artificial intelligence, from the launch of ChatGPT in\nNovember 2022 to March 2023. The findings revealed in sentiment analysis,\nChatGPT and artificial intelligence, were perceived more positively than\nnegatively in the mainstream media. In regards to word frequency results, over\nsixty-five percent of the top frequency words were focused on Big Tech issues\nand actors while topics such as jobs, diversity, ethics, copyright, gender and\nwomen were poorly represented or completely absent and only accounted for six\npercent of the total corpus. This article is a critical analysis into the power\nstructures and collusions between Big Tech and Big Media in their hegemonic\nexclusion of diversity and job challenges from mainstream media.\n", "date": "2023-05-25", "categories": []}
{"id": "2307.15072", "title": "Detecting the Presence of COVID-19 Vaccination Hesitancy from South\n  African Twitter Data Using Machine Learning", "abstract": "  Very few social media studies have been done on South African user-generated\ncontent during the COVID-19 pandemic and even fewer using hand-labelling over\nautomated methods. Vaccination is a major tool in the fight against the\npandemic, but vaccine hesitancy jeopardizes any public health effort. In this\nstudy, sentiment analysis on South African tweets related to vaccine hesitancy\nwas performed, with the aim of training AI-mediated classification models and\nassessing their reliability in categorizing UGC. A dataset of 30000 tweets from\nSouth Africa were extracted and hand-labelled into one of three sentiment\nclasses: positive, negative, neutral. The machine learning models used were\nLSTM, bi-LSTM, SVM, BERT-base-cased and the RoBERTa-base models, whereby their\nhyperparameters were carefully chosen and tuned using the WandB platform. We\nused two different approaches when we pre-processed our data for comparison:\none was semantics-based, while the other was corpus-based. The pre-processing\nof the tweets in our dataset was performed using both methods, respectively.\nAll models were found to have low F1-scores within a range of 45$\\%$-55$\\%$,\nexcept for BERT and RoBERTa which both achieved significantly better measures\nwith overall F1-scores of 60$\\%$ and 61$\\%$, respectively. Topic modelling\nusing an LDA was performed on the miss-classified tweets of the RoBERTa model\nto gain insight on how to further improve model accuracy.\n", "date": "2023-07-12", "categories": []}
{"id": "2307.14500", "title": "A Predictive Model of Digital Information Engagement: Forecasting User\n  Engagement With English Words by Incorporating Cognitive Biases,\n  Computational Linguistics and Natural Language Processing", "abstract": "  This study introduces and empirically tests a novel predictive model for\ndigital information engagement (IE) - the READ model, an acronym for the four\npivotal attributes of engaging information: Representativeness, Ease-of-use,\nAffect, and Distribution. Conceptualized within the theoretical framework of\nCumulative Prospect Theory, the model integrates key cognitive biases with\ncomputational linguistics and natural language processing to develop a\nmultidimensional perspective on information engagement. A rigorous testing\nprotocol was implemented, involving 50 randomly selected pairs of synonymous\nwords (100 words in total) from the WordNet database. These words' engagement\nlevels were evaluated through a large-scale online survey (n = 80,500) to\nderive empirical IE metrics. The READ attributes for each word were then\ncomputed and their predictive efficacy examined. The findings affirm the READ\nmodel's robustness, accurately predicting a word's IE level and distinguishing\nthe more engaging word from a pair of synonyms with an 84% accuracy rate. The\nREAD model's potential extends across various domains, including business,\neducation, government, and healthcare, where it could enhance content\nengagement and inform AI language model development and generative text work.\nFuture research should address the model's scalability and adaptability across\ndifferent domains and languages, thereby broadening its applicability and\nefficacy.\n", "date": "2023-07-26", "categories": []}
{"id": "2307.14226", "title": "Explore the possibility of advancing climate negotiations on the basis\n  of regional trade organizations: A study based on RICE-N", "abstract": "  Climate issues have become more and more important now. Although global\ngovernments have made some progress, we are still facing the truth that the\nprospect of international cooperation is not clear at present. Due to the\nlimitations of the Integrated assessment models (IAMs) model, it is difficult\nto simulate the dynamic negotiation process. Therefore, using deep learning to\nbuild a new agents based model (ABM) might can provide new theoretical support\nfor climate negotiations. Building on the RICE-N model, this work proposed an\napproach to climate negotiations based on existing trade groups. Simulation\nresults show that the scheme has a good prospect.\n", "date": "2023-07-26", "categories": []}
{"id": "2307.12332", "title": "X-CapsNet For Fake News Detection", "abstract": "  News consumption has significantly increased with the growing popularity and\nuse of web-based forums and social media. This sets the stage for misinforming\nand confusing people. To help reduce the impact of misinformation on users'\npotential health-related decisions and other intents, it is desired to have\nmachine learning models to detect and combat fake news automatically. This\npaper proposes a novel transformer-based model using Capsule neural\nNetworks(CapsNet) called X-CapsNet. This model includes a CapsNet with dynamic\nrouting algorithm paralyzed with a size-based classifier for detecting short\nand long fake news statements. We use two size-based classifiers, a Deep\nConvolutional Neural Network (DCNN) for detecting long fake news statements and\na Multi-Layer Perceptron (MLP) for detecting short news statements. To resolve\nthe problem of representing short news statements, we use indirect features of\nnews created by concatenating the vector of news speaker profiles and a vector\nof polarity, sentiment, and counting words of news statements. For evaluating\nthe proposed architecture, we use the Covid-19 and the Liar datasets. The\nresults in terms of the F1-score for the Covid-19 dataset and accuracy for the\nLiar dataset show that models perform better than the state-of-the-art\nbaselines.\n", "date": "2023-07-23", "categories": []}
{"id": "2307.12788", "title": "Analyzing the Strategy of Propaganda using Inverse Reinforcement\n  Learning: Evidence from the 2022 Russian Invasion of Ukraine", "abstract": "  The 2022 Russian invasion of Ukraine was accompanied by a large-scale,\npro-Russian propaganda campaign on social media. However, the strategy behind\nthe dissemination of propaganda has remained unclear, particularly how the\nonline discourse was strategically shaped by the propagandists' community.\nHere, we analyze the strategy of the Twitter community using an inverse\nreinforcement learning (IRL) approach. Specifically, IRL allows us to model\nonline behavior as a Markov decision process, where the goal is to infer the\nunderlying reward structure that guides propagandists when interacting with\nusers with a supporting or opposing stance toward the invasion. Thereby, we aim\nto understand empirically whether and how between-user interactions are\nstrategically used to promote the proliferation of Russian propaganda. For\nthis, we leverage a large-scale dataset with 349,455 posts with pro-Russian\npropaganda from 132,131 users. We show that bots and humans follow a different\nstrategy: bots respond predominantly to pro-invasion messages, suggesting that\nthey seek to drive virality; while messages indicating opposition primarily\nelicit responses from humans, suggesting that they tend to engage in critical\ndiscussions. To the best of our knowledge, this is the first study analyzing\nthe strategy behind propaganda from the 2022 Russian invasion of Ukraine\nthrough the lens of IRL.\n", "date": "2023-07-24", "categories": []}
{"id": "2307.04668", "title": "Quantifying the Echo Chamber Effect: An Embedding Distance-based\n  Approach", "abstract": "  The rise of social media platforms has facilitated the formation of echo\nchambers, which are online spaces where users predominantly encounter\nviewpoints that reinforce their existing beliefs while excluding dissenting\nperspectives. This phenomenon significantly hinders information dissemination\nacross communities and fuels societal polarization. Therefore, it is crucial to\ndevelop methods for quantifying echo chambers. In this paper, we present the\nEcho Chamber Score (ECS), a novel metric that assesses the cohesion and\nseparation of user communities by measuring distances between users in the\nembedding space. In contrast to existing approaches, ECS is able to function\nwithout labels for user ideologies and makes no assumptions about the structure\nof the interaction graph. To facilitate measuring distances between users, we\npropose EchoGAE, a self-supervised graph autoencoder-based user embedding model\nthat leverages users' posts and the interaction graph to embed them in a manner\nthat reflects their ideological similarity. To assess the effectiveness of ECS,\nwe use a Twitter dataset consisting of four topics - two polarizing and two\nnon-polarizing. Our results showcase ECS's effectiveness as a tool for\nquantifying echo chambers and shedding light on the dynamics of online\ndiscourse.\n", "date": "2023-07-10", "categories": []}
{"id": "2307.10200", "title": "Disentangling Societal Inequality from Model Biases: Gender Inequality\n  in Divorce Court Proceedings", "abstract": "  Divorce is the legal dissolution of a marriage by a court. Since this is\nusually an unpleasant outcome of a marital union, each party may have reasons\nto call the decision to quit which is generally documented in detail in the\ncourt proceedings. Via a substantial corpus of 17,306 court proceedings, this\npaper investigates gender inequality through the lens of divorce court\nproceedings. While emerging data sources (e.g., public court records) on\nsensitive societal issues hold promise in aiding social science research,\nbiases present in cutting-edge natural language processing (NLP) methods may\ninterfere with or affect such studies. We thus require a thorough analysis of\npotential gaps and limitations present in extant NLP resources. In this paper,\non the methodological side, we demonstrate that existing NLP resources required\nseveral non-trivial modifications to quantify societal inequalities. On the\nsubstantive side, we find that while a large number of court cases perhaps\nsuggest changing norms in India where women are increasingly challenging\npatriarchy, AI-powered analyses of these court proceedings indicate striking\ngender inequality with women often subjected to domestic violence.\n", "date": "2023-07-09", "categories": []}
{"id": "2306.16299", "title": "Social World Knowledge: Modeling and Applications", "abstract": "  Social world knowledge is a key ingredient in effective communication and\ninformation processing by humans and machines alike. As of today, there exist\nmany knowledge bases that represent factual world knowledge. Yet, there is no\nresource that is designed to capture social aspects of world knowledge. We\nbelieve that this work makes an important step towards the formulation and\nconstruction of such a resource. We introduce SocialVec, a general framework\nfor eliciting low-dimensional entity embeddings from the social contexts in\nwhich they occur in social networks. In this framework, entities correspond to\nhighly popular accounts which invoke general interest. We assume that entities\nthat individual users tend to co-follow are socially related, and use this\ndefinition of social context to learn the entity embeddings. Similar to word\nembeddings which facilitate tasks that involve text semantics, we expect the\nlearned social entity embeddings to benefit multiple tasks of social flavor. In\nthis work, we elicited the social embeddings of roughly 200K entities from a\nsample of 1.3M Twitter users and the accounts that they follow. We employ and\ngauge the resulting embeddings on two tasks of social importance. First, we\nassess the political bias of news sources in terms of entity similarity in the\nsocial embedding space. Second, we predict the personal traits of individual\nTwitter users based on the social embeddings of entities that they follow. In\nboth cases, we show advantageous or competitive performance using our approach\ncompared with task-specific baselines. We further show that existing entity\nembedding schemes, which are fact-based, fail to capture social aspects of\nknowledge. We make the learned social entity embeddings available to the\nresearch community to support further exploration of social world knowledge and\nits applications.\n", "date": "2023-06-28", "categories": []}
{"id": "2307.08139", "title": "It's All Relative: Interpretable Models for Scoring Bias in Documents", "abstract": "  We propose an interpretable model to score the bias present in web documents,\nbased only on their textual content. Our model incorporates assumptions\nreminiscent of the Bradley-Terry axioms and is trained on pairs of revisions of\nthe same Wikipedia article, where one version is more biased than the other.\nWhile prior approaches based on absolute bias classification have struggled to\nobtain a high accuracy for the task, we are able to develop a useful model for\nscoring bias by learning to perform pairwise comparisons of bias accurately. We\nshow that we can interpret the parameters of the trained model to discover the\nwords most indicative of bias. We also apply our model in three different\nsettings - studying the temporal evolution of bias in Wikipedia articles,\ncomparing news sources based on bias, and scoring bias in law amendments. In\neach case, we demonstrate that the outputs of the model can be explained and\nvalidated, even for the two domains that are outside the training-data domain.\nWe also use the model to compare the general level of bias between domains,\nwhere we see that legal texts are the least biased and news media are the most\nbiased, with Wikipedia articles in between. Given its high performance,\nsimplicity, interpretability, and wide applicability, we hope the model will be\nuseful for a large community, including Wikipedia and news editors, political\nand social scientists, and the general public.\n", "date": "2023-07-16", "categories": []}
{"id": "2307.08580", "title": "The Resume Paradox: Greater Language Differences, Smaller Pay Gaps", "abstract": "  Over the past decade, the gender pay gap has remained steady with women\nearning 84 cents for every dollar earned by men on average. Many studies\nexplain this gap through demand-side bias in the labor market represented\nthrough employers' job postings. However, few studies analyze potential bias\nfrom the worker supply-side. Here, we analyze the language in millions of US\nworkers' resumes to investigate how differences in workers' self-representation\nby gender compare to differences in earnings. Across US occupations, language\ndifferences between male and female resumes correspond to 11% of the variation\nin gender pay gap. This suggests that females' resumes that are semantically\nsimilar to males' resumes may have greater wage parity. However, surprisingly,\noccupations with greater language differences between male and female resumes\nhave lower gender pay gaps. A doubling of the language difference between\nfemale and male resumes results in an annual wage increase of $2,797 for the\naverage female worker. This result holds with controls for gender-biases of\nresume text and we find that per-word bias poorly describes the variance in\nwage gap. The results demonstrate that textual data and self-representation are\nvaluable factors for improving worker representations and understanding\nemployment inequities.\n", "date": "2023-07-17", "categories": []}
{"id": "2307.07007", "title": "Electoral Agitation Data Set: The Use Case of the Polish Election", "abstract": "  The popularity of social media makes politicians use it for political\nadvertisement. Therefore, social media is full of electoral agitation\n(electioneering), especially during the election campaigns. The election\nadministration cannot track the spread and quantity of messages that count as\nagitation under the election code. It addresses a crucial problem, while also\nuncovering a niche that has not been effectively targeted so far. Hence, we\npresent the first publicly open data set for detecting electoral agitation in\nthe Polish language. It contains 6,112 human-annotated tweets tagged with four\nlegally conditioned categories. We achieved a 0.66 inter-annotator agreement\n(Cohen's kappa score). An additional annotator resolved the mismatches between\nthe first two improving the consistency and complexity of the annotation\nprocess. The newly created data set was used to fine-tune a Polish Language\nModel called HerBERT (achieving a 68% F1 score). We also present a number of\npotential use cases for such data sets and models, enriching the paper with an\nanalysis of the Polish 2020 Presidential Election on Twitter.\n", "date": "2023-07-13", "categories": []}
{"id": "2306.15745", "title": "Identity Construction in a Misogynist Incels Forum", "abstract": "  Online communities of involuntary celibates (incels) are a prominent source\nof misogynist hate speech. In this paper, we use quantitative text and network\nanalysis approaches to examine how identity groups are discussed on\nincels-dot-is, the largest black-pilled incels forum. We find that this\ncommunity produces a wide range of novel identity terms and, while terms for\nwomen are most common, mentions of other minoritized identities are increasing.\nAn analysis of the associations made with identity groups suggests an\nessentialist ideology where physical appearance, as well as gender and racial\nhierarchies, determine human value. We discuss implications for research into\nautomated misogynist hate speech detection.\n", "date": "2023-06-27", "categories": []}
{"id": "2307.03764", "title": "For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis\n  of a Watershed Moment in Iran's Gender Struggles", "abstract": "  In this paper, we present a computational analysis of the Persian language\nTwitter discourse with the aim to estimate the shift in stance toward gender\nequality following the death of Mahsa Amini in police custody. We present an\nensemble active learning pipeline to train a stance classifier. Our novelty\nlies in the involvement of Iranian women in an active role as annotators in\nbuilding this AI system. Our annotators not only provide labels, but they also\nsuggest valuable keywords for more meaningful corpus creation as well as\nprovide short example documents for a guided sampling step. Our analyses\nindicate that Mahsa Amini's death triggered polarized Persian language\ndiscourse where both fractions of negative and positive tweets toward gender\nequality increased. The increase in positive tweets was slightly greater than\nthe increase in negative tweets. We also observe that with respect to account\ncreation time, between the state-aligned Twitter accounts and pro-protest\nTwitter accounts, pro-protest accounts are more similar to baseline Persian\nTwitter activity.\n", "date": "2023-07-07", "categories": []}
{"id": "2307.04053", "title": "How is Fatherhood Framed Online in Singapore?", "abstract": "  The proliferation of discussion about fatherhood in Singapore attests to its\nsignificance, indicating the need for an exploration of how fatherhood is\nframed, aiding policy-making around fatherhood in Singapore. Sound and holistic\npolicy around fatherhood in Singapore may reduce stigma and apprehension around\nbeing a parent, critical to improving the nations flagging birth rate. We\nanalyzed 15,705 articles and 56,221 posts to study how fatherhood is framed in\nSingapore across a range of online platforms (news outlets, parenting forums,\nTwitter). We used NLP techniques to understand these differences. While\nfatherhood was framed in a range of ways on the Singaporean online environment,\nit did not seem that fathers were framed as central to the Singaporean family\nunit. A strength of our work is how the different techniques we have applied\nvalidate each other.\n", "date": "2023-07-08", "categories": []}
{"id": "2307.04761", "title": "Understanding Counterspeech for Online Harm Mitigation", "abstract": "  Counterspeech offers direct rebuttals to hateful speech by challenging\nperpetrators of hate and showing support to targets of abuse. It provides a\npromising alternative to more contentious measures, such as content moderation\nand deplatforming, by contributing a greater amount of positive online speech\nrather than attempting to mitigate harmful content through removal. Advances in\nthe development of large language models mean that the process of producing\ncounterspeech could be made more efficient by automating its generation, which\nwould enable large-scale online campaigns. However, we currently lack a\nsystematic understanding of several important factors relating to the efficacy\nof counterspeech for hate mitigation, such as which types of counterspeech are\nmost effective, what are the optimal conditions for implementation, and which\nspecific effects of hate it can best ameliorate. This paper aims to fill this\ngap by systematically reviewing counterspeech research in the social sciences\nand comparing methodologies and findings with computer science efforts in\nautomatic counterspeech generation. By taking this multi-disciplinary view, we\nidentify promising future directions in both fields.\n", "date": "2023-07-01", "categories": []}
{"id": "2307.01299", "title": "The Evolution of Substance Use Coverage in the Philadelphia Inquirer", "abstract": "  The media's representation of illicit substance use can lead to harmful\nstereotypes and stigmatization for individuals struggling with addiction,\nultimately influencing public perception, policy, and public health outcomes.\nTo explore how the discourse and coverage of illicit drug use changed over\ntime, this study analyzes 157,476 articles published in the Philadelphia\nInquirer over a decade. Specifically, the study focuses on articles that\nmentioned at least one commonly abused substance, resulting in a sample of\n3,903 articles. Our analysis shows that cannabis and narcotics are the most\nfrequently discussed classes of drugs. Hallucinogenic drugs are portrayed more\npositively than other categories, whereas narcotics are portrayed the most\nnegatively. Our research aims to highlight the need for accurate and inclusive\nportrayals of substance use and addiction in the media.\n", "date": "2023-07-03", "categories": []}
{"id": "2307.01693", "title": "Racial Bias Trends in the Text of US Legal Opinions", "abstract": "  Although there is widespread recognition of racial bias in US law, it is\nunclear how such bias appears in the language of law, namely judicial opinions,\nand whether it varies across time period or region. Building upon approaches\nfor measuring implicit racial bias in large-scale corpora, we approximate GloVe\nword embeddings for over 6 million US federal and state court cases from 1860\nto 2009. We find strong evidence of racial bias across nearly all regions and\ntime periods, as traditionally Black names are more closely associated with\npre-classified \"unpleasant\" terms whereas traditionally White names are more\nclosely associated with pre-classified \"pleasant\" terms. We also test whether\nlegal opinions before 1950 exhibit more implicit racial bias than those after\n1950, as well as whether opinions from Southern states exhibit less change in\nracial bias than those from Northeastern states. We do not find evidence of\nelevated bias in legal opinions before 1950, or evidence that legal opinions\nfrom Northeastern states show greater change in racial bias over time compared\nto Southern states. These results motivate further research into\ninstitutionalized racial bias.\n", "date": "2023-07-04", "categories": []}
{"id": "2210.10031", "title": "Understanding COVID-19 Vaccine Campaign on Facebook using Minimal\n  Supervision", "abstract": "  In the age of social media, where billions of internet users share\ninformation and opinions, the negative impact of pandemics is not limited to\nthe physical world. It provokes a surge of incomplete, biased, and incorrect\ninformation, also known as an infodemic. This global infodemic jeopardizes\nmeasures to control the pandemic by creating panic, vaccine hesitancy, and\nfragmented social response. Platforms like Facebook allow advertisers to adapt\ntheir messaging to target different demographics and help alleviate or\nexacerbate the infodemic problem depending on their content. In this paper, we\npropose a minimally supervised multi-task learning framework for understanding\nmessaging on Facebook related to the COVID vaccine by identifying ad themes and\nmoral foundations. Furthermore, we perform a more nuanced thematic analysis of\nmessaging tactics of vaccine campaigns on social media so that policymakers can\nmake better decisions on pandemic control.\n", "date": "2022-10-18", "categories": []}
{"id": "2210.10669", "title": "Weakly Supervised Learning for Analyzing Political Campaigns on Facebook", "abstract": "  Social media platforms are currently the main channel for political\nmessaging, allowing politicians to target specific demographics and adapt based\non their reactions. However, making this communication transparent is\nchallenging, as the messaging is tightly coupled with its intended audience and\noften echoed by multiple stakeholders interested in advancing specific\npolicies. Our goal in this paper is to take a first step towards understanding\nthese highly decentralized settings. We propose a weakly supervised approach to\nidentify the stance and issue of political ads on Facebook and analyze how\npolitical campaigns use some kind of demographic targeting by location, gender,\nor age. Furthermore, we analyze the temporal dynamics of the political ads on\nelection polls.\n", "date": "2022-10-19", "categories": []}
{"id": "2212.09683", "title": "Human-in-the-loop Evaluation for Early Misinformation Detection: A Case\n  Study of COVID-19 Treatments", "abstract": "  We present a human-in-the-loop evaluation framework for fact-checking novel\nmisinformation claims and identifying social media messages that support them.\nOur approach extracts check-worthy claims, which are aggregated and ranked for\nreview. Stance classifiers are then used to identify tweets supporting novel\nmisinformation claims, which are further reviewed to determine whether they\nviolate relevant policies. To demonstrate the feasibility of our approach, we\ndevelop a baseline system based on modern NLP methods for human-in-the-loop\nfact-checking in the domain of COVID-19 treatments. We make our data and\ndetailed annotation guidelines available to support the evaluation of\nhuman-in-the-loop systems that identify novel misinformation directly from raw\nuser-generated content.\n", "date": "2022-12-19", "categories": []}
{"id": "2305.06174", "title": "Analysis of Climate Campaigns on Social Media using Bayesian Model\n  Averaging", "abstract": "  Climate change is the defining issue of our time, and we are at a defining\nmoment. Various interest groups, social movement organizations, and individuals\nengage in collective action on this issue on social media. In addition, issue\nadvocacy campaigns on social media often arise in response to ongoing societal\nconcerns, especially those faced by energy industries. Our goal in this paper\nis to analyze how those industries, their advocacy group, and climate advocacy\ngroup use social media to influence the narrative on climate change. In this\nwork, we propose a minimally supervised model soup [57] approach combined with\nmessaging themes to identify the stances of climate ads on Facebook. Finally,\nwe release our stance dataset, model, and set of themes related to climate\ncampaigns for future work on opinion mining and the automatic detection of\nclimate change stances.\n", "date": "2023-05-06", "categories": []}
{"id": "2306.14203", "title": "Stance Prediction and Analysis of Twitter data : A case study of Ghana\n  2020 Presidential Elections", "abstract": "  On December 7, 2020, Ghanaians participated in the polls to determine their\npresident for the next four years. To gain insights from this presidential\nelection, we conducted stance analysis (which is not always equivalent to\nsentiment analysis) to understand how Twitter, a popular social media platform,\nreflected the opinions of its users regarding the two main presidential\ncandidates. We collected a total of 99,356 tweets using the Twitter API\n(Tweepy) and manually annotated 3,090 tweets into three classes: Against,\nNeutral, and Support. We then performed preprocessing on the tweets. The\nresulting dataset was evaluated using two lexicon-based approaches, VADER and\nTextBlob, as well as five supervised machine learning-based approaches: Support\nVector Machine (SVM), Logistic Regression (LR), Multinomial Na\\\"ive Bayes\n(MNB), Stochastic Gradient Descent (SGD), and Random Forest (RF), based on\nmetrics such as accuracy, precision, recall, and F1-score. The best performance\nwas achieved by Logistic Regression with an accuracy of 71.13%. We utilized\nLogistic Regression to classify all the extracted tweets and subsequently\nconducted an analysis and discussion of the results. For access to our data and\ncode, please visit:\nhttps://github.com/ShesterG/Stance-Detection-Ghana-2020-Elections.git\n", "date": "2023-06-25", "categories": []}
{"id": "2306.16031", "title": "Exploring Spatial-Temporal Variations of Public Discourse on Social\n  Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy", "abstract": "  This paper proposes a methodology for exploring how linguistic behaviour on\nsocial media can be used to explore societal reactions to important events such\nas those that transpired during the SARS CoV2 pandemic. In particular, where\nspatial and temporal aspects of events are important features. Our methodology\nconsists of grounding spatial-temporal categories in tweet usage trends using\ntime-series analysis and clustering. Salient terms in each category were then\nidentified through qualitative comparative analysis based on scaled f-scores\naggregated into hand-coded categories. To exemplify this approach, we conducted\na case study on the first wave of the coronavirus in Italy. We used our\nproposed methodology to explore existing psychological observations which\nclaimed that physical distance from events affects what is communicated about\nthem. We confirmed these findings by showing that the epicentre of the disease\nand peripheral regions correspond to clear time-series clusters and that those\nliving in the epicentre of the SARS CoV2 outbreak were more focused on\nsolidarity and policy than those from more peripheral regions. Furthermore, we\nalso found that temporal categories corresponded closely to policy changes\nduring the handling of the pandemic.\n", "date": "2023-06-28", "categories": []}
{"id": "2306.16207", "title": "Inferring the Goals of Communicating Agents from Actions and\n  Instructions", "abstract": "  When humans cooperate, they frequently coordinate their activity through both\nverbal communication and non-verbal actions, using this information to infer a\nshared goal and plan. How can we model this inferential ability? In this paper,\nwe introduce a model of a cooperative team where one agent, the principal, may\ncommunicate natural language instructions about their shared plan to another\nagent, the assistant, using GPT-3 as a likelihood function for instruction\nutterances. We then show how a third person observer can infer the team's goal\nvia multi-modal Bayesian inverse planning from actions and instructions,\ncomputing the posterior distribution over goals under the assumption that\nagents will act and communicate rationally to achieve them. We evaluate this\napproach by comparing it with human goal inferences in a multi-agent gridworld,\nfinding that our model's inferences closely correlate with human judgments (R =\n0.96). When compared to inference from actions alone, we also find that\ninstructions lead to more rapid and less uncertain goal inference, highlighting\nthe importance of verbal communication for cooperative agents.\n", "date": "2023-06-28", "categories": []}
{"id": "2306.15231", "title": "Emulating Reader Behaviors for Fake News Detection", "abstract": "  The wide dissemination of fake news has affected our lives in many aspects,\nmaking fake news detection important and attracting increasing attention.\nExisting approaches make substantial contributions in this field by modeling\nnews from a single-modal or multi-modal perspective. However, these modal-based\nmethods can result in sub-optimal outcomes as they ignore reader behaviors in\nnews consumption and authenticity verification. For instance, they haven't\ntaken into consideration the component-by-component reading process: from the\nheadline, images, comments, to the body, which is essential for modeling news\nwith more granularity. To this end, we propose an approach of Emulating the\nbehaviors of readers (Ember) for fake news detection on social media,\nincorporating readers' reading and verificating process to model news from the\ncomponent perspective thoroughly. Specifically, we first construct\nintra-component feature extractors to emulate the behaviors of semantic\nanalyzing on each component. Then, we design a module that comprises\ninter-component feature extractors and a sequence-based aggregator. This module\nmimics the process of verifying the correlation between components and the\noverall reading and verification sequence. Thus, Ember can handle the news with\nvarious components by emulating corresponding sequences. We conduct extensive\nexperiments on nine real-world datasets, and the results demonstrate the\nsuperiority of Ember.\n", "date": "2023-06-27", "categories": []}
{"id": "2306.13797", "title": "An analysis of vaccine-related sentiments from development to deployment\n  of COVID-19 vaccines", "abstract": "  Anti-vaccine sentiments have been well-known and reported throughout the\nhistory of viral outbreaks and vaccination programmes. The COVID-19 pandemic\nhad fear and uncertainty about vaccines which has been well expressed on social\nmedia platforms such as Twitter. We analyse Twitter sentiments from the\nbeginning of the COVID-19 pandemic and study the public behaviour during the\nplanning, development and deployment of vaccines expressed in tweets worldwide\nusing a sentiment analysis framework via deep learning models. In this way, we\nprovide visualisation and analysis of anti-vaccine sentiments over the course\nof the COVID-19 pandemic. Our results show a link between the number of tweets,\nthe number of cases, and the change in sentiment polarity scores during major\nwaves of COVID-19 cases. We also found that the first half of the pandemic had\ndrastic changes in the sentiment polarity scores that later stabilised which\nimplies that the vaccine rollout had an impact on the nature of discussions on\nsocial media.\n", "date": "2023-06-23", "categories": []}
{"id": "2306.13954", "title": "Characterizing the Emotion Carriers of COVID-19 Misinformation and Their\n  Impact on Vaccination Outcomes in India and the United States", "abstract": "  The COVID-19 Infodemic had an unprecedented impact on health behaviors and\noutcomes at a global scale. While many studies have focused on a qualitative\nand quantitative understanding of misinformation, including sentiment analysis,\nthere is a gap in understanding the emotion-carriers of misinformation and\ntheir differences across geographies. In this study, we characterized emotion\ncarriers and their impact on vaccination rates in India and the United States.\nA manually labelled dataset was created from 2.3 million tweets and collated\nwith three publicly available datasets (CoAID, AntiVax, CMU) to train deep\nlearning models for misinformation classification. Misinformation labelled\ntweets were further analyzed for behavioral aspects by leveraging Plutchik\nTransformers to determine the emotion for each tweet. Time series analysis was\nconducted to study the impact of misinformation on spatial and temporal\ncharacteristics. Further, categorical classification was performed using\ntransformer models to assign categories for the misinformation tweets.\nWord2Vec+BiLSTM was the best model for misinformation classification, with an\nF1-score of 0.92. The US had the highest proportion of misinformation tweets\n(58.02%), followed by the UK (10.38%) and India (7.33%). Disgust, anticipation,\nand anger were associated with an increased prevalence of misinformation\ntweets. Disgust was the predominant emotion associated with misinformation\ntweets in the US, while anticipation was the predominant emotion in India. For\nIndia, the misinformation rate exhibited a lead relationship with vaccination,\nwhile in the US it lagged behind vaccination. Our study deciphered that\nemotions acted as differential carriers of misinformation across geography and\ntime. These carriers can be monitored to develop strategic interventions for\ncountering misinformation, leading to improved public health.\n", "date": "2023-06-24", "categories": []}
{"id": "2306.12014", "title": "3HAN: A Deep Neural Network for Fake News Detection", "abstract": "  The rapid spread of fake news is a serious problem calling for AI solutions.\nWe employ a deep learning based automated detector through a three level\nhierarchical attention network (3HAN) for fast, accurate detection of fake\nnews. 3HAN has three levels, one each for words, sentences, and the headline,\nand constructs a news vector: an effective representation of an input news\narticle, by processing an article in an hierarchical bottom-up manner. The\nheadline is known to be a distinguishing feature of fake news, and furthermore,\nrelatively few words and sentences in an article are more important than the\nrest. 3HAN gives a differential importance to parts of an article, on account\nof its three layers of attention. By experiments on a large real-world data\nset, we observe the effectiveness of 3HAN with an accuracy of 96.77%. Unlike\nsome other deep learning models, 3HAN provides an understandable output through\nthe attention weights given to different parts of an article, which can be\nvisualized through a heatmap to enable further manual fact checking.\n", "date": "2023-06-21", "categories": []}
{"id": "2306.02137", "title": "Inconsistent Matters: A Knowledge-guided Dual-consistency Network for\n  Multi-modal Rumor Detection", "abstract": "  Rumor spreaders are increasingly utilizing multimedia content to attract the\nattention and trust of news consumers. Though quite a few rumor detection\nmodels have exploited the multi-modal data, they seldom consider the\ninconsistent semantics between images and texts, and rarely spot the\ninconsistency among the post contents and background knowledge. In addition,\nthey commonly assume the completeness of multiple modalities and thus are\nincapable of handling handle missing modalities in real-life scenarios.\nMotivated by the intuition that rumors in social media are more likely to have\ninconsistent semantics, a novel Knowledge-guided Dual-consistency Network is\nproposed to detect rumors with multimedia contents. It uses two consistency\ndetection subnetworks to capture the inconsistency at the cross-modal level and\nthe content-knowledge level simultaneously. It also enables robust multi-modal\nrepresentation learning under different missing visual modality conditions,\nusing a special token to discriminate between posts with visual modality and\nposts without visual modality. Extensive experiments on three public real-world\nmultimedia datasets demonstrate that our framework can outperform the\nstate-of-the-art baselines under both complete and incomplete modality\nconditions. Our codes are available at https://github.com/MengzSun/KDCN.\n", "date": "2023-06-03", "categories": []}
{"id": "2306.10769", "title": "Gender Differences in Abuse: The Case of Dutch Politicians on Twitter", "abstract": "  Online abuse and threats towards politicians have become a significant\nconcern in the Netherlands, like in many other countries across the world. This\npaper analyses gender differences in abuse received by Dutch politicians on\nTwitter, while taking into account the possible additional impact of ethnic\nminority status. All tweets directed at party leaders throughout the entire\nyear of 2022 were collected. The effect of gender and ethnic minority status\nwere estimated for six different linguistic measures of abuse, namely,\ntoxicity, severe toxicity, identity attacks, profanity, insults, and threats.\nContrary to expectations, male politicians received higher levels of all forms\nof abuse, with the exception of threats, for which no significant gender\ndifference was found. Significant interaction effects between gender and ethnic\nminority status were found for a number of abuse measures. In the case of\nsevere toxicity, identity attacks, and profanity, female ethnic minority\npoliticians were more severely impacted than their ethnic majority female\ncolleagues, but not worse than male politicians. Finally, female ethnic\nminority politicians received the highest levels of threats compared to all\ngroups. Given that online abuse and threats are reported to have a negative\neffect on political participation and retention, these results are particularly\nworrying.\n", "date": "2023-06-19", "categories": []}
{"id": "2211.16733", "title": "A minor extension of the logistic equation for growth of word counts on\n  online media: Parametric description of diversity of growth phenomena in\n  society", "abstract": "  To understand the growing phenomena of new vocabulary on nationwide online\nsocial media, we analyzed monthly word count time series extracted from\napproximately 1 billion Japanese blog articles from 2007 to 2019. In\nparticular, we first introduced the extended logistic equation by adding one\nparameter to the original equation and showed that the model can consistently\nreproduce various patterns of actual growth curves, such as the logistic\nfunction, linear growth, and finite-time divergence. Second, by analyzing the\nmodel parameters, we found that the typical growth pattern is not only a\nlogistic function, which often appears in various complex systems, but also a\nnontrivial growth curve that starts with an exponential function and\nasymptotically approaches a power function without a steady state. Furthermore,\nwe observed a connection between the functional form of growth and the\npeak-out. Finally, we showed that the proposed model and statistical properties\nare also valid for Google Trends data (English, French, Spanish, and Japanese),\nwhich is a time series of the nationwide popularity of search queries.\n", "date": "2022-11-30", "categories": []}
{"id": "2306.08999", "title": "Voting Booklet Bias: Stance Detection in Swiss Federal Communication", "abstract": "  In this study, we use recent stance detection methods to study the stance\n(for, against or neutral) of statements in official information booklets for\nvoters. Our main goal is to answer the fundamental question: are topics to be\nvoted on presented in a neutral way?\n  To this end, we first train and compare several models for stance detection\non a large dataset about Swiss politics. We find that fine-tuning an M-BERT\nmodel leads to the best accuracy. We then use our best model to analyze the\nstance of utterances extracted from the Swiss federal voting booklet concerning\nthe Swiss popular votes of September 2022, which is the main goal of this\nproject.\n  We evaluated the models in both a multilingual as well as a monolingual\ncontext for German, French, and Italian. Our analysis shows that some issues\nare heavily favored while others are more balanced, and that the results are\nlargely consistent across languages.\n  Our findings have implications for the editorial process of future voting\nbooklets and the design of better automated systems for analyzing political\ndiscourse. The data and code accompanying this paper are available at\nhttps://github.com/ZurichNLP/voting-booklet-bias.\n", "date": "2023-06-15", "categories": []}
{"id": "2304.06858", "title": "Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter", "abstract": "  Vaccine hesitancy continues to be a main challenge for public health\nofficials during the COVID-19 pandemic. As this hesitancy undermines vaccine\ncampaigns, many researchers have sought to identify its root causes, finding\nthat the increasing volume of anti-vaccine misinformation on social media\nplatforms is a key element of this problem. We explored Twitter as a source of\nmisleading content with the goal of extracting overlapping cultural and\npolitical beliefs that motivate the spread of vaccine misinformation. To do\nthis, we have collected a data set of vaccine-related Tweets and annotated them\nwith the help of a team of annotators with a background in communications and\njournalism. Ultimately we hope this can lead to effective and targeted public\nhealth communication strategies for reaching individuals with anti-vaccine\nbeliefs. Moreover, this information helps with developing Machine Learning\nmodels to automatically detect vaccine misinformation posts and combat their\nnegative impacts. In this paper, we present Vax-Culture, a novel Twitter\nCOVID-19 dataset consisting of 6373 vaccine-related tweets accompanied by an\nextensive set of human-provided annotations including vaccine-hesitancy stance,\nindication of any misinformation in tweets, the entities criticized and\nsupported in each tweet and the communicated message of each tweet. Moreover,\nwe define five baseline tasks including four classification and one sequence\ngeneration tasks, and report the results of a set of recent transformer-based\nmodels for them. The dataset and code are publicly available at\nhttps://github.com/mrzarei5/Vax-Culture.\n", "date": "2023-04-13", "categories": []}
{"id": "2306.05803", "title": "Causality between Sentiment and Cryptocurrency Prices", "abstract": "  This study investigates the relationship between narratives conveyed through\nmicroblogging platforms, namely Twitter, and the value of crypto assets. Our\nstudy provides a unique technique to build narratives about cryptocurrency by\ncombining topic modelling of short texts with sentiment analysis. First, we\nused an unsupervised machine learning algorithm to discover the latent topics\nwithin the massive and noisy textual data from Twitter, and then we revealed\n4-5 cryptocurrency-related narratives, including financial investment,\ntechnological advancement related to crypto, financial and political\nregulations, crypto assets, and media coverage. In a number of situations, we\nnoticed a strong link between our narratives and crypto prices. Our work\nconnects the most recent innovation in economics, Narrative Economics, to a new\narea of study that combines topic modelling and sentiment analysis to relate\nconsumer behaviour to narratives.\n", "date": "2023-06-09", "categories": []}
{"id": "2306.05373", "title": "A Computational Analysis of Oral Argument in the Supreme Court", "abstract": "  As the most public component of the Supreme Court's decision-making process,\noral argument receives an out-sized share of attention in the popular media.\nDespite its prominence, however, the basic function and operation of oral\nargument as an institution remains poorly understood, as political scientists\nand legal scholars continue to debate even the most fundamental questions about\nits role.\n  Past study of oral argument has tended to focus on discrete, quantifiable\nattributes of oral argument, such as the number of questions asked to each\nadvocate, the party of the Justices' appointing president, or the ideological\nimplications of the case on appeal. Such studies allow broad generalizations\nabout oral argument and judicial decision making: Justices tend to vote in\naccordance with their ideological preferences, and they tend to ask more\nquestions when they are skeptical of a party's position. But they tell us\nlittle about the actual goings on at oral argument -- the running dialog\nbetween Justice and advocate that is the heart of the institution.\n  This Article fills that void, using machine learning techniques to, for the\nfirst time, construct predictive models of judicial decision making based not\non oral argument's superficial features or on factors external to oral\nargument, such as where the case falls on a liberal-conservative spectrum, but\non the actual content of the oral argument itself -- the Justices' questions to\neach side. The resultant models offer an important new window into aspects of\noral argument that have long resisted empirical study, including the Justices'\nindividual questioning styles, how each expresses skepticism, and which of the\nJustices' questions are most central to oral argument dialog.\n", "date": "2023-06-05", "categories": []}
{"id": "2305.02770", "title": "The Politics of Language Choice: How the Russian-Ukrainian War\n  Influences Ukrainians' Language Use on Twitter", "abstract": "  The use of language is innately political and often a vehicle of cultural\nidentity as well as the basis for nation building. Here, we examine language\nchoice and tweeting activity of Ukrainian citizens based on more than 4 million\ngeo-tagged tweets from over 62,000 users before and during the\nRussian-Ukrainian War, from January 2020 to October 2022. Using statistical\nmodels, we disentangle sample effects, arising from the in- and outflux of\nusers on Twitter, from behavioural effects, arising from behavioural changes of\nthe users. We observe a steady shift from the Russian language towards the\nUkrainian language already before the war, which drastically speeds up with its\noutbreak. We attribute these shifts in large part to users' behavioural\nchanges. Notably, we find that more than half of the Russian-tweeting users\nshift towards Ukrainian as a result of the war.\n", "date": "2023-05-04", "categories": []}
{"id": "2210.03971", "title": "An Ordinal Latent Variable Model of Conflict Intensity", "abstract": "  Measuring the intensity of events is crucial for monitoring and tracking\narmed conflict. Advances in automated event extraction have yielded massive\ndata sets of \"who did what to whom\" micro-records that enable data-driven\napproaches to monitoring conflict. The Goldstein scale is a widely-used\nexpert-based measure that scores events on a conflictual-cooperative scale. It\nis based only on the action category (\"what\") and disregards the subject\n(\"who\") and object (\"to whom\") of an event, as well as contextual information,\nlike associated casualty count, that should contribute to the perception of an\nevent's \"intensity\". This paper takes a latent variable-based approach to\nmeasuring conflict intensity. We introduce a probabilistic generative model\nthat assumes each observed event is associated with a latent intensity class. A\nnovel aspect of this model is that it imposes an ordering on the classes, such\nthat higher-valued classes denote higher levels of intensity. The ordinal\nnature of the latent variable is induced from naturally ordered aspects of the\ndata (e.g., casualty counts) where higher values naturally indicate higher\nintensity. We evaluate the proposed model both intrinsically and extrinsically,\nshowing that it obtains comparatively good held-out predictive performance.\n", "date": "2022-10-08", "categories": []}
{"id": "2306.01183", "title": "Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation", "abstract": "  Very large language models (LLMs) perform extremely well on a spectrum of NLP\ntasks in a zero-shot setting. However, little is known about their performance\non human-level NLP problems which rely on understanding psychological concepts,\nsuch as assessing personality traits. In this work, we investigate the\nzero-shot ability of GPT-3 to estimate the Big 5 personality traits from users'\nsocial media posts. Through a set of systematic experiments, we find that\nzero-shot GPT-3 performance is somewhat close to an existing pre-trained SotA\nfor broad classification upon injecting knowledge about the trait in the\nprompts. However, when prompted to provide fine-grained classification, its\nperformance drops to close to a simple most frequent class (MFC) baseline. We\nfurther analyze where GPT-3 performs better, as well as worse, than a\npretrained lexical model, illustrating systematic errors that suggest ways to\nimprove LLMs on human-level NLP tasks.\n", "date": "2023-06-01", "categories": []}
{"id": "2306.00492", "title": "Effect of Monetary Reward on Users' Individual Strategies Using\n  Co-Evolutionary Learning", "abstract": "  Consumer generated media (CGM), such as social networking services rely on\nthe voluntary activity of users to prosper, garnering the psychological rewards\nof feeling connected with other people through comments and reviews received\nonline. To attract more users, some CGM have introduced monetary rewards (MR)\nfor posting activity and quality articles and comments. However, the impact of\nMR on the article posting strategies of users, especially frequency and\nquality, has not been fully analyzed by previous studies, because they ignored\nthe difference in the standpoint in the CGM networks, such as how many\nfriends/followers they have, although we think that their strategies vary with\ntheir standpoints. The purpose of this study is to investigate the impact of MR\non individual users by considering the differences in dominant strategies\nregarding user standpoints. Using the game-theoretic model for CGM, we\nexperimentally show that a variety of realistic dominant strategies are evolved\ndepending on user standpoints in the CGM network, using multiple-world genetic\nalgorithm.\n", "date": "2023-06-01", "categories": []}
{"id": "2305.18397", "title": "Prediction of the 2023 Turkish Presidential Election Results Using\n  Social Media Data", "abstract": "  Social media platforms influence the way political campaigns are run and\ntherefore they have become an increasingly important tool for politicians to\ndirectly interact with citizens. Previous elections in various countries have\nshown that social media data may significantly impact election results. In this\nstudy, we aim to predict the vote shares of parties participating in the 2023\nelections in Turkey by combining social media data from various platforms\ntogether with traditional polling data. Our approach is a volume-based approach\nthat considers the number of social media interactions rather than content. We\ncompare several prediction models across varying time windows. Our results show\nthat for all time windows, the ARIMAX model outperforms the other algorithms.\n", "date": "2023-05-28", "categories": []}
{"id": "2212.01117", "title": "Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning", "abstract": "  The spread of rumors along with breaking events seriously hinders the truth\nin the era of social media. Previous studies reveal that due to the lack of\nannotated resources, rumors presented in minority languages are hard to be\ndetected. Furthermore, the unforeseen breaking events not involved in\nyesterday's news exacerbate the scarcity of data resources. In this work, we\npropose a novel zero-shot framework based on prompt learning to detect rumors\nfalling in different domains or presented in different languages. More\nspecifically, we firstly represent rumor circulated on social media as diverse\npropagation threads, then design a hierarchical prompt encoding mechanism to\nlearn language-agnostic contextual representations for both prompts and rumor\ndata. To further enhance domain adaptation, we model the domain-invariant\nstructural features from the propagation threads, to incorporate structural\nposition representations of influential community response. In addition, a new\nvirtual response augmentation method is used to improve model training.\nExtensive experiments conducted on three real-world datasets demonstrate that\nour proposed model achieves much better performance than state-of-the-art\nmethods and exhibits a superior capacity for detecting rumors at early stages.\n", "date": "2022-12-02", "categories": []}
{"id": "2305.16471", "title": "Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine\n  Learning Analysis of Extraneous Factors in Immigration Court Decisions", "abstract": "  In this study, we introduce a novel two-pronged scoring system to measure\nindividual and systemic bias in immigration courts under the U.S. Executive\nOffice of Immigration Review (EOIR). We analyze nearly 6 million immigration\ncourt proceedings and 228 case features to build on prior research showing that\nU.S. asylum decisions vary dramatically based on factors that are extraneous to\nthe merits of a case. We close a critical gap in the literature of variability\nmetrics that can span space and time. Using predictive modeling, we explain\n58.54% of the total decision variability using two metrics: partisanship and\ninter-judge cohort consistency. Thus, whether the EOIR grants asylum to an\napplicant or not depends in majority on the combined effects of the political\nclimate and the individual variability of the presiding judge - not the\nindividual merits of the case. Using time series analysis, we also demonstrate\nthat partisanship increased in the early 1990s but plateaued following the turn\nof the century. These conclusions are striking to the extent that they diverge\nfrom the U.S. immigration system's commitments to independence and due process.\nOur contributions expose systemic inequities in the U.S. asylum decision-making\nprocess, and we recommend improved and standardized variability metrics to\nbetter diagnose and monitor these issues.\n", "date": "2023-05-25", "categories": []}
{"id": "2305.16641", "title": "Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event\n  Chains of Children's Fairy Tales", "abstract": "  Social biases and stereotypes are embedded in our culture in part through\ntheir presence in our stories, as evidenced by the rich history of humanities\nand social science literature analyzing such biases in children stories.\nBecause these analyses are often conducted manually and at a small scale, such\ninvestigations can benefit from the use of more recent natural language\nprocessing methods that examine social bias in models and data corpora. Our\nwork joins this interdisciplinary effort and makes a unique contribution by\ntaking into account the event narrative structures when analyzing the social\nbias of stories. We propose a computational pipeline that automatically\nextracts a story's temporal narrative verb-based event chain for each of its\ncharacters as well as character attributes such as gender. We also present a\nverb-based event annotation scheme that can facilitate bias analysis by\nincluding categories such as those that align with traditional stereotypes.\nThrough a case study analyzing gender bias in fairy tales, we demonstrate that\nour framework can reveal bias in not only the unigram verb-based events in\nwhich female and male characters participate but also in the temporal narrative\norder of such event participation.\n", "date": "2023-05-26", "categories": []}
{"id": "2305.12376", "title": "Measuring Intersectional Biases in Historical Documents", "abstract": "  Data-driven analyses of biases in historical texts can help illuminate the\norigin and development of biases prevailing in modern society.\n  However, digitised historical documents pose a challenge for NLP\npractitioners as these corpora suffer from errors introduced by optical\ncharacter recognition (OCR) and are written in an archaic language. In this\npaper, we investigate the continuities and transformations of bias in\nhistorical newspapers published in the Caribbean during the colonial era (18th\nto 19th centuries). Our analyses are performed along the axes of gender, race,\nand their intersection. We examine these biases by conducting a temporal study\nin which we measure the development of lexical associations using\ndistributional semantics models and word embeddings. Further, we evaluate the\neffectiveness of techniques designed to process OCR-generated data and assess\ntheir stability when trained on and applied to the noisy historical newspapers.\nWe find that there is a trade-off between the stability of the word embeddings\nand their compatibility with the historical dataset. We provide evidence that\ngender and racial biases are interdependent, and their intersection triggers\ndistinct effects. These findings align with the theory of intersectionality,\nwhich stresses that biases affecting people with multiple marginalised\nidentities compound to more than the sum of their constituents.\n", "date": "2023-05-21", "categories": []}
{"id": "2305.12835", "title": "Open-Domain Event Graph Induction for Mitigating Framing Bias", "abstract": "  Researchers have proposed various information extraction (IE) techniques to\nconvert news articles into structured knowledge for news understanding.\nHowever, none of the existing methods have explicitly addressed the issue of\nframing bias that is inherent in news articles. We argue that studying and\nidentifying framing bias is a crucial step towards trustworthy event\nunderstanding. We propose a novel task, neutral event graph induction, to\naddress this problem. An event graph is a network of events and their temporal\nrelations. Our task aims to induce such structural knowledge with minimal\nframing bias in an open domain. We propose a three-step framework to induce a\nneutral event graph from multiple input sources. The process starts by inducing\nan event graph from each input source, then merging them into one merged event\ngraph, and lastly using a Graph Convolutional Network to remove event nodes\nwith biased connotations. We demonstrate the effectiveness of our framework\nthrough the use of graph prediction metrics and bias-focused metrics.\n", "date": "2023-05-22", "categories": []}
{"id": "2302.02813", "title": "Migration Reframed? A multilingual analysis on the stance shift in\n  Europe during the Ukrainian crisis", "abstract": "  The war in Ukraine seems to have positively changed the attitude toward the\ncritical societal topic of migration in Europe -- at least towards refugees\nfrom Ukraine. We investigate whether this impression is substantiated by how\nthe topic is reflected in online news and social media, thus linking the\nrepresentation of the issue on the Web to its perception in society. For this\npurpose, we combine and adapt leading-edge automatic text processing for a\nnovel multilingual stance detection approach. Starting from 5.5M Twitter posts\npublished by 565 European news outlets in one year, beginning September 2021,\nplus replies, we perform a multilingual analysis of migration-related media\ncoverage and associated social media interaction for Europe and selected\nEuropean countries.\n  The results of our analysis show that there is actually a reframing of the\ndiscussion illustrated by the terminology change, e.g., from \"migrant\" to\n\"refugee\", often even accentuated with phrases such as \"real refugees\".\nHowever, concerning a stance shift in public perception, the picture is more\ndiverse than expected. All analyzed cases show a noticeable temporal stance\nshift around the start of the war in Ukraine. Still, there are apparent\nnational differences in the size and stability of this shift.\n", "date": "2023-02-06", "categories": []}
{"id": "2305.10136", "title": "Additive manifesto decomposition: A policy domain aware method for\n  understanding party positioning", "abstract": "  Automatic extraction of party (dis)similarities from texts such as party\nelection manifestos or parliamentary speeches plays an increasing role in\ncomputational political science. However, existing approaches are fundamentally\nlimited to targeting only global party (dis)-similarity: they condense the\nrelationship between a pair of parties into a single figure, their similarity.\nIn aggregating over all policy domains (e.g., health or foreign policy), they\ndo not provide any qualitative insights into which domains parties agree or\ndisagree on. This paper proposes a workflow for estimating policy domain aware\nparty similarity that overcomes this limitation. The workflow covers (a)\ndefinition of suitable policy domains; (b) automatic labeling of domains, if no\nmanual labels are available; (c) computation of domain-level similarities and\naggregation at a global level; (d) extraction of interpretable party positions\non major policy axes via multidimensional scaling. We evaluate our workflow on\nmanifestos from the German federal elections. We find that our method (a)\nyields high correlation when predicting party similarity at a global level and\n(b) provides accurate party-specific positions, even with automatically\nlabelled policy domains.\n", "date": "2023-05-17", "categories": []}
{"id": "2206.08406", "title": "Predicting Hate Intensity of Twitter Conversation Threads", "abstract": "  Tweets are the most concise form of communication in online social media,\nwherein a single tweet has the potential to make or break the discourse of the\nconversation. Online hate speech is more accessible than ever, and stifling its\npropagation is of utmost importance for social media companies and users for\ncongenial communication. Most of the research barring a recent few has focused\non classifying an individual tweet regardless of the tweet thread/context\nleading up to that point. One of the classical approaches to curb hate speech\nis to adopt a reactive strategy after the hate speech postage. The ex-post\nfacto strategy results in neglecting subtle posts that do not show the\npotential to instigate hate speech on their own but may portend in the\nsubsequent discussion ensuing in the post's replies. In this paper, we propose\nDRAGNET++, which aims to predict the intensity of hatred that a tweet can bring\nin through its reply chain in the future. It uses the semantic and propagating\nstructure of the tweet threads to maximize the contextual information leading\nup to and the fall of hate intensity at each subsequent tweet. We explore three\npublicly available Twitter datasets -- Anti-Racism contains the reply tweets of\na collection of social media discourse on racist remarks during US political\nand Covid-19 background; Anti-Social presents a dataset of 40 million tweets\namidst the COVID-19 pandemic on anti-social behaviours; and Anti-Asian presents\nTwitter datasets collated based on anti-Asian behaviours during COVID-19\npandemic. All the curated datasets consist of structural graph information of\nthe Tweet threads. We show that DRAGNET++ outperforms all the state-of-the-art\nbaselines significantly. It beats the best baseline by an 11% margin on the\nPerson correlation coefficient and a decrease of 25% on RMSE for the\nAnti-Racism dataset with a similar performance on the other two datasets.\n", "date": "2022-06-16", "categories": []}
{"id": "2303.15708", "title": "Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S.\n  News Headlines", "abstract": "  There is a broad consensus that news media outlets incorporate ideological\nbiases in their news articles. However, prior studies on measuring the\ndiscrepancies among media outlets and further dissecting the origins of\nthematic differences suffer from small sample sizes and limited scope and\ngranularity. In this study, we use a large dataset of 1.8 million news\nheadlines from major U.S. media outlets spanning from 2014 to 2022 to\nthoroughly track and dissect the fine-grained thematic discrepancy in U.S. news\nmedia. We employ multiple correspondence analysis (MCA) to quantify the\nfine-grained thematic discrepancy related to four prominent topics - domestic\npolitics, economic issues, social issues, and foreign affairs in order to\nderive a more holistic analysis. Additionally, we compare the most frequent\n$n$-grams in media headlines to provide further qualitative insights into our\nanalysis. Our findings indicate that on domestic politics and social issues,\nthe discrepancy can be attributed to a certain degree of media bias. Meanwhile,\nthe discrepancy in reporting foreign affairs is largely attributed to the\ndiversity in individual journalistic styles. Finally, U.S. media outlets show\nconsistency and high similarity in their coverage of economic issues.\n", "date": "2023-03-28", "categories": []}
{"id": "2304.04811", "title": "A Large-Scale Comparative Study of Accurate COVID-19 Information versus\n  Misinformation", "abstract": "  The COVID-19 pandemic led to an infodemic where an overwhelming amount of\nCOVID-19 related content was being disseminated at high velocity through social\nmedia. This made it challenging for citizens to differentiate between accurate\nand inaccurate information about COVID-19. This motivated us to carry out a\ncomparative study of the characteristics of COVID-19 misinformation versus\nthose of accurate COVID-19 information through a large-scale computational\nanalysis of over 242 million tweets. The study makes comparisons alongside four\nkey aspects: 1) the distribution of topics, 2) the live status of tweets, 3)\nlanguage analysis and 4) the spreading power over time. An added contribution\nof this study is the creation of a COVID-19 misinformation classification\ndataset. Finally, we demonstrate that this new dataset helps improve\nmisinformation classification by more than 9\\% based on average F1 measure.\n", "date": "2023-04-10", "categories": []}
{"id": "2305.04631", "title": "XAI in Computational Linguistics: Understanding Political Leanings in\n  the Slovenian Parliament", "abstract": "  The work covers the development and explainability of machine learning models\nfor predicting political leanings through parliamentary transcriptions. We\nconcentrate on the Slovenian parliament and the heated debate on the European\nmigrant crisis, with transcriptions from 2014 to 2020. We develop both\nclassical machine learning and transformer language models to predict the left-\nor right-leaning of parliamentarians based on their given speeches on the topic\nof migrants. With both types of models showing great predictive success, we\ncontinue with explaining their decisions. Using explainability techniques, we\nidentify keywords and phrases that have the strongest influence in predicting\npolitical leanings on the topic, with left-leaning parliamentarians using\nconcepts such as people and unity and speak about refugees, and right-leaning\nparliamentarians using concepts such as nationality and focus more on illegal\nmigrants. This research is an example that understanding the reasoning behind\npredictions can not just be beneficial for AI engineers to improve their\nmodels, but it can also be helpful as a tool in the qualitative analysis steps\nin interdisciplinary research.\n", "date": "2023-05-08", "categories": []}
{"id": "2305.03336", "title": "QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion\n  Techniques Detection using Multilingual Models", "abstract": "  Misinformation spreading in mainstream and social media has been misleading\nusers in different ways. Manual detection and verification efforts by\njournalists and fact-checkers can no longer cope with the great scale and quick\nspread of misleading information. This motivated research and industry efforts\nto develop systems for analyzing and verifying news spreading online. The\nSemEval-2023 Task 3 is an attempt to address several subtasks under this\noverarching problem, targeting writing techniques used in news articles to\naffect readers' opinions. The task addressed three subtasks with six languages,\nin addition to three ``surprise'' test languages, resulting in 27 different\ntest setups. This paper describes our participating system to this task. Our\nteam is one of the 6 teams that successfully submitted runs for all setups. The\nofficial results show that our system is ranked among the top 3 systems for 10\nout of the 27 setups.\n", "date": "2023-05-05", "categories": []}
{"id": "2305.00182", "title": "Examining European Press Coverage of the Covid-19 No-Vax Movement: An\n  NLP Framework", "abstract": "  This paper examines how the European press dealt with the no-vax reactions\nagainst the Covid-19 vaccine and the dis- and misinformation associated with\nthis movement. Using a curated dataset of 1786 articles from 19 European\nnewspapers on the anti-vaccine movement over a period of 22 months in\n2020-2021, we used Natural Language Processing techniques including topic\nmodeling, sentiment analysis, semantic relationship with word embeddings,\npolitical analysis, named entity recognition, and semantic networks, to\nunderstand the specific role of the European traditional press in the\ndisinformation ecosystem. The results of this multi-angle analysis demonstrate\nthat the European well-established press actively opposed a variety of hoaxes\nmainly spread on social media, and was critical of the anti-vax trend,\nregardless of the political orientation of the newspaper. This confirms the\nrelevance of studying the role of high-quality press in the disinformation\necosystem.\n", "date": "2023-04-29", "categories": []}
{"id": "2304.14456", "title": "Framing the News:From Human Perception to Large Language Model\n  Inferences", "abstract": "  Identifying the frames of news is important to understand the articles'\nvision, intention, message to be conveyed, and which aspects of the news are\nemphasized. Framing is a widely studied concept in journalism, and has emerged\nas a new topic in computing, with the potential to automate processes and\nfacilitate the work of journalism professionals. In this paper, we study this\nissue with articles related to the Covid-19 anti-vaccine movement. First, to\nunderstand the perspectives used to treat this theme, we developed a protocol\nfor human labeling of frames for 1786 headlines of No-Vax movement articles of\nEuropean newspapers from 5 countries. Headlines are key units in the written\npress, and worth of analysis as many people only read headlines (or use them to\nguide their decision for further reading.) Second, considering advances in\nNatural Language Processing (NLP) with large language models, we investigated\ntwo approaches for frame inference of news headlines: first with a GPT-3.5\nfine-tuning approach, and second with GPT-3.5 prompt-engineering. Our work\ncontributes to the study and analysis of the performance that these models have\nto facilitate journalistic tasks like classification of frames, while\nunderstanding whether the models are able to replicate human perception in the\nidentification of these frames.\n", "date": "2023-04-27", "categories": []}
{"id": "2304.05232", "title": "Lady and the Tramp Nextdoor: Online Manifestations of Economic\n  Inequalities in the Nextdoor Social Network", "abstract": "  From health to education, income impacts a huge range of life choices.\nEarlier research has leveraged data from online social networks to study\nprecisely this impact. In this paper, we ask the opposite question: do\ndifferent levels of income result in different online behaviors? We demonstrate\nit does. We present the first large-scale study of Nextdoor, a popular\nlocation-based social network. We collect 2.6 Million posts from 64,283\nneighborhoods in the United States and 3,325 neighborhoods in the United\nKingdom, to examine whether online discourse reflects the income and income\ninequality of a neighborhood. We show that posts from neighborhoods with\ndifferent incomes indeed differ, e.g. richer neighborhoods have a more positive\nsentiment and discuss crimes more, even though their actual crime rates are\nmuch lower. We then show that user-generated content can predict both income\nand inequality. We train multiple machine learning models and predict both\nincome (R-squared=0.841) and inequality (R-squared=0.77).\n", "date": "2023-04-11", "categories": []}
{"id": "2304.13148", "title": "Introducing MBIB -- the first Media Bias Identification Benchmark Task\n  and Dataset Collection", "abstract": "  Although media bias detection is a complex multi-task problem, there is, to\ndate, no unified benchmark grouping these evaluation tasks. We introduce the\nMedia Bias Identification Benchmark (MBIB), a comprehensive benchmark that\ngroups different types of media bias (e.g., linguistic, cognitive, political)\nunder a common framework to test how prospective detection techniques\ngeneralize. After reviewing 115 datasets, we select nine tasks and carefully\npropose 22 associated datasets for evaluating media bias detection techniques.\nWe evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5,\nBART). Our results suggest that while hate speech, racial bias, and gender bias\nare easier to detect, models struggle to handle certain bias types, e.g.,\ncognitive and political bias. However, our results show that no single\ntechnique can outperform all the others significantly. We also find an uneven\ndistribution of research interest and resource allocation to the individual\ntasks in media bias. A unified benchmark encourages the development of more\nrobust systems and shifts the current paradigm in media bias detection\nevaluation towards solutions that tackle not one but multiple media bias types\nsimultaneously.\n", "date": "2023-04-25", "categories": []}
{"id": "2304.11367", "title": "Detecting Political Opinions in Tweets through Bipartite Graph Analysis:\n  A Skip Aggregation Graph Convolution Approach", "abstract": "  Public opinion is a crucial factor in shaping political decision-making.\nNowadays, social media has become an essential platform for individuals to\nengage in political discussions and express their political views, presenting\nresearchers with an invaluable resource for analyzing public opinion. In this\npaper, we focus on the 2020 US presidential election and create a large-scale\ndataset from Twitter. To detect political opinions in tweets, we build a\nuser-tweet bipartite graph based on users' posting and retweeting behaviors and\nconvert the task into a Graph Neural Network (GNN)-based node classification\nproblem. Then, we introduce a novel skip aggregation mechanism that makes tweet\nnodes aggregate information from second-order neighbors, which are also tweet\nnodes due to the graph's bipartite nature, effectively leveraging user\nbehavioral information. The experimental results show that our proposed model\nsignificantly outperforms several competitive baselines. Further analyses\ndemonstrate the significance of user behavioral information and the\neffectiveness of skip aggregation.\n", "date": "2023-04-22", "categories": []}
{"id": "2304.11666", "title": "Hold the Suspect! : An Analysis on Media Framing of Itaewon Halloween\n  Crowd Crush", "abstract": "  Based on the 10.9K articles from top 40 news providers of South Korea, this\npaper analyzed the media framing of Itaewon Halloween Crowd Crush during the\nfirst 72 hours after the incident. By adopting word-vector embedding and\nclustering, we figured out that conservative media focused on political\nparties' responses and the suspect's identity while the liberal media covered\nthe responsibility of the government and possible unequal spillover effect on\nthe low-income industry workers. Although the social tragedy was not directly\nconnected to institutional politics, the media clearly exhibited political bias\nin the coverage process.\n", "date": "2023-04-23", "categories": []}
{"id": "2304.09982", "title": "Radar de Parit\\'e: An NLP system to measure gender representation in\n  French news stories", "abstract": "  We present the Radar de Parit\\'e, an automated Natural Language Processing\n(NLP) system that measures the proportion of women and men quoted daily in six\nCanadian French-language media outlets. We outline the system's architecture\nand detail the challenges we overcame to address French-specific issues, in\nparticular regarding coreference resolution, a new contribution to the NLP\nliterature on French. We also showcase statistics covering over one year's\nworth of data (282,512 news articles). Our results highlight the\nunderrepresentation of women in news stories, while also illustrating the\napplication of modern NLP methods to measure gender representation and address\nsocietal issues.\n", "date": "2023-04-19", "categories": []}
{"id": "2304.09339", "title": "The Unintended Consequences of Censoring Digital Technology -- Evidence\n  from Italy's ChatGPT Ban", "abstract": "  We analyse the effects of the ban of ChatGPT, a generative pre-trained\ntransformer chatbot, on individual productivity. We first compile data on the\nhourly coding output of over 8,000 professional GitHub users in Italy and other\nEuropean countries to analyse the impact of the ban on individual productivity.\nCombining the high-frequency data with the sudden announcement of the ban in a\ndifference-in-differences framework, we find that the output of Italian\ndevelopers decreased by around 50% in the first two business days after the ban\nand recovered after that. Applying a synthetic control approach to daily Google\nsearch and Tor usage data shows that the ban led to a significant increase in\nthe use of censorship bypassing tools. Our findings show that users swiftly\nimplement strategies to bypass Internet restrictions but this adaptation\nactivity creates short-term disruptions and hampers productivity.\n", "date": "2023-04-18", "categories": []}
{"id": "2304.08759", "title": "Exoskeleton for the Mind: Exploring Strategies Against Misinformation\n  with a Metacognitive Agent", "abstract": "  Misinformation is a global problem in modern social media platforms with few\nsolutions known to be effective. Social media platforms have offered tools to\nraise awareness of information, but these are closed systems that have not been\nempirically evaluated. Others have developed novel tools and strategies, but\nmost have been studied out of context using static stimuli, researcher prompts,\nor low fidelity prototypes. We offer a new anti-misinformation agent grounded\nin theories of metacognition that was evaluated within Twitter. We report on a\npilot study (n=17) and multi-part experimental study (n=57, n=49) where\nparticipants experienced three versions of the agent, each deploying a\ndifferent strategy. We found that no single strategy was superior over the\ncontrol. We also confirmed the necessity of transparency and clarity about the\nagent's underlying logic, as well as concerns about repeated exposure to\nmisinformation and lack of user engagement.\n", "date": "2023-04-18", "categories": []}
{"id": "2301.06660", "title": "VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19\n  Vaccination on Twitter", "abstract": "  Vaccine hesitancy has been a common concern, probably since vaccines were\ncreated and, with the popularisation of social media, people started to express\ntheir concerns about vaccines online alongside those posting pro- and\nanti-vaccine content. Predictably, since the first mentions of a COVID-19\nvaccine, social media users posted about their fears and concerns or about\ntheir support and belief into the effectiveness of these rapidly developing\nvaccines. Identifying and understanding the reasons behind public hesitancy\ntowards COVID-19 vaccines is important for policy markers that need to develop\nactions to better inform the population with the aim of increasing vaccine\ntake-up. In the case of COVID-19, where the fast development of the vaccines\nwas mirrored closely by growth in anti-vaxx disinformation, automatic means of\ndetecting citizen attitudes towards vaccination became necessary. This is an\nimportant computational social sciences task that requires data analysis in\norder to gain in-depth understanding of the phenomena at hand. Annotated data\nis also necessary for training data-driven models for more nuanced analysis of\nattitudes towards vaccination. To this end, we created a new collection of over\n3,101 tweets annotated with users' attitudes towards COVID-19 vaccination\n(stance). Besides, we also develop a domain-specific language model (VaxxBERT)\nthat achieves the best predictive performance (73.0 accuracy and 69.3 F1-score)\nas compared to a robust set of baselines. To the best of our knowledge, these\nare the first dataset and model that model vaccine hesitancy as a category\ndistinct from pro- and anti-vaccine stance.\n", "date": "2023-01-17", "categories": []}
{"id": "2304.07535", "title": "From Online Behaviours to Images: A Novel Approach to Social Bot\n  Detection", "abstract": "  Online Social Networks have revolutionized how we consume and share\ninformation, but they have also led to a proliferation of content not always\nreliable and accurate. One particular type of social accounts is known to\npromote unreputable content, hyperpartisan, and propagandistic information.\nThey are automated accounts, commonly called bots. Focusing on Twitter\naccounts, we propose a novel approach to bot detection: we first propose a new\nalgorithm that transforms the sequence of actions that an account performs into\nan image; then, we leverage the strength of Convolutional Neural Networks to\nproceed with image classification. We compare our performances with\nstate-of-the-art results for bot detection on genuine accounts / bot accounts\ndatasets well known in the literature. The results confirm the effectiveness of\nthe proposal, because the detection capability is on par with the state of the\nart, if not better in some cases.\n", "date": "2023-04-15", "categories": []}
{"id": "2209.08697", "title": "Quantifying How Hateful Communities Radicalize Online Users", "abstract": "  While online social media offers a way for ignored or stifled voices to be\nheard, it also allows users a platform to spread hateful speech. Such speech\nusually originates in fringe communities, yet it can spill over into mainstream\nchannels. In this paper, we measure the impact of joining fringe hateful\ncommunities in terms of hate speech propagated to the rest of the social\nnetwork. We leverage data from Reddit to assess the effect of joining one type\nof echo chamber: a digital community of like-minded users exhibiting hateful\nbehavior. We measure members' usage of hate speech outside the studied\ncommunity before and after they become active participants. Using Interrupted\nTime Series (ITS) analysis as a causal inference method, we gauge the spillover\neffect, in which hateful language from within a certain community can spread\noutside that community by using the level of out-of-community hate word usage\nas a proxy for learned hate. We investigate four different Reddit\nsub-communities (subreddits) covering three areas of hate speech: racism,\nmisogyny and fat-shaming. In all three cases we find an increase in hate speech\noutside the originating community, implying that joining such community leads\nto a spread of hate speech throughout the platform. Moreover, users are found\nto pick up this new hateful speech for months after initially joining the\ncommunity. We show that the harmful speech does not remain contained within the\ncommunity. Our results provide new evidence of the harmful effects of echo\nchambers and the potential benefit of moderating them to reduce adoption of\nhateful speech.\n", "date": "2022-09-19", "categories": []}
{"id": "2304.04610", "title": "Attention at SemEval-2023 Task 10: Explainable Detection of Online\n  Sexism (EDOS)", "abstract": "  In this paper, we have worked on interpretability, trust, and understanding\nof the decisions made by models in the form of classification tasks. The task\nis divided into 3 subtasks. The first task consists of determining Binary\nSexism Detection. The second task describes the Category of Sexism. The third\ntask describes a more Fine-grained Category of Sexism. Our work explores\nsolving these tasks as a classification problem by fine-tuning\ntransformer-based architecture. We have performed several experiments with our\narchitecture, including combining multiple transformers, using domain adaptive\npretraining on the unlabelled dataset provided by Reddit and Gab, Joint\nlearning, and taking different layers of transformers as input to a\nclassification head. Our system (with team name Attention) was able to achieve\na macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and\n0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later we\nimproved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.\n", "date": "2023-04-10", "categories": []}
{"id": "2301.08347", "title": "Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During\n  the 2022 Russo-Ukrainian Conflict", "abstract": "  This paper proposes a novel lexicon-based unsupervised sentimental analysis\nmethod to measure the $``\\textit{hope}\"$ and $``\\textit{fear}\"$ for the 2022\nUkrainian-Russian Conflict. $\\textit{Reddit.com}$ is utilised as the main\nsource of human reactions to daily events during nearly the first three months\nof the conflict. The top 50 $``hot\"$ posts of six different subreddits about\nUkraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict,\nUkraineWarVideoReport, UkraineWarReports) and their relative comments are\nscraped and a data set is created. On this corpus, multiple analyses such as\n(1) public interest, (2) hope/fear score, (3) stock price interaction are\nemployed. We promote using a dictionary approach, which scores the hopefulness\nof every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm\nof topic modelling is also utilised to understand the main issues raised by\nusers and what are the key talking points. Experimental analysis shows that the\nhope strongly decreases after the symbolic and strategic losses of Azovstal\n(Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and\nnegatives, are present after important battles, but also some non-military\nevents, such as Eurovision and football games.\n", "date": "2023-01-19", "categories": []}
{"id": "2304.03414", "title": "Towards Corpus-Scale Discovery of Selection Biases in News Coverage:\n  Comparing What Sources Say About Entities as a Start", "abstract": "  News sources undergo the process of selecting newsworthy information when\ncovering a certain topic. The process inevitably exhibits selection biases,\ni.e. news sources' typical patterns of choosing what information to include in\nnews coverage, due to their agenda differences. To understand the magnitude and\nimplications of selection biases, one must first discover (1) on what topics do\nsources typically have diverging definitions of \"newsworthy\" information, and\n(2) do the content selection patterns correlate with certain attributes of the\nnews sources, e.g. ideological leaning, etc.\n  The goal of the paper is to investigate and discuss the challenges of\nbuilding scalable NLP systems for discovering patterns of media selection\nbiases directly from news content in massive-scale news corpora, without\nrelying on labeled data. To facilitate research in this domain, we propose and\nstudy a conceptual framework, where we compare how sources typically mention\ncertain controversial entities, and use such as indicators for the sources'\ncontent selection preferences. We empirically show the capabilities of the\nframework through a case study on NELA-2020, a corpus of 1.8M news articles in\nEnglish from 519 news sources worldwide. We demonstrate an unsupervised\nrepresentation learning method to capture the selection preferences for how\nsources typically mention controversial entities. Our experiments show that\nthat distributional divergence of such representations, when studied\ncollectively across entities and news sources, serve as good indicators for an\nindividual source's ideological leaning. We hope our findings will provide\ninsights for future research on media selection biases.\n", "date": "2023-04-06", "categories": []}
{"id": "2302.12126", "title": "KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate\n  Political Stance Prediction", "abstract": "  The political stance prediction for news articles has been widely studied to\nmitigate the echo chamber effect -- people fall into their thoughts and\nreinforce their pre-existing beliefs. The previous works for the political\nstance problem focus on (1) identifying political factors that could reflect\nthe political stance of a news article and (2) capturing those factors\neffectively. Despite their empirical successes, they are not sufficiently\njustified in terms of how effective their identified factors are in the\npolitical stance prediction. Motivated by this, in this work, we conduct a user\nstudy to investigate important factors in political stance prediction, and\nobserve that the context and tone of a news article (implicit) and external\nknowledge for real-world entities appearing in the article (explicit) are\nimportant in determining its political stance. Based on this observation, we\npropose a novel knowledge-aware approach to political stance prediction (KHAN),\nemploying (1) hierarchical attention networks (HAN) to learn the relationships\namong words and sentences in three different levels and (2) knowledge encoding\n(KE) to incorporate external knowledge for real-world entities into the process\nof political stance prediction. Also, to take into account the subtle and\nimportant difference between opposite political stances, we build two\nindependent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by\nourselves and learn to fuse the different political knowledge. Through\nextensive evaluations on three real-world datasets, we demonstrate the\nsuperiority of DASH in terms of (1) accuracy, (2) efficiency, and (3)\neffectiveness.\n", "date": "2023-02-23", "categories": []}
{"id": "2301.04341", "title": "A Meta Path-based Approach for Rumor Detection on Social Media", "abstract": "  The prominent role of social media in people's daily lives has made them more\ninclined to receive news through social networks than traditional sources. This\nshift in public behavior has opened doors for some to diffuse fake news on\nsocial media; and subsequently cause negative economic, political, and social\nconsequences as well as distrust among the public.\n  There are many proposed methods to solve the rumor detection problem, most of\nwhich do not take full advantage of the heterogeneous nature of news\npropagation networks. With this intention, we considered a previously proposed\narchitecture as our baseline and performed the idea of structural feature\nextraction from the heterogeneous rumor propagation over its architecture using\nthe concept of meta path-based embeddings. We named our model Meta Path-based\nGlobal Local Attention Network (MGLAN). Extensive experimental analysis on\nthree state-of-the-art datasets has demonstrated that MGLAN outperforms other\nmodels by capturing node-level discrimination to different node types.\n", "date": "2023-01-11", "categories": []}
{"id": "2304.00514", "title": "The impact of individual information exchange strategies on the\n  distribution of social wealth", "abstract": "  Wealth distribution is a complex and critical aspect of any society.\nInformation exchange is considered to have played a role in shaping wealth\ndistribution patterns, but the specific dynamic mechanism is still unclear. In\nthis research, we used simulation-based methods to investigate the impact of\ndifferent modes of information exchange on wealth distribution. We compared\ndifferent combinations of information exchange strategies and moving\nstrategies, analyzed their impact on wealth distribution using classic wealth\ndistribution indicators such as the Gini coefficient. Our findings suggest that\ninformation exchange strategies have significant impact on wealth distribution\nand that promoting more equitable access to information and resources is\ncrucial in building a just and equitable society for all.\n", "date": "2023-04-02", "categories": []}
{"id": "2303.18049", "title": "No Place to Hide: Dual Deep Interaction Channel Network for Fake News\n  Detection based on Data Augmentation", "abstract": "  Online Social Network (OSN) has become a hotbed of fake news due to the low\ncost of information dissemination. Although the existing methods have made many\nattempts in news content and propagation structure, the detection of fake news\nis still facing two challenges: one is how to mine the unique key features and\nevolution patterns, and the other is how to tackle the problem of small samples\nto build the high-performance model. Different from popular methods which take\nfull advantage of the propagation topology structure, in this paper, we propose\na novel framework for fake news detection from perspectives of semantic,\nemotion and data enhancement, which excavates the emotional evolution patterns\nof news participants during the propagation process, and a dual deep\ninteraction channel network of semantic and emotion is designed to obtain a\nmore comprehensive and fine-grained news representation with the consideration\nof comments. Meanwhile, the framework introduces a data enhancement module to\nobtain more labeled data with high quality based on confidence which further\nimproves the performance of the classification model. Experiments show that the\nproposed approach outperforms the state-of-the-art methods.\n", "date": "2023-03-31", "categories": []}
{"id": "2303.16759", "title": "Exploring celebrity influence on public attitude towards the COVID-19\n  pandemic: social media shared sentiment analysis", "abstract": "  The COVID-19 pandemic has introduced new opportunities for health\ncommunication, including an increase in the public use of online outlets for\nhealth-related emotions. People have turned to social media networks to share\nsentiments related to the impacts of the COVID-19 pandemic. In this paper we\nexamine the role of social messaging shared by Persons in the Public Eye (i.e.\nathletes, politicians, news personnel) in determining overall public discourse\ndirection. We harvested approximately 13 million tweets ranging from 1 January\n2020 to 1 March 2022. The sentiment was calculated for each tweet using a\nfine-tuned DistilRoBERTa model, which was used to compare COVID-19\nvaccine-related Twitter posts (tweets) that co-occurred with mentions of People\nin the Public Eye. Our findings suggest the presence of consistent patterns of\nemotional content co-occurring with messaging shared by Persons in the Public\nEye for the first two years of the COVID-19 pandemic influenced public opinion\nand largely stimulated online public discourse. We demonstrate that as the\npandemic progressed, public sentiment shared on social networks was shaped by\nrisk perceptions, political ideologies and health-protective behaviours shared\nby Persons in the Public Eye, often in a negative light.\n", "date": "2023-02-23", "categories": []}
{"id": "2303.16777", "title": "Not cool, calm or collected: Using emotional language to detect COVID-19\n  misinformation", "abstract": "  COVID-19 misinformation on social media platforms such as twitter is a threat\nto effective pandemic management. Prior works on tweet COVID-19 misinformation\nnegates the role of semantic features common to twitter such as charged\nemotions. Thus, we present a novel COVID-19 misinformation model, which uses\nboth a tweet emotion encoder and COVID-19 misinformation encoder to predict\nwhether a tweet contains COVID-19 misinformation. Our emotion encoder was\nfine-tuned on a novel annotated dataset and our COVID-19 misinformation encoder\nwas fine-tuned on a subset of the COVID-HeRA dataset. Experimental results show\nsuperior results using the combination of emotion and misinformation encoders\nas opposed to a misinformation classifier alone. Furthermore, extensive result\nanalysis was conducted, highlighting low quality labels and mismatched label\ndistributions as key limitations to our study.\n", "date": "2023-03-27", "categories": []}
{"id": "2303.16779", "title": "Language Models Trained on Media Diets Can Predict Public Opinion", "abstract": "  Public opinion reflects and shapes societal behavior, but the traditional\nsurvey-based tools to measure it are limited. We introduce a novel approach to\nprobe media diet models -- language models adapted to online news, TV\nbroadcast, or radio show content -- that can emulate the opinions of\nsubpopulations that have consumed a set of media. To validate this method, we\nuse as ground truth the opinions expressed in U.S. nationally representative\nsurveys on COVID-19 and consumer confidence. Our studies indicate that this\napproach is (1) predictive of human judgements found in survey response\ndistributions and robust to phrasing and channels of media exposure, (2) more\naccurate at modeling people who follow media more closely, and (3) aligned with\nliterature on which types of opinions are affected by media consumption.\nProbing language models provides a powerful new method for investigating media\neffects, has practical applications in supplementing polls and forecasting\npublic opinion, and suggests a need for further study of the surprising\nfidelity with which neural language models can predict human responses.\n", "date": "2023-03-28", "categories": []}
{"id": "2303.14309", "title": "The Challenges of Studying Misinformation on Video-Sharing Platforms\n  During Crises and Mass-Convergence Events", "abstract": "  Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs).\nDespite the growing use of VSPs, there has not been a proportional increase in\nour ability to understand this medium and the messages conveyed through it. In\nthis work, we draw on our prior experiences to outline three core challenges\nfaced in studying VSPs in high-stakes and fast-paced settings: (1) navigating\nthe unique affordances of VSPs, (2) understanding VSP content and determining\nits authenticity, and (3) novel user behaviors on VSPs for spreading\nmisinformation. By highlighting these challenges, we hope that researchers can\nreflect on how to adapt existing research methods and tools to these new\ncontexts, or develop entirely new ones.\n", "date": "2023-03-25", "categories": []}
{"id": "2303.12808", "title": "PACO: Provocation Involving Action, Culture, and Oppression", "abstract": "  In India, people identify with a particular group based on certain attributes\nsuch as religion. The same religious groups are often provoked against each\nother. Previous studies show the role of provocation in increasing tensions\nbetween India's two prominent religious groups: Hindus and Muslims. With the\nadvent of the Internet, such provocation also surfaced on social media\nplatforms such as WhatsApp.\n  By leveraging an existing dataset of Indian WhatsApp posts, we identified\nthree categories of provoking sentences against Indian Muslims. Further, we\nlabeled 7,000 sentences for three provocation categories and called this\ndataset PACO. We leveraged PACO to train a model that can identify provoking\nsentences from a WhatsApp post. Our best model is fine-tuned RoBERTa and\nachieved a 0.851 average AUC score over five-fold cross-validation.\nAutomatically identifying provoking sentences could stop provoking text from\nreaching out to the masses, and can prevent possible discrimination or violence\nagainst the target religious group.\n  Further, we studied the provocative speech through a pragmatic lens, by\nidentifying the dialog acts and impoliteness super-strategies used against the\nreligious group.\n", "date": "2023-03-19", "categories": []}
{"id": "2303.13452", "title": "Human Behavior in the Time of COVID-19: Learning from Big Data", "abstract": "  Since the World Health Organization (WHO) characterized COVID-19 as a\npandemic in March 2020, there have been over 600 million confirmed cases of\nCOVID-19 and more than six million deaths as of October 2022. The relationship\nbetween the COVID-19 pandemic and human behavior is complicated. On one hand,\nhuman behavior is found to shape the spread of the disease. On the other hand,\nthe pandemic has impacted and even changed human behavior in almost every\naspect. To provide a holistic understanding of the complex interplay between\nhuman behavior and the COVID-19 pandemic, researchers have been employing big\ndata techniques such as natural language processing, computer vision, audio\nsignal processing, frequent pattern mining, and machine learning. In this\nstudy, we present an overview of the existing studies on using big data\ntechniques to study human behavior in the time of the COVID-19 pandemic. In\nparticular, we categorize these studies into three groups - using big data to\nmeasure, model, and leverage human behavior, respectively. The related tasks,\ndata, and methods are summarized accordingly. To provide more insights into how\nto fight the COVID-19 pandemic and future global catastrophes, we further\ndiscuss challenges and potential opportunities.\n", "date": "2023-03-23", "categories": []}
{"id": "2303.10311", "title": "On the rise of fear speech in online social media", "abstract": "  Recently, social media platforms are heavily moderated to prevent the spread\nof online hate speech, which is usually fertile in toxic words and is directed\ntoward an individual or a community. Owing to such heavy moderation, newer and\nmore subtle techniques are being deployed. One of the most striking among these\nis fear speech. Fear speech, as the name suggests, attempts to incite fear\nabout a target community. Although subtle, it might be highly effective, often\npushing communities toward a physical conflict. Therefore, understanding their\nprevalence in social media is of paramount importance. This article presents a\nlarge-scale study to understand the prevalence of 400K fear speech and over\n700K hate speech posts collected from Gab.com. Remarkably, users posting a\nlarge number of fear speech accrue more followers and occupy more central\npositions in social networks than users posting a large number of hate speech.\nThey can also reach out to benign users more effectively than hate speech users\nthrough replies, reposts, and mentions. This connects to the fact that, unlike\nhate speech, fear speech has almost zero toxic content, making it look\nplausible. Moreover, while fear speech topics mostly portray a community as a\nperpetrator using a (fake) chain of argumentation, hate speech topics hurl\ndirect multitarget insults, thus pointing to why general users could be more\ngullible to fear speech. Our findings transcend even to other platforms\n(Twitter and Facebook) and thus necessitate using sophisticated moderation\npolicies and mass awareness to combat fear speech.\n", "date": "2023-03-18", "categories": []}
{"id": "2303.06433", "title": "Reinforcement Learning-based Counter-Misinformation Response Generation:\n  A Case Study of COVID-19 Vaccine Misinformation", "abstract": "  The spread of online misinformation threatens public health, democracy, and\nthe broader society. While professional fact-checkers form the first line of\ndefense by fact-checking popular false claims, they do not engage directly in\nconversations with misinformation spreaders. On the other hand, non-expert\nordinary users act as eyes-on-the-ground who proactively counter misinformation\n-- recent research has shown that 96% counter-misinformation responses are made\nby ordinary users. However, research also found that 2/3 times, these responses\nare rude and lack evidence. This work seeks to create a counter-misinformation\nresponse generation model to empower users to effectively correct\nmisinformation. This objective is challenging due to the absence of datasets\ncontaining ground-truth of ideal counter-misinformation responses, and the lack\nof models that can generate responses backed by communication theories. In this\nwork, we create two novel datasets of misinformation and counter-misinformation\nresponse pairs from in-the-wild social media and crowdsourcing from\ncollege-educated students. We annotate the collected data to distinguish poor\nfrom ideal responses that are factual, polite, and refute misinformation. We\npropose MisinfoCorrect, a reinforcement learning-based framework that learns to\ngenerate counter-misinformation responses for an input misinformation post. The\nmodel rewards the generator to increase the politeness, factuality, and\nrefutation attitude while retaining text fluency and relevancy. Quantitative\nand qualitative evaluation shows that our model outperforms several baselines\nby generating high-quality counter-responses. This work illustrates the promise\nof generative text models for social good -- here, to help create a safe and\nreliable information ecosystem. The code and data is accessible on\nhttps://github.com/claws-lab/MisinfoCorrect.\n", "date": "2023-03-11", "categories": []}
{"id": "2303.05401", "title": "Early Warning Signals of Social Instabilities in Twitter Data", "abstract": "  The goal of this project is to create and study novel techniques to identify\nearly warning signals for socially disruptive events, like riots, wars, or\nrevolutions using only publicly available data on social media. Such techniques\nneed to be robust enough to work on real-time data: to achieve this goal we\npropose a topological approach together with more standard BERT models. Indeed,\ntopology-based algorithms, being provably stable against deformations and\nnoise, seem to work well in low-data regimes. The general idea is to build a\nbinary classifier that predicts if a given tweet is related to a disruptive\nevent or not. The results indicate that the persistent-gradient approach is\nstable and even more performant than deep-learning-based anomaly detection\nalgorithms. We also benchmark the generalisability of the methodology against\nout-of-samples tasks, with very promising results.\n", "date": "2023-03-03", "categories": []}
{"id": "2208.04873", "title": "Kill Chaos with Kindness: Agreeableness Improves Team Performance Under\n  Uncertainty", "abstract": "  Teams are central to human accomplishment. Over the past half-century,\npsychologists have identified the Big-Five cross-culturally valid personality\nvariables: Neuroticism, Extraversion, Openness, Conscientiousness, and\nAgreeableness. The first four have shown consistent relationships with team\nperformance. Agreeableness (being harmonious, altruistic, humble, and\ncooperative), however, has demonstrated a non-significant and highly variable\nrelationship with team performance. We resolve this inconsistency through\ncomputational modelling. An agent-based model (ABM) is used to predict the\neffects of personality traits on teamwork and a genetic algorithm is then used\nto explore the limits of the ABM in order to discover which traits correlate\nwith best and worst performing teams for a problem with different levels of\nuncertainty (noise). New dependencies revealed by the exploration are\ncorroborated by analyzing previously-unseen data from one the largest datasets\non team performance to date comprising 3,698 individuals in 593 teams working\non more than 5,000 group tasks with and without uncertainty, collected over a\n10-year period. Our finding is that the dependency between team performance and\nAgreeableness is moderated by task uncertainty. Combining evolutionary\ncomputation with ABMs in this way provides a new methodology for the scientific\ninvestigation of teamwork, making new predictions, and improving our\nunderstanding of human behaviors. Our results confirm the potential usefulness\nof computer modelling for developing theory, as well as shedding light on the\nfuture of teams as work environments are becoming increasingly fluid and\nuncertain.\n", "date": "2022-08-09", "categories": []}
{"id": "2301.09507", "title": "Characterizing Polarization in Social Networks using the Signed\n  Relational Latent Distance Model", "abstract": "  Graph representation learning has become a prominent tool for the\ncharacterization and understanding of the structure of networks in general and\nsocial networks in particular. Typically, these representation learning\napproaches embed the networks into a low-dimensional space in which the role of\neach individual can be characterized in terms of their latent position. A major\ncurrent concern in social networks is the emergence of polarization and filter\nbubbles promoting a mindset of \"us-versus-them\" that may be defined by extreme\npositions believed to ultimately lead to political violence and the erosion of\ndemocracy. Such polarized networks are typically characterized in terms of\nsigned links reflecting likes and dislikes. We propose the latent Signed\nrelational Latent dIstance Model (SLIM) utilizing for the first time the\nSkellam distribution as a likelihood function for signed networks and extend\nthe modeling to the characterization of distinct extreme positions by\nconstraining the embedding space to polytopes. On four real social signed\nnetworks of polarization, we demonstrate that the model extracts\nlow-dimensional characterizations that well predict friendships and animosity\nwhile providing interpretable visualizations defined by extreme positions when\nendowing the model with an embedding space restricted to polytopes.\n", "date": "2023-01-23", "categories": []}
{"id": "2303.01241", "title": "PANACEA: An Automated Misinformation Detection System on COVID-19", "abstract": "  In this demo, we introduce a web-based misinformation detection system\nPANACEA on COVID-19 related claims, which has two modules, fact-checking and\nrumour detection. Our fact-checking module, which is supported by novel natural\nlanguage inference methods with a self-attention network, outperforms\nstate-of-the-art approaches. It is also able to give automated veracity\nassessment and ranked supporting evidence with the stance towards the claim to\nbe checked. In addition, PANACEA adapts the bi-directional graph convolutional\nnetworks model, which is able to detect rumours based on comment networks of\nrelated tweets, instead of relying on the knowledge base. This rumour detection\nmodule assists by warning the users in the early stages when a knowledge base\nmay not be available.\n", "date": "2023-02-28", "categories": []}
{"id": "2302.13782", "title": "Neural networks for learning personality traits from natural language", "abstract": "  Personality is considered one of the most influential research topics in\npsychology, as it predicts many consequential outcomes such as mental and\nphysical health and explains human behaviour. With the widespread use of social\nnetworks as a means of communication, it is becoming increasingly important to\ndevelop models that can automatically and accurately read the essence of\nindividuals based solely on their writing. In particular, the convergence of\nsocial and computer sciences has led researchers to develop automatic\napproaches for extracting and studying \"hidden\" information in textual data on\nthe internet. The nature of this thesis project is highly experimental, and the\nmotivation behind this work is to present detailed analyses on the topic, as\ncurrently there are no significant investigations of this kind. The objective\nis to identify an adequate semantic space that allows for defining the\npersonality of the object to which a certain text refers. The starting point is\na dictionary of adjectives that psychological literature defines as markers of\nthe five major personality traits, or Big Five. In this work, we started with\nthe implementation of fully-connected neural networks as a basis for\nunderstanding how simple deep learning models can provide information on hidden\npersonality characteristics. Finally, we use a class of distributional\nalgorithms invented in 2013 by Tomas Mikolov, which consists of using a\nconvolutional neural network that learns the contexts of words in an\nunsupervised way. In this way, we construct an embedding that contains the\nsemantic information on the text, obtaining a kind of \"geometry of meaning\" in\nwhich concepts are translated into linear relationships. With this last\nexperiment, we hypothesize that an individual writing style is largely coupled\nwith their personality traits.\n", "date": "2023-02-23", "categories": []}
{"id": "2302.10964", "title": "Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation\n  into the 2021 U.S. Capitol Attack", "abstract": "  Social media platforms have enabled extremists to organize violent events,\nsuch as the 2021 U.S. Capitol Attack. Simultaneously, these platforms enable\nprofessional investigators and amateur sleuths to collaboratively collect and\nidentify imagery of suspects with the goal of holding them accountable for\ntheir actions. Through a case study of Sedition Hunters, a Twitter community\nwhose goal is to identify individuals who participated in the 2021 U.S. Capitol\nAttack, we explore what are the main topics or targets of the community, who\nparticipates in the community, and how. Using topic modeling, we find that\ninformation sharing is the main focus of the community. We also note an\nincrease in awareness of privacy concerns. Furthermore, using social network\nanalysis, we show how some participants played important roles in the\ncommunity. Finally, we discuss implications for the content and structure of\nonline crowdsourced investigations.\n", "date": "2023-02-21", "categories": []}
{"id": "2302.09885", "title": "Mental Health Coping Stories on Social Media: A Causal-Inference Study\n  of Papageno Effect", "abstract": "  The Papageno effect concerns how media can play a positive role in preventing\nand mitigating suicidal ideation and behaviors. With the increasing ubiquity\nand widespread use of social media, individuals often express and share lived\nexperiences and struggles with mental health. However, there is a gap in our\nunderstanding about the existence and effectiveness of the Papageno effect in\nsocial media, which we study in this paper. In particular, we adopt a\ncausal-inference framework to examine the impact of exposure to mental health\ncoping stories on individuals on Twitter. We obtain a Twitter dataset with\n$\\sim$2M posts by $\\sim$10K individuals. We consider engaging with coping\nstories as the Treatment intervention, and adopt a stratified propensity score\napproach to find matched cohorts of Treatment and Control individuals. We\nmeasure the psychosocial shifts in affective, behavioral, and cognitive\noutcomes in longitudinal Twitter data before and after engaging with the coping\nstories. Our findings reveal that, engaging with coping stories leads to\ndecreased stress and depression, and improved expressive writing, diversity,\nand interactivity. Our work discusses the practical and platform design\nimplications in supporting mental wellbeing.\n", "date": "2023-02-20", "categories": []}
{"id": "2302.08709", "title": "Multimodal Propaganda Processing", "abstract": "  Propaganda campaigns have long been used to influence public opinion via\ndisseminating biased and/or misleading information. Despite the increasing\nprevalence of propaganda content on the Internet, few attempts have been made\nby AI researchers to analyze such content. We introduce the task of multimodal\npropaganda processing, where the goal is to automatically analyze propaganda\ncontent. We believe that this task presents a long-term challenge to AI\nresearchers and that successful processing of propaganda could bring machine\nunderstanding one important step closer to human understanding. We discuss the\ntechnical challenges associated with this task and outline the steps that need\nto be taken to address it.\n", "date": "2023-02-17", "categories": []}
{"id": "2302.05657", "title": "Dialectograms: Machine Learning Differences between Discursive\n  Communities", "abstract": "  Word embeddings provide an unsupervised way to understand differences in word\nusage between discursive communities. A number of recent papers have focused on\nidentifying words that are used differently by two or more communities. But\nword embeddings are complex, high-dimensional spaces and a focus on identifying\ndifferences only captures a fraction of their richness. Here, we take a step\ntowards leveraging the richness of the full embedding space, by using word\nembeddings to map out how words are used differently. Specifically, we describe\nthe construction of dialectograms, an unsupervised way to visually explore the\ncharacteristic ways in which each community use a focal word. Based on these\ndialectograms, we provide a new measure of the degree to which words are used\ndifferently that overcomes the tendency for existing measures to pick out low\nfrequent or polysemous words. We apply our methods to explore the discourses of\ntwo US political subreddits and show how our methods identify stark affective\npolarisation of politicians and political entities, differences in the\nassessment of proper political action as well as disagreement about whether\ncertain issues require political intervention at all.\n", "date": "2023-02-11", "categories": []}
{"id": "2302.03183", "title": "Capturing Topic Framing via Masked Language Modeling", "abstract": "  Differential framing of issues can lead to divergent world views on important\nissues. This is especially true in domains where the information presented can\nreach a large audience, such as traditional and social media. Scalable and\nreliable measurement of such differential framing is an important first step in\naddressing them. In this work, based on the intuition that framing affects the\ntone and word choices in written language, we propose a framework for modeling\nthe differential framing of issues through masked token prediction via\nlarge-scale fine-tuned language models (LMs). Specifically, we explore three\nkey factors for our framework: 1) prompt generation methods for the masked\ntoken prediction; 2) methods for normalizing the output of fine-tuned LMs; 3)\nrobustness to the choice of pre-trained LMs used for fine-tuning. Through\nexperiments on a dataset of articles from traditional media outlets covering\nfive diverse and politically polarized topics, we show that our framework can\ncapture differential framing of these topics with high reliability.\n", "date": "2023-02-07", "categories": []}
{"id": "2302.03475", "title": "Entity-Aware Dual Co-Attention Network for Fake News Detection", "abstract": "  Fake news and misinformation spread rapidly on the Internet. How to identify\nit and how to interpret the identification results have become important\nissues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for\nfake news detection, which takes news content, social media replies, and\nexternal knowledge into consideration. Our experimental results support that\nthe proposed Dual-CAN outperforms current representative models in two\nbenchmark datasets. We further make in-depth discussions by comparing how\nmodels work in both datasets with empirical analysis of attention weights.\n", "date": "2023-02-07", "categories": []}
{"id": "2302.03490", "title": "Natural Language Processing for Policymaking", "abstract": "  Language is the medium for many political activities, from campaigns to news\nreports. Natural language processing (NLP) uses computational tools to parse\ntext into key information that is needed for policymaking. In this chapter, we\nintroduce common methods of NLP, including text classification, topic modeling,\nevent extraction, and text scaling. We then overview how these methods can be\nused for policymaking through four major applications including data collection\nfor evidence-based policymaking, interpretation of political decisions, policy\ncommunication, and investigation of policy effects. Finally, we highlight some\npotential limitations and ethical concerns when using NLP for policymaking.\n  This text is from Chapter 7 (pages 141-162) of the Handbook of Computational\nSocial Science for Policy (2023). Open Access on Springer:\nhttps://doi.org/10.1007/978-3-031-16624-2\n", "date": "2023-02-07", "categories": []}
{"id": "2302.02479", "title": "Hatemongers ride on echo chambers to escalate hate speech diffusion", "abstract": "  Recent years have witnessed a swelling rise of hateful and abusive content\nover online social networks. While detection and moderation of hate speech have\nbeen the early go-to countermeasures, the solution requires a deeper\nexploration of the dynamics of hate generation and propagation. We analyze more\nthan 32 million posts from over 6.8 million users across three popular online\nsocial networks to investigate the interrelations between hateful behavior,\ninformation dissemination, and polarised organization mediated by echo\nchambers. We find that hatemongers play a more crucial role in governing the\nspread of information compared to singled-out hateful content. This observation\nholds for both the growth of information cascades as well as the conglomeration\nof hateful actors. Dissection of the core-wise distribution of these networks\npoints towards the fact that hateful users acquire a more well-connected\nposition in the social network and often flock together to build up information\ncascades. We observe that this cohesion is far from mere organized behavior;\ninstead, in these networks, hatemongers dominate the echo chambers -- groups of\nusers actively align themselves to specific ideological positions. The observed\ndominance of hateful users to inflate information cascades is primarily via\nuser interactions amplified within these echo chambers. We conclude our study\nwith a cautionary note that popularity-based recommendation of content is\nsusceptible to be exploited by hatemongers given their potential to escalate\ncontent popularity via echo-chambered interactions.\n", "date": "2023-02-05", "categories": []}
{"id": "2302.00239", "title": "Filtering Context Mitigates Scarcity and Selection Bias in Political\n  Ideology Prediction", "abstract": "  We propose a novel supervised learning approach for political ideology\nprediction (PIP) that is capable of predicting out-of-distribution inputs. This\nproblem is motivated by the fact that manual data-labeling is expensive, while\nself-reported labels are often scarce and exhibit significant selection bias.\nWe propose a novel statistical model that decomposes the document embeddings\ninto a linear superposition of two vectors; a latent neutral \\emph{context}\nvector independent of ideology, and a latent \\emph{position} vector aligned\nwith ideology. We train an end-to-end model that has intermediate contextual\nand positional vectors as outputs. At deployment time, our model predicts\nlabels for input documents by exclusively leveraging the predicted positional\nvectors. On two benchmark datasets we show that our model is capable of\noutputting predictions even when trained with as little as 5\\% biased data, and\nis significantly more accurate than the state-of-the-art. Through\ncrowd-sourcing we validate the neutrality of contextual vectors, and show that\ncontext filtering results in ideological concentration, allowing for prediction\non out-of-distribution examples.\n", "date": "2023-02-01", "categories": []}
{"id": "2302.00493", "title": "You Are What You Talk About: Inducing Evaluative Topics for Personality\n  Analysis", "abstract": "  Expressing attitude or stance toward entities and concepts is an integral\npart of human behavior and personality. Recently, evaluative language data has\nbecome more accessible with social media's rapid growth, enabling large-scale\nopinion analysis. However, surprisingly little research examines the\nrelationship between personality and evaluative language. To bridge this gap,\nwe introduce the notion of evaluative topics, obtained by applying topic models\nto pre-filtered evaluative text from social media. We then link evaluative\ntopics to individual text authors to build their evaluative profiles. We apply\nevaluative profiling to Reddit comments labeled with personality scores and\nconduct an exploratory study on the relationship between evaluative topics and\nBig Five personality facets, aiming for a more interpretable, facet-level\nanalysis. Finally, we validate our approach by observing correlations\nconsistent with prior research in personality psychology.\n", "date": "2023-02-01", "categories": []}
{"id": "2210.10085", "title": "Auditing YouTube's Recommendation Algorithm for Misinformation Filter\n  Bubbles", "abstract": "  In this paper, we present results of an auditing study performed over YouTube\naimed at investigating how fast a user can get into a misinformation filter\nbubble, but also what it takes to \"burst the bubble\", i.e., revert the bubble\nenclosure. We employ a sock puppet audit methodology, in which pre-programmed\nagents (acting as YouTube users) delve into misinformation filter bubbles by\nwatching misinformation promoting content. Then they try to burst the bubbles\nand reach more balanced recommendations by watching misinformation debunking\ncontent. We record search results, home page results, and recommendations for\nthe watched videos. Overall, we recorded 17,405 unique videos, out of which we\nmanually annotated 2,914 for the presence of misinformation. The labeled data\nwas used to train a machine learning model classifying videos into three\nclasses (promoting, debunking, neutral) with the accuracy of 0.82. We use the\ntrained model to classify the remaining videos that would not be feasible to\nannotate manually.\n  Using both the manually and automatically annotated data, we observe the\nmisinformation bubble dynamics for a range of audited topics. Our key finding\nis that even though filter bubbles do not appear in some situations, when they\ndo, it is possible to burst them by watching misinformation debunking content\n(albeit it manifests differently from topic to topic). We also observe a sudden\ndecrease of misinformation filter bubble effect when misinformation debunking\nvideos are watched after misinformation promoting videos, suggesting a strong\ncontextuality of recommendations. Finally, when comparing our results with a\nprevious similar study, we do not observe significant improvements in the\noverall quantity of recommended misinformation content.\n", "date": "2022-10-18", "categories": []}
{"id": "2301.10604", "title": "Automated multilingual detection of Pro-Kremlin propaganda in newspapers\n  and Telegram posts", "abstract": "  The full-scale conflict between the Russian Federation and Ukraine generated\nan unprecedented amount of news articles and social media data reflecting\nopposing ideologies and narratives. These polarized campaigns have led to\nmutual accusations of misinformation and fake news, shaping an atmosphere of\nconfusion and mistrust for readers worldwide. This study analyses how the media\naffected and mirrored public opinion during the first month of the war using\nnews articles and Telegram news channels in Ukrainian, Russian, Romanian and\nEnglish. We propose and compare two methods of multilingual automated\npro-Kremlin propaganda identification, based on Transformers and linguistic\nfeatures. We analyse the advantages and disadvantages of both methods, their\nadaptability to new genres and languages, and ethical considerations of their\nusage for content moderation. With this work, we aim to lay the foundation for\nfurther development of moderation tools tailored to the current conflict.\n", "date": "2023-01-25", "categories": []}
{"id": "2301.07981", "title": "Continuously Reliable Detection of New-Normal Misinformation: Semantic\n  Masking and Contrastive Smoothing in High-Density Latent Regions", "abstract": "  Toxic misinformation campaigns have caused significant societal harm, e.g.,\naffecting elections and COVID-19 information awareness. Unfortunately, despite\nsuccesses of (gold standard) retrospective studies of misinformation that\nconfirmed their harmful effects after the fact, they arrive too late for timely\nintervention and reduction of such harm. By design, misinformation evades\nretrospective classifiers by exploiting two properties we call new-normal: (1)\nnever-seen-before novelty that cause inescapable generalization challenges for\nprevious classifiers, and (2) massive but short campaigns that end before they\ncan be manually annotated for new classifier training. To tackle these\nchallenges, we propose UFIT, which combines two techniques: semantic masking of\nstrong signal keywords to reduce overfitting, and intra-proxy smoothness\nregularization of high-density regions in the latent space to improve\nreliability and maintain accuracy. Evaluation of UFIT on public new-normal\nmisinformation data shows over 30% improvement over existing approaches on\nfuture (and unseen) campaigns. To the best of our knowledge, UFIT is the first\nsuccessful effort to achieve such high level of generalization on new-normal\nmisinformation data with minimal concession (1 to 5%) of accuracy compared to\noracles trained with full knowledge of all campaigns.\n", "date": "2023-01-19", "categories": []}
{"id": "2301.05863", "title": "Detecting Stance of Authorities towards Rumors in Arabic Tweets: A\n  Preliminary Study", "abstract": "  A myriad of studies addressed the problem of rumor verification in Twitter by\neither utilizing evidence from the propagation networks or external evidence\nfrom the Web. However, none of these studies exploited evidence from trusted\nauthorities. In this paper, we define the task of detecting the stance of\nauthorities towards rumors in tweets, i.e., whether a tweet from an authority\nagrees, disagrees, or is unrelated to the rumor. We believe the task is useful\nto augment the sources of evidence utilized by existing rumor verification\nsystems. We construct and release the first Authority STance towards Rumors\n(AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic\nTwitter. Due to the relatively limited size of our dataset, we study the\nusefulness of existing datasets for stance detection in our task. We show that\nexisting datasets are somewhat useful for the task; however, they are clearly\ninsufficient, which motivates the need to augment them with annotated data\nconstituting stance of authorities from Twitter.\n", "date": "2023-01-14", "categories": []}
{"id": "2301.05327", "title": "Blind Judgement: Agent-Based Supreme Court Modelling With GPT", "abstract": "  We present a novel Transformer-based multi-agent system for simulating the\njudicial rulings of the 2010-2016 Supreme Court of the United States. We train\nnine separate models with the respective authored opinions of each supreme\njustice active ca. 2015 and test the resulting system on 96 real-world cases.\nWe find our system predicts the decisions of the real-world Supreme Court with\nbetter-than-random accuracy. We further find a correlation between model\naccuracy with respect to individual justices and their alignment between legal\nconservatism & liberalism. Our methods and results hold significance for\nresearchers interested in using language models to simulate politically-charged\ndiscourse between multiple agents.\n", "date": "2023-01-12", "categories": []}
{"id": "2212.14486", "title": "Examining Political Rhetoric with Epistemic Stance Detection", "abstract": "  Participants in political discourse employ rhetorical strategies -- such as\nhedging, attributions, or denials -- to display varying degrees of belief\ncommitments to claims proposed by themselves or others. Traditionally,\npolitical scientists have studied these epistemic phenomena through\nlabor-intensive manual content analysis. We propose to help automate such work\nthrough epistemic stance prediction, drawn from research in computational\nsemantics, to distinguish at the clausal level what is asserted, denied, or\nonly ambivalently suggested by the author or other mentioned entities (belief\nholders). We first develop a simple RoBERTa-based model for multi-source stance\npredictions that outperforms more complex state-of-the-art modeling. Then we\ndemonstrate its novel application to political science by conducting a\nlarge-scale analysis of the Mass Market Manifestos corpus of U.S. political\nopinion books, where we characterize trends in cited belief holders --\nrespected allies and opposed bogeymen -- across U.S. political ideologies.\n", "date": "2022-12-29", "categories": []}
{"id": "2212.10778", "title": "Mining User-aware Multi-relations for Fake News Detection in Large Scale\n  Online Social Networks", "abstract": "  Users' involvement in creating and propagating news is a vital aspect of fake\nnews detection in online social networks. Intuitively, credible users are more\nlikely to share trustworthy news, while untrusted users have a higher\nprobability of spreading untrustworthy news. In this paper, we construct a\ndual-layer graph (i.e., the news layer and the user layer) to extract multiple\nrelations of news and users in social networks to derive rich information for\ndetecting fake news. Based on the dual-layer graph, we propose a fake news\ndetection model named Us-DeFake. It learns the propagation features of news in\nthe news layer and the interaction features of users in the user layer. Through\nthe inter-layer in the graph, Us-DeFake fuses the user signals that contain\ncredibility information into the news features, to provide distinctive\nuser-aware embeddings of news for fake news detection. The training process\nconducts on multiple dual-layer subgraphs obtained by a graph sampler to scale\nUs-DeFake in large scale social networks. Extensive experiments on real-world\ndatasets illustrate the superiority of Us-DeFake which outperforms all\nbaselines, and the users' credibility signals learned by interaction relation\ncan notably improve the performance of our model.\n", "date": "2022-12-21", "categories": []}
{"id": "2301.00604", "title": "Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of\n  Various Countries", "abstract": "  With Twitter's growth and popularity, a huge number of views are shared by\nusers on various topics, making this platform a valuable information source on\nvarious political, social, and economic issues. This paper investigates English\ntweets on the Russia-Ukraine war to analyze trends reflecting users' opinions\nand sentiments regarding the conflict. The tweets' positive and negative\nsentiments are analyzed using a BERT-based model, and the time series\nassociated with the frequency of positive and negative tweets for various\ncountries is calculated. Then, we propose a method based on the neighborhood\naverage for modeling and clustering the time series of countries. The\nclustering results provide valuable insight into public opinion regarding this\nconflict. Among other things, we can mention the similar thoughts of users from\nthe United States, Canada, the United Kingdom, and most Western European\ncountries versus the shared views of Eastern European, Scandinavian, Asian, and\nSouth American nations toward the conflict.\n", "date": "2023-01-02", "categories": []}
{"id": "2212.14648", "title": "Distant Reading of the German Coalition Deal: Recognizing Policy\n  Positions with BERT-based Text Classification", "abstract": "  Automated text analysis has become a widely used tool in political science.\nIn this research, we use a BERT model trained on German party manifestos to\nidentify the individual parties' contribution to the coalition agreement of\n2021.\n", "date": "2022-12-30", "categories": []}
{"id": "2212.14672", "title": "Twitter's Agenda-Setting Role: A Study of Twitter Strategy for Political\n  Diversion", "abstract": "  This study verified the effectiveness of Donald Trump's Twitter campaign in\nguiding agen-da-setting and deflecting political risk and examined Trump's\nTwitter communication strategy and explores the communication effects of his\ntweet content during Covid-19 pandemic. We collected all tweets posted by Trump\non the Twitter platform from January 1, 2020 to December 31, 2020.We used\nOrdinary Least Squares (OLS) regression analysis with a fixed effects model to\nanalyze the existence of the Twitter strategy. The correlation between the\nnumber of con-firmed daily Covid-19 diagnoses and the number of particular\nthematic tweets was investigated using time series analysis. Empirical analysis\nrevealed Twitter's strategy is used to divert public attention from negative\nCovid-19 reports during the epidemic, and it posts a powerful political\ncommunication effect on Twitter. However, findings suggest that Trump did not\nuse false claims to divert political risk and shape public opinion.\n", "date": "2022-12-16", "categories": []}
{"id": "2212.12955", "title": "Search, Structure, and Sentiment: A Comparative Analysis of Network\n  Opinion in Different Query Types on Twitter", "abstract": "  Understanding the relationship between structure and sentiment is essential\nin highlighting future operations with online social networks. More\nspecifically, within popular conversation on Twitter. This paper provides a\ndevelopment on the relationship between the two variables: structure, defined\nas the composition of a directed network, and sentiment, a quantified value of\nthe positive/negative connotations of a conversation. We highlight thread\nsentiment to be inversely proportional to the strength and connectivity of a\nnetwork. The second portion of this paper highlights differences in query\ntypes, specifically how the aforementioned behavior differs within four key\nquery types. This paper focuses on topical, event-based, geographic, and\nindividual queries as orientations which have differing behavior. Using\ncross-query analysis, we see that the relationship between structure and\nsentiment, though still inversely proportional, differs greatly across query\ntypes. We find this relationship to be the most clear within the individual\nqueries and the least prevalent within the event-based queries. This paper\nprovides a sociological progression in our understanding of opinion and\nnetworks, while providing a methodological advancement for future studies on\nsimilar subjects.\n", "date": "2022-12-25", "categories": []}
{"id": "2212.08864", "title": "'If you build they will come': Automatic Identification of\n  News-Stakeholders to detect Party Preference in News Coverage", "abstract": "  The coverage of different stakeholders mentioned in the news articles\nsignificantly impacts the slant or polarity detection of the concerned news\npublishers. For instance, the pro-government media outlets would give more\ncoverage to the government stakeholders to increase their accessibility to the\nnews audiences. In contrast, the anti-government news agencies would focus more\non the views of the opponent stakeholders to inform the readers about the\nshortcomings of government policies. In this paper, we address the problem of\nstakeholder extraction from news articles and thereby determine the inherent\nbias present in news reporting. Identifying potential stakeholders in\nmulti-topic news scenarios is challenging because each news topic has different\nstakeholders. The research presented in this paper utilizes both contextual\ninformation and external knowledge to identify the topic-specific stakeholders\nfrom news articles. We also apply a sequential incremental clustering algorithm\nto group the entities with similar stakeholder types. We carried out all our\nexperiments on news articles on four Indian government policies published by\nnumerous national and international news agencies. We also further generalize\nour system, and the experimental results show that the proposed model can be\nextended to other news topics.\n", "date": "2022-12-17", "categories": []}
{"id": "2212.08353", "title": "How to disagree well: Investigating the dispute tactics used on\n  Wikipedia", "abstract": "  Disagreements are frequently studied from the perspective of either detecting\ntoxicity or analysing argument structure. We propose a framework of dispute\ntactics that unifies these two perspectives, as well as other dialogue acts\nwhich play a role in resolving disputes, such as asking questions and providing\nclarification. This framework includes a preferential ordering among\nrebuttal-type tactics, ranging from ad hominem attacks to refuting the central\nargument. Using this framework, we annotate 213 disagreements (3,865\nutterances) from Wikipedia Talk pages. This allows us to investigate research\nquestions around the tactics used in disagreements; for instance, we provide\nempirical validation of the approach to disagreement recommended by Wikipedia.\nWe develop models for multilabel prediction of dispute tactics in an utterance,\nachieving the best performance with a transformer-based label powerset model.\nAdding an auxiliary task to incorporate the ordering of rebuttal tactics\nfurther yields a statistically significant increase. Finally, we show that\nthese annotations can be used to provide useful additional signals to improve\nperformance on the task of predicting escalation.\n", "date": "2022-12-16", "categories": []}
{"id": "2212.08550", "title": "Fine-grained Czech News Article Dataset: An Interdisciplinary Approach\n  to Trustworthiness Analysis", "abstract": "  We present the Verifee Dataset: a novel dataset of news articles with\nfine-grained trustworthiness annotations. We develop a detailed methodology\nthat assesses the texts based on their parameters encompassing editorial\ntransparency, journalist conventions, and objective reporting while penalizing\nmanipulative techniques. We bring aboard a diverse set of researchers from\nsocial, media, and computer sciences to overcome barriers and limited framing\nof this interdisciplinary problem. We collect over $10,000$ unique articles\nfrom almost $60$ Czech online news sources. These are categorized into one of\nthe $4$ classes across the credibility spectrum we propose, raging from\nentirely trustworthy articles all the way to the manipulative ones. We produce\ndetailed statistics and study trends emerging throughout the set. Lastly, we\nfine-tune multiple popular sequence-to-sequence language models using our\ndataset on the trustworthiness classification task and report the best testing\nF-1 score of $0.52$. We open-source the dataset, annotation methodology, and\nannotators' instructions in full length at https://verifee.ai/research to\nenable easy build-up work. We believe similar methods can help prevent\ndisinformation and educate in the realm of media literacy.\n", "date": "2022-12-16", "categories": []}
{"id": "2212.07526", "title": "Relationship Between Online Harmful Behaviors and Social Network Message\n  Writing Style", "abstract": "  In this paper, we explore the relationship between an individual's writing\nstyle and the risk that they will engage in online harmful behaviors (such as\ncyberbullying). In particular, we consider whether measurable differences in\nwriting style relate to different personality types, as modeled by the Big-Five\npersonality traits and the Dark Triad traits, and can differentiate between\nusers who do or do not engage in harmful behaviors. We study messages from\nnearly 2,500 users from two online communities (Twitter and Reddit) and find\nthat we can measure significant personality differences between regular and\nharmful users from the writing style of as few as 100 tweets or 40 Reddit\nposts, aggregate these values to distinguish between healthy and harmful\ncommunities, and also use style attributes to predict which users will engage\nin harmful behaviors.\n", "date": "2022-12-14", "categories": []}
{"id": "2212.06048", "title": "Machine Learning Approaches for Principle Prediction in Naturally\n  Occurring Stories", "abstract": "  Value alignment is the task of creating autonomous systems whose values align\nwith those of humans. Past work has shown that stories are a potentially rich\nsource of information on human values; however, past work has been limited to\nconsidering values in a binary sense. In this work, we explore the use of\nmachine learning models for the task of normative principle prediction on\nnaturally occurring story data. To do this, we extend a dataset that has been\npreviously used to train a binary normative classifier with annotations of\nmoral principles. We then use this dataset to train a variety of machine\nlearning models, evaluate these models and compare their results against humans\nwho were asked to perform the same task. We show that while individual\nprinciples can be classified, the ambiguity of what \"moral principles\"\nrepresent, poses a challenge for both human participants and autonomous systems\nwhich are faced with the same task.\n", "date": "2022-11-19", "categories": []}
{"id": "2212.04765", "title": "Understanding Online Migration Decisions Following the Banning of\n  Radical Communities", "abstract": "  The proliferation of radical online communities and their violent offshoots\nhas sparked great societal concern. However, the current practice of banning\nsuch communities from mainstream platforms has unintended consequences: (I) the\nfurther radicalization of their members in fringe platforms where they migrate;\nand (ii) the spillover of harmful content from fringe back onto mainstream\nplatforms. Here, in a large observational study on two banned subreddits,\nr/The\\_Donald and r/fatpeoplehate, we examine how factors associated with the\nRECRO radicalization framework relate to users' migration decisions.\nSpecifically, we quantify how these factors affect users' decisions to post on\nfringe platforms and, for those who do, whether they continue posting on the\nmainstream platform. Our results show that individual-level factors, those\nrelating to the behavior of users, are associated with the decision to post on\nthe fringe platform. Whereas social-level factors, users' connection with the\nradical community, only affect the propensity to be coactive on both platforms.\nOverall, our findings pave the way for evidence-based moderation policies, as\nthe decisions to migrate and remain coactive amplify unintended consequences of\ncommunity bans.\n", "date": "2022-12-09", "categories": []}
{"id": "2212.04185", "title": "Beyond Discrete Genres: Mapping News Items onto a Multidimensional\n  Framework of Genre Cues", "abstract": "  In the contemporary media landscape, with the vast and diverse supply of\nnews, it is increasingly challenging to study such an enormous amount of items\nwithout a standardized framework. Although attempts have been made to organize\nand compare news items on the basis of news values, news genres receive little\nattention, especially the genres in a news consumer's perception. Yet,\nperceived news genres serve as an essential component in exploring how news has\ndeveloped, as well as a precondition for understanding media effects. We\napproach this concept by conceptualizing and operationalizing a non-discrete\nframework for mapping news items in terms of genre cues. As a starting point,\nwe propose a preliminary set of dimensions consisting of \"factuality\" and\n\"formality\". To automatically analyze a large amount of news items, we deliver\ntwo computational models for predicting news sentences in terms of the said two\ndimensions. Such predictions could then be used for locating news items within\nour framework. This proposed approach that positions news items upon a\nmultidimensional grid helps in deepening our insight into the evolving nature\nof news genres.\n", "date": "2022-12-08", "categories": []}
{"id": "2212.04272", "title": "A Modality-level Explainable Framework for Misinformation Checking in\n  Social Networks", "abstract": "  The widespread of false information is a rising concern worldwide with\ncritical social impact, inspiring the emergence of fact-checking organizations\nto mitigate misinformation dissemination. However, human-driven verification\nleads to a time-consuming task and a bottleneck to have checked trustworthy\ninformation at the same pace they emerge. Since misinformation relates not only\nto the content itself but also to other social features, this paper addresses\nautomatic misinformation checking in social networks from a multimodal\nperspective. Moreover, as simply naming a piece of news as incorrect may not\nconvince the citizen and, even worse, strengthen confirmation bias, the\nproposal is a modality-level explainable-prone misinformation classifier\nframework. Our framework comprises a misinformation classifier assisted by\nexplainable methods to generate modality-oriented explainable inferences.\nPreliminary findings show that the misinformation classifier does benefit from\nmultimodal information encoding and the modality-oriented explainable mechanism\nincreases both inferences' interpretability and completeness.\n", "date": "2022-12-08", "categories": []}
{"id": "2210.10839", "title": "How Hate Speech Varies by Target Identity: A Computational Analysis", "abstract": "  This paper investigates how hate speech varies in systematic ways according\nto the identities it targets. Across multiple hate speech datasets annotated\nfor targeted identities, we find that classifiers trained on hate speech\ntargeting specific identity groups struggle to generalize to other targeted\nidentities. This provides empirical evidence for differences in hate speech by\ntarget identity; we then investigate which patterns structure this variation.\nWe find that the targeted demographic category (e.g. gender/sexuality or\nrace/ethnicity) appears to have a greater effect on the language of hate speech\nthan does the relative social power of the targeted identity group. We also\nfind that words associated with hate speech targeting specific identities often\nrelate to stereotypes, histories of oppression, current social movements, and\nother social contexts specific to identities. These experiments suggest the\nimportance of considering targeted identity, as well as the social contexts\nassociated with these identities, in automated hate speech classification.\n", "date": "2022-10-19", "categories": []}
{"id": "2212.01401", "title": "Thread With Caution: Proactively Helping Users Assess and Deescalate\n  Tension in Their Online Discussions", "abstract": "  Incivility remains a major challenge for online discussion platforms, to such\nan extent that even conversations between well-intentioned users can often\nderail into uncivil behavior. Traditionally, platforms have relied on\nmoderators to -- with or without algorithmic assistance -- take corrective\nactions such as removing comments or banning users. In this work we propose a\ncomplementary paradigm that directly empowers users by proactively enhancing\ntheir awareness about existing tension in the conversation they are engaging in\nand actively guides them as they are drafting their replies to avoid further\nescalation.\n  As a proof of concept for this paradigm, we design an algorithmic tool that\nprovides such proactive information directly to users, and conduct a user study\nin a popular discussion platform. Through a mixed methods approach combining\nsurveys with a randomized controlled experiment, we uncover qualitative and\nquantitative insights regarding how the participants utilize and react to this\ninformation. Most participants report finding this proactive paradigm valuable,\nnoting that it helps them to identify tension that they may have otherwise\nmissed and prompts them to further reflect on their own replies and to revise\nthem. These effects are corroborated by a comparison of how the participants\ndraft their reply when our tool warns them that their conversation is at risk\nof derailing into uncivil behavior versus in a control condition where the tool\nis disabled. These preliminary findings highlight the potential of this\nuser-centered paradigm and point to concrete directions for future\nimplementations.\n", "date": "2022-12-02", "categories": []}
{"id": "2212.02352", "title": "Fake News and Hate Speech: Language in Common", "abstract": "  In this paper we raise the research question of whether fake news and hate\nspeech spreaders share common patterns in language. We compute a novel index,\nthe ingroup vs outgroup index, in three different datasets and we show that\nboth phenomena share an \"us vs them\" narrative.\n", "date": "2022-12-05", "categories": []}
{"id": "2212.00237", "title": "Inference of Media Bias and Content Quality Using Natural-Language\n  Processing", "abstract": "  Media bias can significantly impact the formation and development of opinions\nand sentiments in a population. It is thus important to study the emergence and\ndevelopment of partisan media and political polarization. However, it is\nchallenging to quantitatively infer the ideological positions of media outlets.\nIn this paper, we present a quantitative framework to infer both political bias\nand content quality of media outlets from text, and we illustrate this\nframework with empirical experiments with real-world data. We apply a\nbidirectional long short-term memory (LSTM) neural network to a data set of\nmore than 1 million tweets to generate a two-dimensional ideological-bias and\ncontent-quality measurement for each tweet. We then infer a ``media-bias\nchart'' of (bias, quality) coordinates for the media outlets by integrating the\n(bias, quality) measurements of the tweets of the media outlets. We also apply\na variety of baseline machine-learning methods, such as a naive-Bayes method\nand a support-vector machine (SVM), to infer the bias and quality values for\neach tweet. All of these baseline approaches are based on a bag-of-words\napproach. We find that the LSTM-network approach has the best performance of\nthe examined methods. Our results illustrate the importance of leveraging word\norder into machine-learning methods in text analysis.\n", "date": "2022-12-01", "categories": []}
{"id": "2211.14358", "title": "A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at\n  A Large Scale", "abstract": "  Fairy tales are a common resource for young children to learn a language or\nunderstand how a society works. However, gender bias, e.g., stereotypical\ngender roles, in this literature may cause harm and skew children's world view.\nInstead of decades of qualitative and manual analysis of gender bias in fairy\ntales, we computationally analyze gender bias in a fairy tale dataset\ncontaining 624 fairy tales from 7 different cultures. We specifically examine\ngender difference in terms of moral foundations, which are measures of human\nmorality, and events, which reveal human activities associated with each\ncharacter. We find that the number of male characters is two times that of\nfemale characters, showing a disproportionate gender representation. Our\nanalysis further reveal stereotypical portrayals of both male and female\ncharacters in terms of moral foundations and events. Female characters turn out\nmore associated with care-, loyalty- and sanctity- related moral words, while\nmale characters are more associated with fairness- and authority- related moral\nwords. Female characters' events are often about emotion (e.g., weep),\nappearance (e.g., comb), household (e.g., bake), etc.; while male characters'\nevents are more about profession (e.g., hunt), violence (e.g., destroy),\njustice (e.g., judge), etc. Gender bias in terms of moral foundations shows an\nobvious difference across cultures. For example, female characters are more\nassociated with care and sanctity in high uncertainty-avoidance cultures which\nare less open to changes and unpredictability. Based on the results, we propose\nimplications for children's literature and early literacy research.\n", "date": "2022-11-25", "categories": []}
{"id": "2211.15407", "title": "Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media\n  Data: Comparative Study", "abstract": "  This study investigated and compared public sentiment related to COVID-19\nvaccines expressed on two popular social media platforms, Reddit and Twitter,\nharvested from January 1, 2020, to March 1, 2022. To accomplish this task, we\ncreated a fine-tuned DistilRoBERTa model to predict sentiments of approximately\n9.5 million Tweets and 70 thousand Reddit comments. To fine-tune our model, our\nteam manually labeled the sentiment of 3600 Tweets and then augmented our\ndataset by the method of back-translation. Text sentiment for each social media\nplatform was then classified with our fine-tuned model using Python and the\nHuggingface sentiment analysis pipeline. Our results determined that the\naverage sentiment expressed on Twitter was more negative (52% positive) than\npositive and the sentiment expressed on Reddit was more positive than negative\n(53% positive). Though average sentiment was found to vary between these social\nmedia platforms, both displayed similar behavior related to sentiment shared at\nkey vaccine-related developments during the pandemic. Considering this similar\ntrend in shared sentiment demonstrated across social media platforms, Twitter\nand Reddit continue to be valuable data sources that public health officials\ncan utilize to strengthen vaccine confidence and combat misinformation. As the\nspread of misinformation poses a range of psychological and psychosocial risks\n(anxiety, fear, etc.), there is an urgency in understanding the public\nperspective and attitude toward shared falsities. Comprehensive educational\ndelivery systems tailored to the population's expressed sentiments that\nfacilitate digital literacy, health information-seeking behavior, and precision\nhealth promotion could aid in clarifying such misinformation.\n", "date": "2022-10-17", "categories": []}
{"id": "2211.14279", "title": "Multiverse: Multilingual Evidence for Fake News Detection", "abstract": "  Misleading information spreads on the Internet at an incredible speed, which\ncan lead to irreparable consequences in some cases. It is becoming essential to\ndevelop fake news detection technologies. While substantial work has been done\nin this direction, one of the limitations of the current approaches is that\nthese models are focused only on one language and do not use multilingual\ninformation. In this work, we propose Multiverse -- a new feature based on\nmultilingual evidence that can be used for fake news detection and improve\nexisting approaches. The hypothesis of the usage of cross-lingual evidence as a\nfeature for fake news detection is confirmed, firstly, by manual experiment\nbased on a set of known true and fake news. After that, we compared our fake\nnews classification system based on the proposed feature with several baselines\non two multi-domain datasets of general-topic news and one fake COVID-19 news\ndataset showing that in additional combination with linguistic features it\nyields significant improvements.\n", "date": "2022-11-25", "categories": []}
{"id": "2211.10707", "title": "Suffering from Vaccines or from Government? : Partisan Bias in COVID-19\n  Vaccine Adverse Events Coverage", "abstract": "  Vaccine adverse events have been presumed to be a relatively objective\nmeasure that is immune to political polarization. The real-world data, however,\nshows the correlation between presidential disapproval ratings and the\nsubjective severity of adverse events. This paper investigates the partisan\nbias in COVID vaccine adverse events coverage with language models that can\nclassify the topic of vaccine-related articles and the political disposition of\nnews comments. Based on 90K news articles from 52 major newspaper companies, we\nfound that conservative media are inclined to report adverse events more\nfrequently than their liberal counterparts, while the coverage itself was\nstatistically uncorrelated with the severity of real-world adverse events. The\nusers who support the conservative opposing party were more likely to write the\npopular comments from 2.3K random sampled articles on news platforms. This\nresearch implies that bipartisanship can still play a significant role in\nforming public opinion on the COVID vaccine even after the majority of the\npopulation's vaccination\n", "date": "2022-11-19", "categories": []}
{"id": "2211.11521", "title": "Un discours et un public \"Gilets Jaunes\" au coeur du Grand D\\'ebat\n  National? Combinaison des approches IA et textom\\'etriques pour l'analyse de\n  discours des plateformes \"Grand D\\'ebat National\" et \"Vrai d\\'ebat\"", "abstract": "  In this contribution, we propose to analyze the statements coming from two\n''civic tech'' platforms-the governmental platform, ''Grand D{\\'e}bat\nNational'' and, its political and algorithmic response proposed by a Yellow\nVest collective, ''Vrai D{\\'e}bat''-, by confronting two families of algorithms\ndedicated to text analysis. We propose to implement, on the one hand, proven\napproaches in textual data analysis (Reinert/Iramuteq Method) which have\nrecently shown their interest in the analysis of very large corpora and, on the\nother hand, new methods resulting from the crossroads of the computer worlds,\nartificial intelligence and automatic language processing. We will examine the\nmethodological solutions for qualifying the social properties of speakers about\nwhom we have little direct information. Finally, we will attempt to present\nsome research questions at the crossroads of the political sociology of public\nopinion and data science, which such a confrontation opens up.\n", "date": "2022-11-14", "categories": []}
{"id": "2211.11724", "title": "Legal and Political Stance Detection of SCOTUS Language", "abstract": "  We analyze publicly available US Supreme Court documents using automated\nstance detection. In the first phase of our work, we investigate the extent to\nwhich the Court's public-facing language is political. We propose and calculate\ntwo distinct ideology metrics of SCOTUS justices using oral argument\ntranscripts. We then compare these language-based metrics to existing social\nscientific measures of the ideology of the Supreme Court and the public.\nThrough this cross-disciplinary analysis, we find that justices who are more\nresponsive to public opinion tend to express their ideology during oral\narguments. This observation provides a new kind of evidence in favor of the\nattitudinal change hypothesis of Supreme Court justice behavior. As a natural\nextension of this political stance detection, we propose the more specialized\ntask of legal stance detection with our new dataset SC-stance, which matches\nwritten opinions to legal questions. We find competitive performance on this\ndataset using language adapters trained on legal documents.\n", "date": "2022-11-21", "categories": []}
{"id": "2211.10057", "title": "Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic", "abstract": "  Propaganda is the expression of an opinion or an action by an individual or a\ngroup deliberately designed to influence the opinions or the actions of other\nindividuals or groups with reference to predetermined ends, which is achieved\nby means of well-defined rhetorical and psychological devices. Propaganda\ntechniques are commonly used in social media to manipulate or to mislead users.\nThus, there has been a lot of recent research on automatic detection of\npropaganda techniques in text as well as in memes. However, so far the focus\nhas been primarily on English. With the aim to bridge this language gap, we ran\na shared task on detecting propaganda techniques in Arabic tweets as part of\nthe WANLP 2022 workshop, which included two subtasks. Subtask~1 asks to\nidentify the set of propaganda techniques used in a tweet, which is a\nmultilabel classification problem, while Subtask~2 asks to detect the\npropaganda techniques used in a tweet together with the exact span(s) of text\nin which each propaganda technique appears. The task attracted 63 team\nregistrations, and eventually 14 and 3 teams made submissions for subtask 1 and\n2, respectively. Finally, 11 teams submitted system description papers.\n", "date": "2022-11-18", "categories": []}
{"id": "2211.05289", "title": "Combating Health Misinformation in Social Media: Characterization,\n  Detection, Intervention, and Open Issues", "abstract": "  Social media has been one of the main information consumption sources for the\npublic, allowing people to seek and spread information more quickly and easily.\nHowever, the rise of various social media platforms also enables the\nproliferation of online misinformation. In particular, misinformation in the\nhealth domain has significant impacts on our society such as the COVID-19\ninfodemic. Therefore, health misinformation in social media has become an\nemerging research direction that attracts increasing attention from researchers\nof different disciplines. Compared to misinformation in other domains, the key\ndifferences of health misinformation include the potential of causing actual\nharm to humans' bodies and even lives, the hardness to identify for normal\npeople, and the deep connection with medical science. In addition, health\nmisinformation on social media has distinct characteristics from conventional\nchannels such as television on multiple dimensions including the generation,\ndissemination, and consumption paradigms. Because of the uniqueness and\nimportance of combating health misinformation in social media, we conduct this\nsurvey to further facilitate interdisciplinary research on this problem. In\nthis survey, we present a comprehensive review of existing research about\nonline health misinformation in different disciplines. Furthermore, we also\nsystematically organize the related literature from three perspectives:\ncharacterization, detection, and intervention. Lastly, we conduct a deep\ndiscussion on the pressing open issues of combating health misinformation in\nsocial media and provide future directions for multidisciplinary researchers.\n", "date": "2022-11-10", "categories": []}
{"id": "2211.05533", "title": "GREENER: Graph Neural Networks for News Media Profiling", "abstract": "  We study the problem of profiling news media on the Web with respect to their\nfactuality of reporting and bias. This is an important but under-studied\nproblem related to disinformation and \"fake news\" detection, but it addresses\nthe issue at a coarser granularity compared to looking at an individual article\nor an individual claim. This is useful as it allows to profile entire media\noutlets in advance. Unlike previous work, which has focused primarily on text\n(e.g.,~on the text of the articles published by the target website, or on the\ntextual description in their social media profiles or in Wikipedia), here our\nmain focus is on modeling the similarity between media outlets based on the\noverlap of their audience. This is motivated by homophily considerations,\ni.e.,~the tendency of people to have connections to people with similar\ninterests, which we extend to media, hypothesizing that similar types of media\nwould be read by similar kinds of users. In particular, we propose GREENER\n(GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph of\ninter-media connections based on their audience overlap, and then uses graph\nneural networks to represent each medium. We find that such representations are\nquite useful for predicting the factuality and the bias of news media outlets,\nyielding improvements over state-of-the-art results reported on two datasets.\nWhen augmented with conventionally used representations obtained from news\narticles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy is\nfound to improve by 2.5-27 macro-F1 points for the two tasks.\n", "date": "2022-11-10", "categories": []}
{"id": "2210.15954", "title": "Stanceosaurus: Classifying Stance Towards Multilingual Misinformation", "abstract": "  We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi,\nand Arabic annotated with stance towards 251 misinformation claims. As far as\nwe are aware, it is the largest corpus annotated with stance towards\nmisinformation claims. The claims in Stanceosaurus originate from 15\nfact-checking sources that cover diverse geographical regions and cultures.\nUnlike existing stance datasets, we introduce a more fine-grained 5-class\nlabeling strategy with additional subcategories to distinguish implicit stance.\nPre-trained transformer-based stance classifiers that are fine-tuned on our\ncorpus show good generalization on unseen claims and regional claims from\ncountries outside the training data. Cross-lingual experiments demonstrate\nStanceosaurus' capability of training multi-lingual models, achieving 53.1 F1\non Hindi and 50.4 F1 on Arabic without any target-language fine-tuning.\nFinally, we show how a domain adaptation method can be used to improve\nperformance on Stanceosaurus using additional RumourEval-2019 data. We make\nStanceosaurus publicly available to the research community and hope it will\nencourage further work on misinformation identification across languages and\ncultures.\n", "date": "2022-10-28", "categories": []}
{"id": "2210.13534", "title": "Classification of Misinformation in New Articles using Natural Language\n  Processing and a Recurrent Neural Network", "abstract": "  This paper seeks to address the classification of misinformation in news\narticles using a Long Short Term Memory Recurrent Neural Network. Articles were\ntaken from 2018; a year that was filled with reporters writing about President\nDonald Trump, Special Counsel Robert Mueller, the Fifa World Cup, and Russia.\nThe model presented successfully classifies these articles with an accuracy\nscore of 0.779944. We consider this to be successful because the model was\ntrained on articles that included languages other than English as well as\nincomplete, or fragmented, articles.\n", "date": "2022-10-24", "categories": []}
{"id": "2210.13594", "title": "Datavoidant: An AI System for Addressing Political Data Voids on Social\n  Media", "abstract": "  The limited information (data voids) on political topics relevant to\nunderrepresented communities has facilitated the spread of disinformation.\nIndependent journalists who combat disinformation in underrepresented\ncommunities have reported feeling overwhelmed because they lack the tools\nnecessary to make sense of the information they monitor and address the data\nvoids. In this paper, we present a system to identify and address political\ndata voids within underrepresented communities. Armed with an interview study,\nindicating that the independent news media has the potential to address them,\nwe designed an intelligent collaborative system, called Datavoidant.\nDatavoidant uses state-of-the-art machine learning models and introduces a\nnovel design space to provide independent journalists with a collective\nunderstanding of data voids to facilitate generating content to cover the\nvoids. We performed a user interface evaluation with independent news media\njournalists (N=22). These journalists reported that Datavoidant's features\nallowed them to more rapidly while easily having a sense of what was taking\nplace in the information ecosystem to address the data voids. They also\nreported feeling more confident about the content they created and the unique\nperspectives they had proposed to cover the voids. We conclude by discussing\nhow Datavoidant enables a new design space wherein individuals can collaborate\nto make sense of their information ecosystem and actively devise strategies to\nprevent disinformation.\n", "date": "2022-10-24", "categories": []}
{"id": "2210.12170", "title": "Discovering Differences in the Representation of People using\n  Contextualized Semantic Axes", "abstract": "  A common paradigm for identifying semantic differences across social and\ntemporal contexts is the use of static word embeddings and their distances. In\nparticular, past work has compared embeddings against \"semantic axes\" that\nrepresent two opposing concepts. We extend this paradigm to BERT embeddings,\nand construct contextualized axes that mitigate the pitfall where antonyms have\nneighboring representations. We validate and demonstrate these axes on two\npeople-centric datasets: occupations from Wikipedia, and multi-platform\ndiscussions in extremist, men's communities over fourteen years. In both\nstudies, contextualized semantic axes can characterize differences among\ninstances of the same word type. In the latter study, we show that references\nto women and the contexts around them have become more detestable over time.\n", "date": "2022-10-21", "categories": []}
{"id": "2210.12883", "title": "A Greek Parliament Proceedings Dataset for Computational Linguistics and\n  Political Analysis", "abstract": "  Large, diachronic datasets of political discourse are hard to come across,\nespecially for resource-lean languages such as Greek. In this paper, we\nintroduce a curated dataset of the Greek Parliament Proceedings that extends\nchronologically from 1989 up to 2020. It consists of more than 1 million\nspeeches with extensive metadata, extracted from 5,355 parliamentary record\nfiles. We explain how it was constructed and the challenges that we had to\novercome. The dataset can be used for both computational linguistics and\npolitical analysis-ideally, combining the two. We present such an application,\nshowing (i) how the dataset can be used to study the change of word usage\nthrough time, (ii) between significant historical events and political parties,\n(iii) by evaluating and employing algorithms for detecting semantic shifts.\n", "date": "2022-10-23", "categories": []}
{"id": "2210.11989", "title": "Optimizing text representations to capture (dis)similarity between\n  political parties", "abstract": "  Even though fine-tuned neural language models have been pivotal in enabling\n\"deep\" automatic text analysis, optimizing text representations for specific\napplications remains a crucial bottleneck. In this study, we look at this\nproblem in the context of a task from computational social science, namely\nmodeling pairwise similarities between political parties. Our research question\nis what level of structural information is necessary to create robust text\nrepresentation, contrasting a strongly informed approach (which uses both claim\nspan and claim category annotations) with approaches that forgo one or both\ntypes of annotation with document structure-based heuristics. Evaluating our\nmodels on the manifestos of German parties for the 2021 federal election. We\nfind that heuristics that maximize within-party over between-party similarity\nalong with a normalization step lead to reliable party similarity prediction,\nwithout the need for manual annotation.\n", "date": "2022-10-21", "categories": []}
{"id": "2209.13487", "title": "Sentiment is all you need to win US Presidential elections", "abstract": "  Election speeches play an integral role in communicating the vision and\nmission of the candidates. From lofty promises to mud-slinging, the electoral\ncandidate accounts for all. However, there remains an open question about what\nexactly wins over the voters. In this work, we used state-of-the-art natural\nlanguage processing methods to study the speeches and sentiments of the\nRepublican candidate, Donald Trump, and Democratic candidate, Joe Biden,\nfighting for the 2020 US Presidential election. Comparing the racial dichotomy\nof the United States, we analyze what led to the victory and defeat of the\ndifferent candidates. We believe this work will inform the election campaigning\nstrategy and provide a basis for communicating to diverse crowds.\n", "date": "2022-09-27", "categories": []}
{"id": "2210.08362", "title": "PAR: Political Actor Representation Learning with Social Context and\n  Expert Knowledge", "abstract": "  Modeling the ideological perspectives of political actors is an essential\ntask in computational political science with applications in many downstream\ntasks. Existing approaches are generally limited to textual data and voting\nrecords, while they neglect the rich social context and valuable expert\nknowledge for holistic ideological analysis. In this paper, we propose\n\\textbf{PAR}, a \\textbf{P}olitical \\textbf{A}ctor \\textbf{R}epresentation\nlearning framework that jointly leverages social context and expert knowledge.\nSpecifically, we retrieve and extract factual statements about legislators to\nleverage social context information. We then construct a heterogeneous\ninformation network to incorporate social context and use relational graph\nneural networks to learn legislator representations. Finally, we train PAR with\nthree objectives to align representation learning with expert knowledge, model\nideological stance consistency, and simulate the echo chamber phenomenon.\nExtensive experiments demonstrate that PAR is better at augmenting political\ntext understanding and successfully advances the state-of-the-art in political\nperspective detection and roll call vote prediction. Further analysis proves\nthat PAR learns representations that reflect the political reality and provide\nnew insights into political behavior.\n", "date": "2022-10-15", "categories": []}
{"id": "2210.07518", "title": "Counterfactual Neural Temporal Point Process for Estimating Causal\n  Influence of Misinformation on Social Media", "abstract": "  Recent years have witnessed the rise of misinformation campaigns that spread\nspecific narratives on social media to manipulate public opinions on different\nareas, such as politics and healthcare. Consequently, an effective and\nefficient automatic methodology to estimate the influence of the misinformation\non user beliefs and activities is needed. However, existing works on\nmisinformation impact estimation either rely on small-scale psychological\nexperiments or can only discover the correlation between user behaviour and\nmisinformation. To address these issues, in this paper, we build up a causal\nframework that model the causal effect of misinformation from the perspective\nof temporal point process. To adapt the large-scale data, we design an\nefficient yet precise way to estimate the Individual Treatment Effect(ITE) via\nneural temporal point process and gaussian mixture models. Extensive\nexperiments on synthetic dataset verify the effectiveness and efficiency of our\nmodel. We further apply our model on a real-world dataset of social media posts\nand engagements about COVID-19 vaccines. The experimental results indicate that\nour model recognized identifiable causal effect of misinformation that hurts\npeople's subjective emotions toward the vaccines.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.05760", "title": "Detecting Propagators of Disinformation on Twitter Using Quantitative\n  Discursive Analysis", "abstract": "  Efforts by foreign actors to influence public opinion have gained\nconsiderable attention because of their potential to impact democratic\nelections. Thus, the ability to identify and counter sources of disinformation\nis increasingly becoming a top priority for government entities in order to\nprotect the integrity of democratic processes. This study presents a method of\nidentifying Russian disinformation bots on Twitter using centering resonance\nanalysis and Clauset-Newman-Moore community detection. The data reflect a\nsignificant degree of discursive dissimilarity between known Russian\ndisinformation bots and a control set of Twitter users during the timeframe of\nthe 2016 U.S. Presidential Election. The data also demonstrate statistically\nsignificant classification capabilities (MCC = 0.9070) based on community\nclustering. The prediction algorithm is very effective at identifying true\npositives (bots), but is not able to resolve true negatives (non-bots) because\nof the lack of discursive similarity between control users. This leads to a\nhighly sensitive means of identifying propagators of disinformation with a high\ndegree of discursive similarity on Twitter, with implications for limiting the\nspread of disinformation that could impact democratic processes.\n", "date": "2022-10-11", "categories": []}
{"id": "2210.03404", "title": "Quantifying Political Bias in News Articles", "abstract": "  Search bias analysis is getting more attention in recent years since search\nresults could affect In this work, we aim to establish an automated model for\nevaluating ideological bias in online news articles. The dataset is composed of\nnews articles in search results as well as the newspaper articles. The current\nautomated model results show that model capability is not sufficient to be\nexploited for annotating the documents automatically, thereby computing bias in\nsearch results.\n", "date": "2022-10-07", "categories": []}
{"id": "2210.00339", "title": "Longitudinal Sentiment Analyses for Radicalization Research:\n  Intertemporal Dynamics on Social Media Platforms and their Implications", "abstract": "  This discussion paper demonstrates how longitudinal sentiment analyses can\ndepict intertemporal dynamics on social media platforms, what challenges are\ninherent and how further research could benefit from a longitudinal\nperspective. Furthermore and since tools for sentiment analyses shall simplify\nand accelerate the analytical process regarding qualitative data at acceptable\ninter-rater reliability, their applicability in the context of radicalization\nresearch will be examined regarding the Tweets collected on January 6th 2021,\nthe day of the storming of the U.S. Capitol in Washington. Therefore, a total\nof 49,350 Tweets will be analyzed evenly distributed within three different\nsequences: before, during and after the U.S. Capitol in Washington was stormed.\nThese sequences highlight the intertemporal dynamics within comments on social\nmedia platforms as well as the possible benefits of a longitudinal perspective\nwhen using conditional means and conditional variances. Limitations regarding\nthe identification of supporters of such events and associated hate speech as\nwell as common application errors will be demonstrated as well. As a result,\nonly under certain conditions a longitudinal sentiment analysis can increase\nthe accuracy of evidence based predictions in the context of radicalization\nresearch.\n", "date": "2022-10-01", "categories": []}
{"id": "2209.13595", "title": "What Are You Anxious About? Examining Subjects of Anxiety during the\n  COVID-19 Pandemic", "abstract": "  COVID-19 poses disproportionate mental health consequences to the public\nduring different phases of the pandemic. We use a computational approach to\ncapture the specific aspects that trigger an online community's anxiety about\nthe pandemic and investigate how these aspects change over time. First, we\nidentified nine subjects of anxiety (SOAs) in a sample of Reddit posts ($N$=86)\nfrom r/COVID19\\_support using thematic analysis. Then, we quantified Reddit\nusers' anxiety by training algorithms on a manually annotated sample ($N$=793)\nto automatically label the SOAs in a larger chronological sample ($N$=6,535).\nThe nine SOAs align with items in various recently developed pandemic anxiety\nmeasurement scales. We observed that Reddit users' concerns about health risks\nremained high in the first eight months of the pandemic. These concerns\ndiminished dramatically despite the surge of cases occurring later. In general,\nusers' language disclosing the SOAs became less intense as the pandemic\nprogressed. However, worries about mental health and the future increased\nsteadily throughout the period covered in this study. People also tended to use\nmore intense language to describe mental health concerns than health risks or\ndeath concerns. Our results suggest that this online group's mental health\ncondition does not necessarily improve despite COVID-19 gradually weakening as\na health threat due to appropriate countermeasures. Our system lays the\ngroundwork for population health and epidemiology scholars to examine aspects\nthat provoke pandemic anxiety in a timely fashion.\n", "date": "2022-09-27", "categories": []}
{"id": "2209.13114", "title": "Style Matters! Investigating Linguistic Style in Online Communities", "abstract": "  Content has historically been the primary lens used to study language in\nonline communities. This paper instead focuses on the linguistic style of\ncommunities. While we know that individuals have distinguishable styles, here\nwe ask whether communities have distinguishable styles. Additionally, while\nprior work has relied on a narrow definition of style, we employ a broad\ndefinition involving 262 features to analyze the linguistic style of 9 online\ncommunities from 3 social media platforms discussing politics, television and\ntravel. We find that communities indeed have distinct styles. Also, style is an\nexcellent predictor of group membership (F-score 0.952 and Accuracy 96.09%).\nWhile on average it is statistically equivalent to predictions using content\nalone, it is more resilient to reductions in training data.\n", "date": "2022-09-27", "categories": []}
{"id": "2209.12030", "title": "Dead or Murdered? Predicting Responsibility Perception in Femicide News\n  Reports", "abstract": "  Different linguistic expressions can conceptualize the same event from\ndifferent viewpoints by emphasizing certain participants over others. Here, we\ninvestigate a case where this has social consequences: how do linguistic\nexpressions of gender-based violence (GBV) influence who we perceive as\nresponsible? We build on previous psycholinguistic research in this area and\nconduct a large-scale perception survey of GBV descriptions automatically\nextracted from a corpus of Italian newspapers. We then train regression models\nthat predict the salience of GBV participants with respect to different\ndimensions of perceived responsibility. Our best model (fine-tuned BERT) shows\nsolid overall performance, with large differences between dimensions and\nparticipants: salient _focus_ is more predictable than salient _blame_, and\nperpetrators' salience is more predictable than victims' salience. Experiments\nwith ridge regression models using different representations show that features\nbased on linguistic theory similarly to word-based features. Overall, we show\nthat different linguistic choices do trigger different perceptions of\nresponsibility, and that such perceptions can be modelled automatically. This\nwork can be a core instrument to raise awareness of the consequences of\ndifferent perspectivizations in the general public and in news producers alike.\n", "date": "2022-09-24", "categories": []}
{"id": "2209.08129", "title": "Evons: A Dataset for Fake and Real News Virality Analysis and Prediction", "abstract": "  We present a novel collection of news articles originating from fake and real\nnews media sources for the analysis and prediction of news virality. Unlike\nexisting fake news datasets which either contain claims or news article\nheadline and body, in this collection each article is supported with a Facebook\nengagement count which we consider as an indicator of the article virality. In\naddition we also provide the article description and thumbnail image with which\nthe article was shared on Facebook. These images were automatically annotated\nwith object tags and color attributes. Using cloud based vision analysis tools,\nthumbnail images were also analyzed for faces and detected faces were annotated\nwith facial attributes. We empirically investigate the use of this collection\non an example task of article virality prediction.\n", "date": "2022-09-16", "categories": []}
{"id": "2209.08799", "title": "Probing Spurious Correlations in Popular Event-Based Rumor Detection\n  Benchmarks", "abstract": "  As social media becomes a hotbed for the spread of misinformation, the\ncrucial task of rumor detection has witnessed promising advances fostered by\nopen-source benchmark datasets. Despite being widely used, we find that these\ndatasets suffer from spurious correlations, which are ignored by existing\nstudies and lead to severe overestimation of existing rumor detection\nperformance. The spurious correlations stem from three causes: (1) event-based\ndata collection and labeling schemes assign the same veracity label to multiple\nhighly similar posts from the same underlying event; (2) merging multiple data\nsources spuriously relates source identities to veracity labels; and (3)\nlabeling bias. In this paper, we closely investigate three of the most popular\nrumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and\npropose event-separated rumor detection as a solution to eliminate spurious\ncues. Under the event-separated setting, we observe that the accuracy of\nexisting state-of-the-art models drops significantly by over 40%, becoming only\ncomparable to a simple neural classifier. To better address this task, we\npropose Publisher Style Aggregation (PSA), a generalizable approach that\naggregates publisher posting records to learn writing style and veracity\nstance. Extensive experiments demonstrate that our method outperforms existing\nbaselines in terms of effectiveness, efficiency and generalizability.\n", "date": "2022-09-19", "categories": []}
{"id": "2209.07816", "title": "Properties of Reddit News Topical Interactions", "abstract": "  Most models of information diffusion online rely on the assumption that\npieces of information spread independently from each other. However, several\nworks pointed out the necessity of investigating the role of interactions in\nreal-world processes, and highlighted possible difficulties in doing so:\ninteractions are sparse and brief. As an answer, recent advances developed\nmodels to account for interactions in underlying publication dynamics. In this\narticle, we propose to extend and apply one such model to determine whether\ninteractions between news headlines on Reddit play a significant role in their\nunderlying publication mechanisms. After conducting an in-depth case study on\n100,000 news headline from 2019, we retrieve state-of-the-art conclusions about\ninteractions and conclude that they play a minor role in this dataset.\n", "date": "2022-09-16", "categories": []}
{"id": "2209.07272", "title": "Socially Enhanced Situation Awareness from Microblogs using Artificial\n  Intelligence: A Survey", "abstract": "  The rise of social media platforms provides an unbounded, infinitely rich\nsource of aggregate knowledge of the world around us, both historic and\nreal-time, from a human perspective. The greatest challenge we face is how to\nprocess and understand this raw and unstructured data, go beyond individual\nobservations and see the \"big picture\"--the domain of Situation Awareness. We\nprovide an extensive survey of Artificial Intelligence research, focusing on\nmicroblog social media data with applications to Situation Awareness, that\ngives the seminal work and state-of-the-art approaches across six thematic\nareas: Crime, Disasters, Finance, Physical Environment, Politics, and Health\nand Population. We provide a novel, unified methodological perspective,\nidentify key results and challenges, and present ongoing research directions.\n", "date": "2022-09-13", "categories": []}
{"id": "2209.06750", "title": "LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty", "abstract": "  Quantifying the moral narratives expressed in the user-generated text, news,\nor public discourses is fundamental for understanding individuals' concerns and\nviewpoints and preventing violent protests and social polarisation. The Moral\nFoundation Theory (MFT) was developed to operationalise morality in a\nfive-dimensional scale system. Recent developments of the theory urged for the\nintroduction of a new foundation, the Liberty Foundation. Being only recently\nadded to the theory, there are no available linguistic resources to assess\nwhether liberty is present in text corpora. Given its importance to current\nsocial issues such as the vaccination debate, we propose two data-driven\napproaches, deriving two candidate lexicons generated based on aligned\ndocuments from online news sources with different worldviews. After extensive\nexperimentation, we contribute to the research community a novel lexicon that\nassesses the liberty moral foundation in the way individuals with contrasting\nviewpoints express themselves through written text. The LibertyMFD dictionary\ncan be a valuable tool for policymakers to understand diverse viewpoints on\ncontroversial social issues such as vaccination, abortion, or even uprisings,\nas they happen and on a large scale.\n", "date": "2022-09-14", "categories": []}
{"id": "2209.05667", "title": "CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets\n  using Deep Learning Models", "abstract": "  Online news and information sources are convenient and accessible ways to\nlearn about current issues. For instance, more than 300 million people engage\nwith posts on Twitter globally, which provides the possibility to disseminate\nmisleading information. There are numerous cases where violent crimes have been\ncommitted due to fake news. This research presents the CovidMis20 dataset\n(COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets\ncollected from February to July 2020. CovidMis20 can be automatically updated\nto fetch the latest news and is publicly available at:\nhttps://github.com/everythingguy/CovidMis20. This research was conducted using\nBi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The\nresults showed that, with testing accuracy of 92.23% and 90.56%, respectively,\nthe ensemble CNN+Bi-GRU model consistently provided higher accuracy than the\nBi-LSTM model.\n", "date": "2022-09-13", "categories": []}
{"id": "2209.03162", "title": "Machine Learning-based Automatic Annotation and Detection of COVID-19\n  Fake News", "abstract": "  COVID-19 impacted every part of the world, although the misinformation about\nthe outbreak traveled faster than the virus. Misinformation spread through\nonline social networks (OSN) often misled people from following correct medical\npractices. In particular, OSN bots have been a primary source of disseminating\nfalse information and initiating cyber propaganda. Existing work neglects the\npresence of bots that act as a catalyst in the spread and focuses on fake news\ndetection in 'articles shared in posts' rather than the post (textual) content.\nMost work on misinformation detection uses manually labeled datasets that are\nhard to scale for building their predictive models. In this research, we\novercome this challenge of data scarcity by proposing an automated approach for\nlabeling data using verified fact-checked statements on a Twitter dataset. In\naddition, we combine textual features with user-level features (such as\nfollowers count and friends count) and tweet-level features (such as number of\nmentions, hashtags and urls in a tweet) to act as additional indicators to\ndetect misinformation. Moreover, we analyzed the presence of bots in tweets and\nshow that bots change their behavior over time and are most active during the\nmisinformation campaign. We collected 10.22 Million COVID-19 related tweets and\nused our annotation model to build an extensive and original ground truth\ndataset for classification purposes. We utilize various machine learning models\nto accurately detect misinformation and our best classification model achieves\nprecision (82%), recall (96%), and false positive rate (3.58%). Also, our bot\nanalysis indicates that bots generated approximately 10% of misinformation\ntweets. Our methodology results in substantial exposure of false information,\nthus improving the trustworthiness of information disseminated through social\nmedia platforms.\n", "date": "2022-09-07", "categories": []}
{"id": "2209.03219", "title": "Reconstructing signed relations from interaction data", "abstract": "  Positive and negative relations play an essential role in human behavior and\nshape the communities we live in. Despite their importance, data about signed\nrelations is rare and commonly gathered through surveys. Interaction data is\nmore abundant, for instance, in the form of proximity or communication data. So\nfar, though, it could not be utilized to detect signed relations. In this\npaper, we show how the underlying signed relations can be extracted with such\ndata. Employing a statistical network approach, we construct networks of signed\nrelations in four communities. We then show that these relations correspond to\nthe ones reported in surveys. Additionally, the inferred relations allow us to\nstudy the homophily of individuals with respect to gender, religious beliefs,\nand financial backgrounds. We evaluate the importance of triads in the signed\nnetwork to study group cohesion.\n", "date": "2022-09-07", "categories": []}
{"id": "2209.01536", "title": "Interpretable Fake News Detection with Topic and Deep Variational Models", "abstract": "  The growing societal dependence on social media and user generated content\nfor news and information has increased the influence of unreliable sources and\nfake content, which muddles public discourse and lessens trust in the media.\nValidating the credibility of such information is a difficult task that is\nsusceptible to confirmation bias, leading to the development of algorithmic\ntechniques to distinguish between fake and real news. However, most existing\nmethods are challenging to interpret, making it difficult to establish trust in\npredictions, and make assumptions that are unrealistic in many real-world\nscenarios, e.g., the availability of audiovisual features or provenance. In\nthis work, we focus on fake news detection of textual content using\ninterpretable features and methods. In particular, we have developed a deep\nprobabilistic model that integrates a dense representation of textual news\nusing a variational autoencoder and bi-directional Long Short-Term Memory\n(LSTM) networks with semantic topic-related features inferred from a Bayesian\nadmixture model. Extensive experimental studies with 3 real-world datasets\ndemonstrate that our model achieves comparable performance to state-of-the-art\ncompeting models while facilitating model interpretability from the learned\ntopics. Finally, we have conducted model ablation studies to justify the\neffectiveness and accuracy of integrating neural embeddings and topic features\nboth quantitatively by evaluating performance and qualitatively through\nseparability in lower dimensional embeddings.\n", "date": "2022-09-04", "categories": []}
{"id": "2209.00495", "title": "Searching for Structure in Unfalsifiable Claims", "abstract": "  Social media platforms give rise to an abundance of posts and comments on\nevery topic imaginable. Many of these posts express opinions on various aspects\nof society, but their unfalsifiable nature makes them ill-suited to\nfact-checking pipelines. In this work, we aim to distill such posts into a\nsmall set of narratives that capture the essential claims related to a given\ntopic. Understanding and visualizing these narratives can facilitate more\ninformed debates on social media. As a first step towards systematically\nidentifying the underlying narratives on social media, we introduce PAPYER, a\nfine-grained dataset of online comments related to hygiene in public restrooms,\nwhich contains a multitude of unfalsifiable claims. We present a\nhuman-in-the-loop pipeline that uses a combination of machine and human kernels\nto discover the prevailing narratives and show that this pipeline outperforms\nrecent large transformer models and state-of-the-art unsupervised topic models.\n", "date": "2022-08-19", "categories": []}
{"id": "2208.13833", "title": "Evolving Label Usage within Generation Z when Self-Describing Sexual\n  Orientation", "abstract": "  Evaluating change in ranked term importance in a growing corpus is a powerful\ntool for understanding changes in vocabulary usage. In this paper, we analyze a\ncorpus of free-response answers where 33,993 LGBTQ Generation Z respondents\nfrom age 13 to 24 in the United States are asked to self-describe their sexual\norientation. We observe that certain labels, such as bisexual, pansexual, and\nlesbian, remain equally important across age groups. The importance of other\nlabels, such as homosexual, demisexual, and omnisexual, evolve across age\ngroups. Although Generation Z is often stereotyped as homogenous, we observe\nnoticeably different label usage when self-describing sexual orientation within\nit. We urge that interested parties must routinely survey the most important\nsexual orientation labels to their target audience and refresh their materials\n(such as demographic surveys) to reflect the constantly evolving LGBTQ\ncommunity and create an inclusive environment.\n", "date": "2022-08-29", "categories": []}
{"id": "2208.13523", "title": "Demystifying the COVID-19 vaccine discourse on Twitter", "abstract": "  Developing an understanding of the public discourse on COVID-19 vaccination\non social media is important not only for addressing the current COVID-19\npandemic, but also for future pathogen outbreaks. We examine a Twitter dataset\ncontaining 75 million English tweets discussing COVID-19 vaccination from March\n2020 to March 2021. We train a stance detection algorithm using natural\nlanguage processing (NLP) techniques to classify tweets as `anti-vax' or\n`pro-vax', and examine the main topics of discourse using topic modelling\ntechniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets\n(10 million), a majority of tweets from both stances (63% anti-vax and 53%\npro-vax tweets) came from dual-stance users who posted both pro- and anti-vax\ntweets during the observation period. Pro-vax tweets focused mostly on vaccine\ndevelopment, while anti-vax tweets covered a wide range of topics, some of\nwhich included genuine concerns, though there was a large dose of falsehoods. A\nnumber of topics were common to both stances, though pro- and anti-vax tweets\ndiscussed them from opposite viewpoints. Memes and jokes were amongst the most\nretweeted messages. Whereas concerns about polarisation and online prevalence\nof anti-vax discourse are unfounded, targeted countering of falsehoods is\nimportant.\n", "date": "2022-08-29", "categories": []}
{"id": "2208.11582", "title": "Graphical Models of False Information and Fact Checking Ecosystems", "abstract": "  The wide spread of false information online including misinformation and\ndisinformation has become a major problem for our highly digitised and\nglobalised society. A lot of research has been done to better understand\ndifferent aspects of false information online such as behaviours of different\nactors and patterns of spreading, and also on better detection and prevention\nof such information using technical and socio-technical means. One major\napproach to detect and debunk false information online is to use human\nfact-checkers, who can be helped by automated tools. Despite a lot of research\ndone, we noticed a significant gap on the lack of conceptual models describing\nthe complicated ecosystems of false information and fact checking. In this\npaper, we report the first graphical models of such ecosystems, focusing on\nfalse information online in multiple contexts, including traditional media\noutlets and user-generated content. The proposed models cover a wide range of\nentity types and relationships, and can be a new useful tool for researchers\nand practitioners to study false information online and the effects of fact\nchecking.\n", "date": "2022-08-24", "categories": []}
{"id": "2208.10766", "title": "We Are in This Together: Quantifying Community Subjective Wellbeing and\n  Resilience", "abstract": "  The COVID-19 pandemic disrupted everyone's life across the world. In this\nwork, we characterize the subjective wellbeing patterns of 112 cities across\nthe United States during the pandemic prior to vaccine availability, as\nexhibited in subreddits corresponding to the cities. We quantify subjective\nwellbeing using positive and negative affect. We then measure the pandemic's\nimpact by comparing a community's observed wellbeing with its expected\nwellbeing, as forecasted by time series models derived from prior to the\npandemic.We show that general community traits reflected in language can be\npredictive of community resilience. We predict how the pandemic would impact\nthe wellbeing of each community based on linguistic and interaction features\nfrom normal times \\textit{before} the pandemic. We find that communities with\ninteraction characteristics corresponding to more closely connected users and\nhigher engagement were less likely to be significantly impacted. Notably, we\nfind that communities that talked more about social ties normally experienced\nin-person, such as friends, family, and affiliations, were actually more likely\nto be impacted. Additionally, we use the same features to also predict how\nquickly each community would recover after the initial onset of the pandemic.\nWe similarly find that communities that talked more about family, affiliations,\nand identifying as part of a group had a slower recovery.\n", "date": "2022-08-23", "categories": []}
{"id": "2208.09214", "title": "Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With\n  Experts?", "abstract": "  Fact-checking is one of the effective solutions in fighting online\nmisinformation. However, traditional fact-checking is a process requiring\nscarce expert human resources, and thus does not scale well on social media\nbecause of the continuous flow of new content to be checked. Methods based on\ncrowdsourcing have been proposed to tackle this challenge, as they can scale\nwith a smaller cost, but, while they have shown to be feasible, have always\nbeen studied in controlled environments. In this work, we study the first\nlarge-scale effort of crowdsourced fact-checking deployed in practice, started\nby Twitter with the Birdwatch program. Our analysis shows that crowdsourcing\nmay be an effective fact-checking strategy in some settings, even comparable to\nresults obtained by human experts, but does not lead to consistent, actionable\nresults in others. We processed 11.9k tweets verified by the Birdwatch program\nand report empirical evidence of i) differences in how the crowd and experts\nselect content to be fact-checked, ii) how the crowd and the experts retrieve\ndifferent resources to fact-check, and iii) the edge the crowd shows in\nfact-checking scalability and efficiency as compared to expert checkers.\n", "date": "2022-08-19", "categories": []}
{"id": "2208.07686", "title": "FALSE: Fake News Automatic and Lightweight Solution", "abstract": "  Fake news existed ever since there was news, from rumors to printed media\nthen radio and television. Recently, the information age, with its\ncommunications and Internet breakthroughs, exacerbated the spread of fake news.\nAdditionally, aside from e-Commerce, the current Internet economy is dependent\non advertisements, views and clicks, which prompted many developers to bait the\nend users to click links or ads. Consequently, the wild spread of fake news\nthrough social media networks has impacted real world issues from elections to\n5G adoption and the handling of the Covid- 19 pandemic. Efforts to detect and\nthwart fake news has been there since the advent of fake news, from fact\ncheckers to artificial intelligence-based detectors. Solutions are still\nevolving as more sophisticated techniques are employed by fake news\npropagators. In this paper, R code have been used to study and visualize a\nmodern fake news dataset. We use clustering, classification, correlation and\nvarious plots to analyze and present the data. The experiments show high\nefficiency of classifiers in telling apart real from fake news.\n", "date": "2022-08-16", "categories": []}
{"id": "2208.04620", "title": "Cascade-based Echo Chamber Detection", "abstract": "  Despite echo chambers in social media have been under considerable scrutiny,\ngeneral models for their detection and analysis are missing. In this work, we\naim to fill this gap by proposing a probabilistic generative model that\nexplains social media footprints -- i.e., social network structure and\npropagations of information -- through a set of latent communities,\ncharacterized by a degree of echo-chamber behavior and by an opinion polarity.\nSpecifically, echo chambers are modeled as communities that are permeable to\npieces of information with similar ideological polarity, and impermeable to\ninformation of opposed leaning: this allows discriminating echo chambers from\ncommunities that lack a clear ideological alignment.\n  To learn the model parameters we propose a scalable, stochastic adaptation of\nthe Generalized Expectation Maximization algorithm, that optimizes the joint\nlikelihood of observing social connections and information propagation.\nExperiments on synthetic data show that our algorithm is able to correctly\nreconstruct ground-truth latent communities with their degree of echo-chamber\nbehavior and opinion polarity. Experiments on real-world data about polarized\nsocial and political debates, such as the Brexit referendum or the COVID-19\nvaccine campaign, confirm the effectiveness of our proposal in detecting echo\nchambers. Finally, we show how our model can improve accuracy in auxiliary\npredictive tasks, such as stance detection and prediction of future\npropagations.\n", "date": "2022-08-09", "categories": []}
{"id": "2208.03796", "title": "Estimating Topic Exposure for Under-Represented Users on Social Media", "abstract": "  Online Social Networks (OSNs) facilitate access to a variety of data allowing\nresearchers to analyze users' behavior and develop user behavioral analysis\nmodels. These models rely heavily on the observed data which is usually biased\ndue to the participation inequality. This inequality consists of three groups\nof online users: the lurkers - users that solely consume the content, the\nengagers - users that contribute little to the content creation, and the\ncontributors - users that are responsible for creating the majority of the\nonline content. Failing to consider the contribution of all the groups while\ninterpreting population-level interests or sentiments may yield biased results.\nTo reduce the bias induced by the contributors, in this work, we focus on\nhighlighting the engagers' contributions in the observed data as they are more\nlikely to contribute when compared to lurkers, and they comprise a bigger\npopulation as compared to the contributors. The first step in behavioral\nanalysis of these users is to find the topics they are exposed to but did not\nengage with. To do so, we propose a novel framework that aids in identifying\nthese users and estimates their topic exposure. The exposure estimation\nmechanism is modeled by incorporating behavioral patterns from similar\ncontributors as well as users' demographic and profile information.\n", "date": "2022-08-07", "categories": []}
{"id": "2208.00479", "title": "The impact of Twitter on political influence on the choice of a running\n  mate: Social Network Analysis and Semantic Analysis -- A Review", "abstract": "  In this new era of social media, social networks are becoming increasingly\nimportant sources of user-generated content on the internet. These kinds of\ninformation resources, which include a lot of people's feelings, opinions,\nfeedback, and reviews, are very useful for big businesses, markets, politics,\njournalism, and many other fields. Politics is one of the most talked-about and\npopular topics on social media networks right now. Many politicians use\nmicro-blogging services like Twitter because they have a large number of\nfollowers and supporters on those networks. Politicians, political parties,\npolitical organizations, and foundations use social media networks to\ncommunicate with citizens ahead of time. Today, social media is used by\nhundreds of thousands of political groups and politicians. On these social\nmedia networks, every politician and political party has millions of followers,\nand politicians find new and innovative ways to urge individuals to participate\nin politics. Furthermore, social media assists politicians in various\ndecision-making processes by providing recommendations, such as developing\npolicies and strategies based on previous experiences, recommending and\nselecting suitable candidates for a particular constituency, recommending a\nsuitable person for a particular position in the party, and launching a\npolitical campaign based on citizen sentiments on various issues and\ncontroversies, among other things. This research is a review on the use of\nsocial network analysis (SNA) and semantic analysis (SA) on the Twitter\nplatform to study the supporters networks of political leaders because it can\nhelp in decision-making when predicting their political futures.\n", "date": "2022-07-31", "categories": []}
{"id": "2207.14740", "title": "Rating the Crisis of Online Public Opinion Using a Multi-Level Index\n  System", "abstract": "  Online public opinion usually spreads rapidly and widely, thus a small\nincident probably evolves into a large social crisis in a very short time, and\nresults in a heavy loss in credit or economic aspects. We propose a method to\nrate the crisis of online public opinion based on a multi-level index system to\nevaluate the impact of events objectively. Firstly, the dissemination mechanism\nof online public opinion is explained from the perspective of information\necology. According to the mechanism, some evaluation indexes are selected\nthrough correlation analysis and principal component analysis. Then, a\nclassification model of text emotion is created via the training by deep\nlearning to achieve the accurate quantification of the emotional indexes in the\nindex system. Finally, based on the multi-level evaluation index system and\ngrey correlation analysis, we propose a method to rate the crisis of online\npublic opinion. The experiment with the real-time incident show that this\nmethod can objectively evaluate the emotional tendency of Internet users and\nrate the crisis in different dissemination stages of online public opinion. It\nis helpful to realizing the crisis warning of online public opinion and timely\nblocking the further spread of the crisis.\n", "date": "2022-07-29", "categories": []}
{"id": "2207.13500", "title": "Modelling Social Context for Fake News Detection: A Graph Neural Network\n  Based Approach", "abstract": "  Detection of fake news is crucial to ensure the authenticity of information\nand maintain the news ecosystems reliability. Recently, there has been an\nincrease in fake news content due to the recent proliferation of social media\nand fake content generation techniques such as Deep Fake. The majority of the\nexisting modalities of fake news detection focus on content based approaches.\nHowever, most of these techniques fail to deal with ultra realistic synthesized\nmedia produced by generative models. Our recent studies find that the\npropagation characteristics of authentic and fake news are distinguishable,\nirrespective of their modalities. In this regard, we have investigated the\nauxiliary information based on social context to detect fake news. This paper\nhas analyzed the social context of fake news detection with a hybrid graph\nneural network based approach. This hybrid model is based on integrating a\ngraph neural network on the propagation of news and bi directional encoder\nrepresentations from the transformers model on news content to learn the text\nfeatures. Thus this proposed approach learns the content as well as the context\nfeatures and hence able to outperform the baseline models with an f1 score of\n0.91 on PolitiFact and 0.93 on the Gossipcop dataset, respectively\n", "date": "2022-07-27", "categories": []}
{"id": "2207.12724", "title": "An Automated News Bias Classifier Using Caenorhabditis Elegans Inspired\n  Recursive Feedback Network Architecture", "abstract": "  Traditional approaches to classify the political bias of news articles have\nfailed to generate accurate, generalizable results. Existing networks premised\non CNNs and DNNs lack a model to identify and extrapolate subtle indicators of\nbias like word choice, context, and presentation. In this paper, we propose a\nnetwork architecture that achieves human-level accuracy in assigning bias\nclassifications to articles. The underlying model is based on a novel Mesh\nNeural Network (MNN),this structure enables feedback and feedforward synaptic\nconnections between any two neurons in the mesh. The MNN ontains six network\nconfigurations that utilize Bernoulli based random sampling, pre-trained DNNs,\nand a network modelled after the C. Elegans nematode. The model is trained on\nover ten-thousand articles scraped from AllSides.com which are labelled to\nindicate political bias. The parameters of the network are then evolved using a\ngenetic algorithm suited to the feedback neural structure. Finally, the best\nperforming model is applied to five popular news sources in the United States\nover a fifty-day trial to quantify political biases in the articles they\ndisplay. We hope our project can spur research into biological solutions for\nNLP tasks and provide accurate tools for citizens to understand subtle biases\nin the articles they consume.\n", "date": "2022-07-26", "categories": []}
{"id": "2207.11521", "title": "Vaccine Discourse on Twitter During the COVID-19 Pandemic", "abstract": "  Since the onset of the COVID-19 pandemic, vaccines have been an important\ntopic in public discourse. The discussions around vaccines are polarized as\nsome see them as an important measure to end the pandemic, and others are\nhesitant or find them harmful. This study investigates posts related to\nCOVID-19 vaccines on Twitter and focuses on those which have a negative stance\ntoward vaccines. A dataset of 16,713,238 English tweets related to COVID-19\nvaccines was collected covering the period from March 1, 2020, to July 31,\n2021. We used the Scikit-learn Python library to apply a support vector machine\n(SVM) classifier to identify the tweets with a negative stance toward the\nCOVID-19 vaccines. A total of 5,163 tweets were used to train the classifier,\nout of which a subset of 2,484 tweets were manually annotated by us and made\npublicly available. We used the BERTtopic model to extract and investigate the\ntopics discussed within the negative tweets and how they changed over time. We\nshow that the negativity with respect to COVID-19 vaccines has decreased over\ntime along with the vaccine roll-outs. We identify 37 topics of discussion and\npresent their respective importance over time. We show that popular topics\nconsist of conspiratorial discussions such as 5G towers and microchips, but\nalso contain legitimate concerns around vaccination safety and side effects as\nwell as concerns about policies. Our study shows that even unpopular opinions\nor conspiracy theories can become widespread when paired with a widely popular\ndiscussion topic such as COVID-19 vaccines. Understanding the concerns and the\ndiscussed topics and how they change over time is essential for policymakers\nand public health authorities to provide better and in-time information and\npolicies, to facilitate vaccination of the population in future similar crises.\n", "date": "2022-07-23", "categories": []}
{"id": "2207.10641", "title": "Deep Learning Reveals Patterns of Diverse and Changing Sentiments\n  Towards COVID-19 Vaccines Based on 11 Million Tweets", "abstract": "  Over 12 billion doses of COVID-19 vaccines have been administered at the time\nof writing. However, public perceptions of vaccines have been complex. We\nanalyzed COVID-19 vaccine-related tweets to understand the evolving perceptions\nof COVID-19 vaccines. We finetuned a deep learning classifier using a\nstate-of-the-art model, XLNet, to detect each tweet's sentiment automatically.\nWe employed validated methods to extract the users' race or ethnicity, gender,\nage, and geographical locations from user profiles. Incorporating multiple data\nsources, we assessed the sentiment patterns among subpopulations and juxtaposed\nthem against vaccine uptake data to unravel their interactive patterns.\n11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users\nover two years were analyzed. The finetuned model for sentiment classification\nyielded an accuracy of 0.92 on testing set. Users from various demographic\ngroups demonstrated distinct patterns in sentiments towards COVID-19 vaccines.\nUser sentiments became more positive over time, upon which we observed\nsubsequent upswing in the population-level vaccine uptake. Surrounding dates\nwhere positive sentiments crest, we detected encouraging news or events\nregarding vaccine development and distribution. Positive sentiments in\npregnancy-related tweets demonstrated a delayed pattern compared with trends in\ngeneral population, with postponed vaccine uptake trends. Distinctive patterns\nacross subpopulations suggest the need of tailored strategies. Global news and\nevents profoundly involved in shaping users' thoughts on social media.\nPopulations with additional concerns, such as pregnancy, demonstrated more\nsubstantial hesitancy since lack of timely recommendations. Feature analysis\nrevealed hesitancies of various subpopulations stemmed from clinical trial\nlogics, risks and complications, and urgency of scientific evidence.\n", "date": "2022-07-05", "categories": []}
{"id": "2207.07586", "title": "Does Twitter know your political views? POLiTweets dataset and\n  semi-automatic method for political leaning discovery", "abstract": "  Every day, the world is flooded by millions of messages and statements posted\non Twitter or Facebook. Social media platforms try to protect users' personal\ndata, but there still is a real risk of misuse, including elections\nmanipulation. Did you know, that only 13 posts addressing important or\ncontroversial topics for society are enough to predict one's political\naffiliation with a 0.85 F1-score? To examine this phenomenon, we created a\nnovel universal method of semi-automated political leaning discovery. It relies\non a heuristical data annotation procedure, which was evaluated to achieve 0.95\nagreement with human annotators (counted as an accuracy metric). We also\npresent POLiTweets - the first publicly open Polish dataset for political\naffiliation discovery in a multi-party setup, consisting of over 147k tweets\nfrom almost 10k Polish-writing users annotated heuristically and almost 40k\ntweets from 166 users annotated manually as a test set. We used our data to\nstudy the aspects of domain shift in the context of topics and the type of\ncontent writers - ordinary citizens vs. professional politicians.\n", "date": "2022-06-14", "categories": []}
{"id": "2207.03990", "title": "Predicting Opinion Dynamics via Sociologically-Informed Neural Networks", "abstract": "  Opinion formation and propagation are crucial phenomena in social networks\nand have been extensively studied across several disciplines. Traditionally,\ntheoretical models of opinion dynamics have been proposed to describe the\ninteractions between individuals (i.e., social interaction) and their impact on\nthe evolution of collective opinions. Although these models can incorporate\nsociological and psychological knowledge on the mechanisms of social\ninteraction, they demand extensive calibration with real data to make reliable\npredictions, requiring much time and effort. Recently, the widespread use of\nsocial media platforms provides new paradigms to learn deep learning models\nfrom a large volume of social media data. However, these methods ignore any\nscientific knowledge about the mechanism of social interaction. In this work,\nwe present the first hybrid method called Sociologically-Informed Neural\nNetwork (SINN), which integrates theoretical models and social media data by\ntransporting the concepts of physics-informed neural networks (PINNs) from\nnatural science (i.e., physics) into social science (i.e., sociology and social\npsychology). In particular, we recast theoretical models as ordinary\ndifferential equations (ODEs). Then we train a neural network that\nsimultaneously approximates the data and conforms to the ODEs that represent\nthe social scientific knowledge. In addition, we extend PINNs by integrating\nmatrix factorization and a language model to incorporate rich side information\n(e.g., user profiles) and structural knowledge (e.g., cluster structure of the\nsocial interaction network). Moreover, we develop an end-to-end training\nprocedure for SINN, which involves Gumbel-Softmax approximation to include\nstochastic mechanisms of social interaction. Extensive experiments on\nreal-world and synthetic datasets show SINN outperforms six baseline methods in\npredicting opinion dynamics.\n", "date": "2022-07-07", "categories": []}
{"id": "2207.01483", "title": "\"COVID-19 was a FIFA conspiracy #curropt\": An Investigation into the\n  Viral Spread of COVID-19 Misinformation", "abstract": "  The outbreak of the infectious and fatal disease COVID-19 has revealed that\npandemics assail public health in two waves: first, from the contagion itself\nand second, from plagues of suspicion and stigma. Now, we have in our hands and\non our phones an outbreak of moral controversy. Modern dependency on social\nmedias has not only facilitated access to the locations of vaccine clinics and\ntesting sites but also-and more frequently-to the convoluted explanations of\nhow \"COVID-19 was a FIFA conspiracy\"[1]. The MIT Media Lab finds that false\nnews \"diffuses significantly farther, faster, deeper, and more broadly than\ntruth, in all categories of information, and by an order of magnitude\"[2]. The\nquestion is, how does the spread of misinformation interact with a physical\nepidemic disease? In this paper, we estimate the extent to which misinformation\nhas influenced the course of the COVID-19 pandemic using natural language\nprocessing models and provide a strategy to combat social media posts that are\nlikely to cause widespread harm.\n", "date": "2022-06-12", "categories": []}
{"id": "2206.14619", "title": "A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter", "abstract": "  Vaccine hesitancy is considered as one main cause of the stagnant uptake\nratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently\nsupplied. Fast and accurate grasp of public attitudes toward vaccination is\ncritical to address vaccine hesitancy, and social media platforms have proved\nto be an effective source of public opinions. In this paper, we describe the\ncollection and release of a dataset of tweets related to COVID-19 vaccines.\nThis dataset consists of the IDs of 2,198,090 tweets collected from Western\nEurope, 17,934 of which are annotated with the originators' vaccination\nstances. Our annotation will facilitate using and developing data-driven models\nto extract vaccination attitudes from social media posts and thus further\nconfirm the power of social media in public health surveillance. To lay the\ngroundwork for future research, we not only perform statistical analysis and\nvisualisation of our dataset, but also evaluate and compare the performance of\nestablished text-based benchmarks in vaccination stance extraction. We\ndemonstrate one potential use of our data in practice in tracking the temporal\nchanges of public COVID-19 vaccination attitudes.\n", "date": "2022-06-27", "categories": []}
{"id": "2206.13456", "title": "\"Double vaccinated, 5G boosted!\": Learning Attitudes towards COVID-19\n  Vaccination from Social Media", "abstract": "  To address the vaccine hesitancy which impairs the efforts of the COVID-19\nvaccination campaign, it is imperative to understand public vaccination\nattitudes and timely grasp their changes. In spite of reliability and\ntrustworthiness, conventional attitude collection based on surveys is\ntime-consuming and expensive, and cannot follow the fast evolution of\nvaccination attitudes. We leverage the textual posts on social media to extract\nand track users' vaccination stances in near real time by proposing a deep\nlearning framework. To address the impact of linguistic features such as\nsarcasm and irony commonly used in vaccine-related discourses, we integrate\ninto the framework the recent posts of a user's social network neighbours to\nhelp detect the user's genuine attitude. Based on our annotated dataset from\nTwitter, the models instantiated from our framework can increase the\nperformance of attitude extraction by up to 23% compared to state-of-the-art\ntext-only models. Using this framework, we successfully validate the\nfeasibility of using social media to track the evolution of vaccination\nattitudes in real life. We further show one practical use of our framework by\nvalidating the possibility to forecast a user's vaccine hesitancy changes with\ninformation perceived from social media.\n", "date": "2022-06-27", "categories": []}
{"id": "2206.09946", "title": "Short Video Uprising: How #BlackLivesMatter Content on TikTok Challenges\n  the Protest Paradigm", "abstract": "  This study uses TikTok (N = 8,173) to examine how short-form video platforms\nchallenge the protest paradigm in the recent Black Lives Matter movement. A\ncomputer-mediated visual analysis, computer vision, is employed to identify the\npresence of four visual frames of protest (riot, confrontation, spectacle, and\ndebate) in multimedia content. Results of descriptive statistics and the t-test\nindicate that the three delegitimizing frames - riot, confrontation, and\nspectacle - are rarely found on TikTok, whereas the debate frame, that empowers\nmarginalized communities, dominates the public sphere. However, although the\nthree delegitimizing frames receive lower social media visibility, as measured\nby views, likes, shares, followers, and durations, legitimizing elements, such\nas the debate frame, minority identities, and unofficial sources, are not\ngenerally favored by TikTok audiences. This study concludes that while\nshort-form video platforms could potentially challenge the protest paradigm on\nthe content creators' side, the audiences' preference as measured by social\nmedia visibility might still be moderately associated with the protest\nparadigm.\n", "date": "2022-06-20", "categories": []}
{"id": "2206.03563", "title": "Two Ways of Understanding Social Dynamics: Analyzing the Predictability\n  of Emergence of Objects in Reddit r/place Dependent on Locality in Space and\n  Time", "abstract": "  Lately, studying social dynamics in interacting agents has been boosted by\nthe power of computer models, which bring the richness of qualitative work,\nwhile offering the precision, transparency, extensiveness, and replicability of\nstatistical and mathematical approaches. A particular set of phenomena for the\nstudy of social dynamics is Web collaborative platforms. A dataset of interest\nis r/place, a collaborative social experiment held in 2017 on Reddit, which\nconsisted of a shared online canvas of 1000 pixels by 1000 pixels co-edited by\nover a million recorded users over 72 hours. In this paper, we designed and\ncompared two methods to analyze the dynamics of this experiment. Our first\nmethod consisted in approximating the set of 2D cellular-automata-like rules\nused to generate the canvas images and how these rules change over time. The\nsecond method consisted in a convolutional neural network (CNN) that learned an\napproximation to the generative rules in order to generate the complex outcomes\nof the canvas. Our results indicate varying context-size dependencies for the\npredictability of different objects in r/place in time and space. They also\nindicate a surprising peak in difficulty to statistically infer behavioral\nrules towards the middle of the social experiment, while user interactions did\nnot drop until before the end. The combination of our two approaches, one\nrule-based and the other statistical CNN-based, shows the ability to highlight\ndiverse aspects of analyzing social dynamics.\n", "date": "2022-06-02", "categories": []}
{"id": "2206.07113", "title": "Minorities in networks and algorithms", "abstract": "  In this chapter, we provide an overview of recent advances in data-driven and\ntheory-informed complex models of social networks and their potential in\nunderstanding societal inequalities and marginalization. We focus on\ninequalities arising from networks and network-based algorithms and how they\naffect minorities. In particular, we examine how homophily and mixing biases\nshape large and small social networks, influence perception of minorities, and\naffect collaboration patterns. We also discuss dynamical processes on and of\nnetworks and the formation of norms and health inequalities. Additionally, we\nargue that network modeling is paramount for unveiling the effect of ranking\nand social recommendation algorithms on the visibility of minorities. Finally,\nwe highlight the key challenges and future opportunities in this emerging\nresearch topic.\n", "date": "2022-06-14", "categories": []}
{"id": "2206.00397", "title": "Predicting Political Ideology from Digital Footprints", "abstract": "  This paper proposes a new method to predict individual political ideology\nfrom digital footprints on one of the world's largest online discussion forum.\nWe compiled a unique data set from the online discussion forum reddit that\ncontains information on the political ideology of around 91,000 users as well\nas records of their comment frequency and the comments' text corpus in over\n190,000 different subforums of interest. Applying a set of statistical learning\napproaches, we show that information about activity in non-political discussion\nforums alone, can very accurately predict a user's political ideology.\nDepending on the model, we are able to predict the economic dimension of\nideology with an accuracy of up to 90.63% and the social dimension with and\naccuracy of up to 82.02%. In comparison, using the textual features from actual\ncomments does not improve predictive accuracy. Our paper highlights the\nimportance of revealed digital behaviour to complement stated preferences from\ndigital communication when analysing human preferences and behaviour using\nonline data.\n", "date": "2022-06-01", "categories": []}
{"id": "2606.21485", "title": "Economic Transformation and Cultural Change: Evidence from Two Centuries of French Drama", "abstract": "How do large-scale economic transformations shape cultural production? We address this question by combining computational linguistics, econometrics, and formal modelling, using French drama as a well-documented empirical laboratory. Applying latent Dirichlet allocation to a corpus of 1,215 theatrical texts published between 1700 and 1900, we show that aristocratic discourse centred on sovereignty and political authority was gradually displaced by bourgeois and household economic themes as French capitalism developed. Bayesian vector autoregressive models with max-share shock identification suggest a temporal shift in the literary response to economic shocks: bourgeois everyday-life themes reacted to GDP shocks in the eighteenth century, whereas household-economic concerns became responsive only after 1820, amid accelerating industrialisation. A discrete-choice model shows that peer effects among authors and sensitivity to prevailing economic conditions can jointly account for these dynamics. Monte Carlo simulations reproduce the observed historical trajectory with reasonable fidelity. These findings offer a quantitative framework for understanding how economic transformations propagate into cultural production through identifiable social mechanisms, contributing to the study of cultural evolution and the long-run relationship between institutions and literary discourse.", "date": "2026-06-19", "categories": []}
{"id": "2606.22785", "title": "Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea", "abstract": "Coordinated foreign influence operations pose a growing threat to online platforms, but detecting state-linked troll activity and tracking its evolution remain challenging. This paper presents an explainable machine learning framework for theory-guided detection and longitudinal analysis of suspected trolling within Korean online news comment sections. Our hierarchical model classifies comments along three dimensions central to influence campaigns: foreign origin, moral-emotional framing, and target country. To support explainability, it also extracts brief span-level textual evidence that provides human-interpretable rationales. We apply the approach to 112M South Korean news comments authored by 4M users over nearly 20 years, identifying 23,998 accounts exhibiting behavior consistent with coordinated manipulation. Analyzing these accounts, we find that they predominantly rely on morally condemning rhetoric rather than direct promotion of foreign-aligned narratives; this rhetoric receives significantly higher user engagement. Among the highest-engagement comments, the moral condemnation most frequently targets domestic political figures (e.g., presidents or party leaders) on both the left and the right, potentially amplifying polarization. Our framework supports transparent platform governance through explainable, evidence-based moderation. These observed rhetorical and engagement patterns can inform how platforms and observatories prioritize defenses and intervene before harmful narrative-target combinations achieve widespread reach.", "date": "2026-06-22", "categories": []}
{"id": "2606.23042", "title": "The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel", "abstract": "Most tools for measuring political positions, manifesto coding, expert surveys, text-scaling models, were built and validated on Western party systems, and outside that setting they work poorly, and often not at all. This paper is an attempt at a method for those settings. It treats a large language model not as a measurement device but as a single, fallible rater in a panel, roughly the way an expert survey treats one expert: the value comes from pooling many judges rather than trusting any one of them. I describe the panel, an applicability rule that keeps a score of zero distinct from a blank, and a lens system that separates what an actor says from what it does. I report three results. First, holding a definition-free round fixed, adding written axis definitions moves scores by a mean of 1.8 points on a 21-point scale and tightens agreement between raters (mean absolute gap 2.81 to 2.50; r 0.81 to 0.89); they make two independent raters agree more closely, which an arbitrary steer would not. Second, across nine models from eight laboratories in two countries, Krippendorff's alpha is 0.86 on both an interval and an ordinal metric, and it stayed put as the panel grew from five raters to nine. That is reliability, the reproducibility of a reading, and not validity, its correctness. Third, where the panel does disagree, the disagreement is informative: the sharpest split, a full-scale divergence on an actor's stance toward its state's foundational order, points to a referent problem, and a blind triple-coding puts about two-thirds of it down to interpretation rather than error. I try to be plain about what the method can't do, including the human validation it still lacks, and I release the instrument and data in full. The worked example is the Middle East and North Africa, but I'd expect the method to carry to any region these standard tools leave out.", "date": "2026-06-22", "categories": []}
{"id": "2606.23764", "title": "Emergent Relational Order in LLM Agent Societies: From Collective Affect to Authority Stratification", "abstract": "Fei Xiaotong's Differential Order Pattern characterizes rural society as egocentric and relationally graded, with cooperation attenuating over social distance. Although often treated as culturally specific, its mechanistic basis remains under-operationalized, and prior LLM-based simulations have mainly addressed short-term coordination rather than long-horizon social structure. We propose CAREB-MAS, a multi-agent framework grounded in Affect Control Theory, Social Identity Theory, and Durkheimian collective affect. Agents reason through an emotion-ethics-belief chain and maintain dynamically evolving egocentric identities, while the macro environment specifies only individual production, preference-based allocation, and minimal interaction protocols. Across long-horizon simulations, agents spontaneously reproduce five core Differential Order phenomena: stable labor specialization, guanxi-based economic ethics, relational decay of cooperation, emergent relational authority, and clan-based center-periphery stratification. These patterns shift with production structure from kin-centered integration toward greater functional interdependence. Extensive experiment results support interpreting Differential Order as a structure-sensitive emergent outcome of general social mechanisms, with LLM-based multi-agent simulation providing an interdisciplinary framework for studying social structure and change.", "date": "2026-06-22", "categories": []}
{"id": "2606.24635", "title": "Visualizing \"We the People\": Bridging the Perception Gap through Pluralistic Data Storytelling", "abstract": "Traditional visual data storytelling relies on binary graphics that depict two simplified groups in conflict. This can increase political polarization by oversimplifying intra-group disagreements and erasing ambiguity and shared ideas or values. This can inadvertently foster \"us versus them\" thinking. Intentional, pluralistic design choices for AI-enabled digital platforms can produce visualizations that emphasize nuance, opinion distribution, and intergroup commonalities. To demonstrate this potential, we examine deliberative technologies that map high-dimensional opinion spaces and highlight areas of both consensus and dissensus. The paper highlights the We the People deliberation conducted by Jigsaw and the Napolitan Institute in September 2025, which engaged over 2,400 Americans across all 435 congressional districts in an AI-supported, asynchronous dialogue regarding freedom and equality. By utilizing AI to synthesize long-form, text-based participant inputs into interactive \"opinion landscapes,\" the initiative provided an alternative format for pluralistic data storytelling that humanized diverse viewpoints and revealed hidden areas of substantial broad consensus. The paper concludes that shifting from divisive, contrast-heavy visual frameworks to distribution-focused, interactive models represents a highly scalable, low-cost intervention capable of bridging perceptual gaps and cultivating a more resilient, collaborative democratic culture.", "date": "2026-06-23", "categories": []}
{"id": "2606.26062", "title": "When Certainty Is an Artifact: Keyword Lexicon Blindness and the (Mis)Measurement of Rhetorical Stance", "abstract": "Can a statistically significant, large-effect-size finding in computational social science be entirely an artifact of the measurement instrument? We present a case where the answer appears to be yes. Analyzing 85 interviews across four public intellectuals (2016--2026), we find a robust negative-affect/emphatic-certainty lexical co-occurrence pattern under keyword-based scoring ($r = 0.72$--$0.93$, $p < 0.01$ for all four speakers). Replacing keyword counting with LLM-based zero-shot semantic classification on the complete diarized corpus (32,625 sentences) dramatically reduces this correlation: Dalio's $r = 0.851$ drops to $r = 0.206$, with two speakers showing negative $r(\\text{neg}, \\text{emphatic})$ and one showing null. In contrast, the LLM reveals a strong negative-hedging coupling across speakers -- Rogoff's $r(\\text{neg}, \\text{hedged}) = 0.875$ ($p = 0.001$) and Zeihan's $r(\\text{neg}, \\text{hedged}) = 0.722$ ($p = 0.008$) -- consistent with the conventional expectation that pessimistic discourse attracts hedging, not certainty. Sentence-level error analysis traces this discrepancy to three structural failure modes in keyword lexicons -- syntactic blindness, polysemy blindness, and categorical absence -- illustrated through cases where keyword counting inverts semantic meaning (e.g., ''never absolutely totally confident'' scored as high-certainty). We argue that keyword lexicons measure a universal lexical co-occurrence tendency -- negative discourse naturally attracts emphatic vocabulary -- that is orthogonal to, and can systematically invert, rhetorical stance. Treating keyword counts as measurements of epistemic certainty is a category error: a finding that appears to be about a speaker's psychology may be entirely about the counting of words.", "date": "2026-06-24", "categories": []}
{"id": "2606.26203", "title": "Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols", "abstract": "As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures at scale. We validate it on two contrasting standards for agent interoperability: ERC-8004 (permissionless, on-chain) and Google A2A (corporate-led). Analyzing 4,323 governance participation records, we combine LLM-assisted coding, topic modeling, and multi-layer network analysis to examine how institutional design shapes thematic priorities and community structure. We find that while governance form influences substantive focus, both regimes exhibit comparable levels of participation inequality and community fragmentation. Discourse alignment is denser in the permissionless setting, suggesting that open governance may foster greater thematic convergence despite decentralized participation. These findings illustrate how LLM-assisted methods can advance the empirical study of technology governance, with implications for designing more equitable agentic AI standards. All data and code are openly available.", "date": "2026-06-24", "categories": []}
{"id": "2606.26489", "title": "Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News", "abstract": "News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions has measurable effects on audience engagement and policy support. Automated detection of these framing patterns at the sentence level would allow researchers to analyze large corpora that are infeasible to code manually. We present a systematic comparison of two approaches for classifying sentences from German-language climate news articles as threat-oriented, solution-oriented, both, or neither. The first approach uses few-shot prompting with an open-weights large language model (Llama 4 Maverick), employing chain-of-thought reasoning and structured output with confidence scoring. The second approach fine-tunes a German BERT model (deepset/gbert-large) for sentence-pair classification, where the preceding sentence provides contextual information for the target sentence. Both approaches implement two independent binary classifiers, one for threat framing and one for solution framing. We evaluate both methods on a corpus of 440 Austrian newspaper articles that were manually coded following a detailed coding scheme developed with domain experts. The fine-tuned BERT classifiers achieve an F1 score of 0.83 for both the threat and solution tasks, while the LLM-based classifiers reach an F1 of 0.78. An ablation study confirms that providing the preceding sentence as context improves BERT classification performance substantially compared to single-sentence input. These results contribute to the growing body of work comparing fine-tuned encoder models with prompted generative models for text classification in computational social science.", "date": "2026-06-25", "categories": []}
{"id": "2606.27347", "title": "Mapping Political-Elite Networks in Europe with a Multilingual Joint Entity-Relation Extraction Pipeline", "abstract": "Whether political elites organise into rent-seeking coalitions that capture public resources or civic networks that sustain governance is a central question in comparative politics. Yet observing these complex, informal, and adversarial ties at scale has historically required intensive manual coding, while automated text-as-data methods have largely been limited to simple co-occurrence. Recent large language model (LLM) approaches offer a path forward but often rely on proprietary APIs, lack cross-lingual capability, and struggle with scalable entity resolution. We present a modular, fully open-weight pipeline for multilingual joint entity-relation extraction that builds signed, temporal knowledge graphs from massive unstructured news corpora. It combines span-based named-entity recognition (NER) with a three-stage linking cascade mapping mentions to language-independent Wikidata identifiers; a high-throughput, ontology-constrained mixture-of-experts model then uses guided decoding to extract directed, signed relationships grounded in a domain ontology. A full-coverage spot-check against a 3491-relation gold standard shows high textual correctness (68.2% strict to 93.7% lenient). Two large-scale case studies validate the pipeline against the public record. In Austria, it reconstructs a political party's complete lifecycle, dating internal fractures and tracking personnel into successor factions and court convictions. In a Polish corpus, it uncovers the overlapping economic and governance networks of state-enterprise patronage, alongside the structurally balanced, signed conflict network of the polarized Civic Platform (Platforma Obywatelska, PO)--Law and Justice (Prawo i Sprawiedliwo\\'s\\'c, PiS) duopoly. By bridging raw multilingual text and structured relational data, our framework provides a robust, replicable foundation for cross-national empirical computational social science.", "date": "2026-06-25", "categories": []}
{"id": "2606.28544", "title": "Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction", "abstract": "Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams' lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.", "date": "2026-06-26", "categories": []}
{"id": "2606.30395", "title": "Uncovering Salience-Driven Dynamics in Consumer Confidence with Generative Social Simulation", "abstract": "Consumer confidence is typically modeled as a persistent macroeconomic index, yet its movements arise from households that interpret economic information through heterogeneous constraints, exposures, prior beliefs, and attention. We introduce ConsumerSim, a generative Human--Environment response framework that reconstructs Consumer Confidence Index (CCI) dynamics from a microdata-calibrated synthetic population, time-stamped macroeconomic, financial, policy, and news signals, survey-like response generation, post-stratified belief expansion, and behavioral inertia alignment. Across U.S., EU27, and Japanese official CCI target series, ConsumerSim ranks first among persistence, time-series, regression, and information-augmented baselines on the reported reconstruction metrics, with clear gains around high-salience shocks. Its reconstructed signal also improves short-horizon prediction of real activity, most consistently for housing outcomes. Mechanism analyses show that CCI movements concentrate around salient events; subgroup trajectories often align in direction while differing in magnitude; and signal sensitivity varies across income, homeownership, education, and political-alignment groups. Population-expansion and ablation results indicate that representative aggregation, situational signals, persona heterogeneity, and inertia are necessary for both accuracy and diagnosis. The findings support a behavioral view of consumer confidence as an interpretable Human--Environment response process rather than a purely aggregate time series.", "date": "2026-06-29", "categories": []}
{"id": "2606.30905", "title": "How Human Feedback Shapes AI-generated Community Notes", "abstract": "Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok. Since its introduction, there has been an open question about what role AI could play in scaling and optimizing the system. Recently, X extended its Community Notes system by introducing Collaborative Notes: notes initially drafted by an LLM and iteratively refined based on feedback from human contributors. In this work, we systematically analyze the complete corpus of 19,146 collaborative notes and 211,850 instances of human feedback. First, we develop a taxonomy of human suggestions for improving AI-generated note drafts and find that suggestions involving factual corrections and additional context are most likely to be incorporated, while subjective policy judgments rarely are. Second, we examine changes in helpfulness across versions of collaborative notes and find that human feedback leads to more helpful notes, with the greatest impact coming from suggestions that challenge the main claim in the previous draft, particularly when submitted by more active contributors. Finally, we find that although collaborative notes improve through human feedback, they reach helpful status and are shown on the platform at lower rates than human-only or AI-only notes, with limited human participation emerging as a key bottleneck. Nevertheless, rather than serving as a weaker substitute, collaborative notes tend to play a complementary role, predominantly targeting posts that do not attract human-only or AI-only notes. Our analysis provides an initial description of efforts to use AI to improve crowdsourced content moderation in a real-world moderation system and outlines pathways for future improvements to such features.", "date": "2026-06-29", "categories": []}
{"id": "2606.30957", "title": "Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups", "abstract": "Managing our emotional responses to events is key to emotional well-being, a process referred to as emotion regulation in psychology. Previous work has established that the degree to which we distance events is a type of emotion regulation. When we psychologically distance from events there can be markers in our language. These markers have been referred to as linguistic distancing. We build upon a previous metric to operationalize linguistic distancing, and explore how it changes across the lifespan. We explore this systematically by analyzing large amounts of social media text, a venue where people express their emotions. By investigating how distancing varies across age groups we can better understand how emotion regulation varies with age and provide initial benchmarks on social media data. We provide additional evidence further strengthening the hypothesis that linguistic distancing occurs in proportionally more instances with age. These findings align with past work in psychology which indicate improved well-being with older age. Better understanding how linguistic distancing changes with age is important because it functions as a marker of well-being and can inform effective health interventions. We provide a foundation for further exploring emotion regulation through linguistic distancing in text data.", "date": "2026-06-29", "categories": []}
{"id": "2607.00143", "title": "Hate Speech Detection in Turkish and Arabic Languages: A Comprehensive Study", "abstract": "Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing. Societies grappling with this issue, particularly when hate speech targets specific groups based on religion, race, ethnicity, culture, nationality, or migration status, face the challenge of balancing freedom of expression with the need for effective content moderation on widely used online platforms. In response to this challenge, we introduce a comprehensive hate speech dataset covering five distinct topics in Turkish: refugees, the Israel-Palestine conflict, anti-Greek sentiment in Turkey, ethnic or religious communities (Alevis, Armenians, Arabs, Jews, and Kurds), and LGBTI+, alongside one topic in Arabic (refugees). In addition, we develop state-of-the-art BERT-based models to address multiple dimensions of hate speech analysis, including hate category classification, hate intensity prediction, target identification, and hate speech span detection, enabling a comprehensive understanding of hateful content in online discourse.", "date": "2026-06-30", "categories": []}