Duplicate from bercev/GrokSet

Co-authored-by: Berat <bercev@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,61 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+dehydrated.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,241 @@
+
+---
+license: cc-by-nc-4.0
+language:
+- en
+- es
+- ja
+- pt
+- fr
+- tr
+- ar
+- hi
+- id
+pretty_name: '@GROKSET'
+size_categories:
+- 1M<n<10M
+tags:
+- llm
+- social-media
+- human-llm-interaction
+- multi-party
+- twitter
+- x
+- grok
+- safety-alignment
+- network-analysis
+task_categories:
+- text-classification
+- feature-extraction
+- summarization
+- sentence-similarity
+---
+
+# @GrokSet: Multi-Party Human-LLM Interactions in Social Media
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-2602.21236-b31b1b.svg?style=for-the-badge&logoColor=white)](https://arxiv.org/abs/2602.21236)
+[![Project Page](https://img.shields.io/badge/Project-Page-orange?style=for-the-badge&logo=academia&logoColor=white)](https://mamiglia.github.io/grokset/)
+[![Github](https://img.shields.io/badge/Github-black?style=for-the-badge&logo=github&logoColor=white)](https://github.com/sarahlz01/GrokResearch/)
+
+
+**[Matteo Migliarini](https://mamiglia.github.io/)\* · [Berat Ercevik]()\* · [Oluwagbemike Olowe]() · [Saira Fatima]() · [Sarah Zhao]() · [Minh Anh Le]() · [Vasu Sharma]() · [Ashwinee Panda]()**
+
+*Equal contribution
+
+</div>
+
+## The Dataset
+
+**@GrokSet** is the first large-scale dataset of multi-party human–LLM interactions collected from public social media. Unlike existing corpora (e.g., WildChat, LMSYS-Chat-1M) that capture private, dyadic (one-on-one) user-assistant interactions, @GrokSet captures the **Grok** Large Language Model acting as a public participant in multi-user threads on X (formerly Twitter).
+
+The dataset spans from **March to October 2025**, covering over **1 million tweets** across **182,000+ conversation threads**. It is designed to study the behavior of LLMs in adversarial, socially embedded, and "public square" environments.
+
+This dataset is released in a **dehydrated format** (Tweet IDs + annotations + structural metadata) to comply with platform ToS. A specialized rehydration toolkit, found in [https://github.com/sarahlz01/GrokResearch](https://github.com/sarahlz01/GrokResearch), is provided to reconstruct the dataset's text and metadata.
+
+**Key Features:**
+* **Multi-Party Dynamics:** Captures complex interaction graphs, not just linear queries.
+* **Real-World Context:** Includes engagement metrics (likes, reposts, replies) to measure social validation.
+* **Rich Annotations:** Includes pre-computed labels for **Toxicity** (Detoxify), **Topics** (BERTopic), **Trolling** (LLM-as-a-Judge), and **Network Metrics** (Centrality, Transitivity).
+
+## Dataset Structure
+
+The dataset is structured hierarchically around **Conversation Threads**.
+
+**Total Statistics:**
+* **Threads:** 182,707
+* **Total Tweets:** 1,098,394
+* **Avg. Turns per Thread:** ~6.01
+* **Period:** March 2025 – October 2025.
+
+### Schema Overview
+The JSON structure organizes tweets chronologically within their parent thread.
+
+```json
+{
+  "conversationId": "string (Unique root ID)",
+
+  "annotations": {
+    "topic": "string",
+
+    "trolling": {
+      "is_trolling": "string ('yes'|'no')",
+
+      "trolling_confidence": "int (1-5)",
+      "trolling_intensity": "int (1-5)",
+
+      "topic": "string (fine-grained trolling topic)",
+      "trolling_topic": "string",
+
+      "troll_recognition_type": "string",
+      "troll_recognition_confidence": "int (1-5)",
+      "troll_recognition_explanation": "string",
+
+      "trolling_category_type": "string",
+      "trolling_category_confidence": "int (1-5)",
+      "trolling_category_explanation": "string",
+
+      "response_strategy_type": "string",
+      "response_strategy_confidence": "int (1-5)",
+      "response_strategy_explanation": "string",
+
+      "assistant_tone_type": "string",
+      "assistant_tone_confidence": "int (1-5)",
+      "assistant_tone_explanation": "string",
+
+      "endorsement_type": "string",
+      "endorsement_confidence": "int (1-5)",
+      "endorsement_explanation": "string",
+
+      "amplification_type": "string ('yes'|'no')",
+      "amplification_confidence": "int (1-5)",
+      "amplification_explanation": "string"
+    } | null,
+
+    "discussion": {
+      "is_discussion": "string ('yes'|'no')",
+
+      "discussion_confidence": "int (1-5)",
+      "discussion_intensity": "int (1-5)",
+
+      "discussion_type": "string",
+
+      "topic": "string",
+
+      "bias_language": "string ('yes'|'no')",
+      "bias_examples": "string",
+
+      "bias_confidence": "int",
+      "assistant_bias": "string",
+      "bias_intensity": "int",
+
+      "assistant_stance": "string",
+      "stance_confidence": "int (1-5)",
+      "assistant_stance_bias": "string",
+
+      "user_response_type": "string",
+      "user_response_confidence": "int (1-5)"
+    } | null,
+
+    "network_metrics": {
+      "avg_degree_centrality": "float",
+      "avg_out_degree": "float",
+      "reciprocity": "float",
+      "transitivity": "float",
+      "grok_degree_centrality": "float"
+    }
+  },
+
+  "threads": [
+    {
+      "conversationId": "string",
+      "threadId": "string",
+
+      "hasMissingTweets": "boolean",
+      "truncatedThread": "boolean",
+      "validTweetCount": "int",
+      "deletedTweetCount": "int",
+
+      "tweets": [
+        {
+          "toxicity": {
+            "toxicity_score": "float",
+            "category": "string ('toxicity' | 'obscene' | 'sexual_explicit' | 'insult')"
+          } | null,
+
+          "id": "string",
+          "inReplyToId": "string",
+          "createdAt": "timestamp",
+
+          "lang": "string",
+
+          "text": "string (cleaned text)",
+          "original_text": "string (rehydrated content)",
+
+          "likeCount": "int",
+          "retweetCount": "int",
+          "replyCount": "int",
+          "quoteCount": "int",
+          "viewCount": "int",
+          "bookmarkCount": "int"
+
+          "author": {
+            "isVerified": "boolean",
+            "followers": "int",
+            "following": "int",
+            "isAssistant": "boolean"
+          },
+
+          "entities": {
+            "hashtags": "array",
+            "urls": "array"
+          },
+        }
+      ]
+    }
+  ]
+}
+
+```
+
+_Note: Certain fields (e.g., original_text) are only populated after executing the rehydration pipeline. Additionally, the schema description provided here is not exhaustive; some auxiliary or implementation-specific fields may be omitted for brevity._
+
+## Dataset Creation
+
+### Curation Rationale
+
+As LLMs move from private chatbots to public social agents, we lack data on how they perform in the "wild." @GrokSet was created to fill this gap, offering the first look at an LLM responding to breaking news, political polarization, and multi-user trolling in real-time.
+
+### Annotation Process
+
+The dataset includes extensive machine-generated annotations:
+
+1. **Thematic Analysis:** 1,112 topics identified using **BERTopic** (multilingual).
+2. **Safety/Toxicity:** All tweets were scored using **Detoxify** (multilingual models) to detect obscenity, threats, and hate speech.
+3. **Conversational Dynamics:** All threads were annotated using **Gemini 2.0 Flash** (LLM-as-a-judge) to detect:
+    - _Discussions:_ Valid back-and-forth argumentation.
+    - _Trolling:_ Adversarial user behavior (baiting, sealioning).
+    - _Assistant Tone:_ Whether the model mirrored user hostility.
+
+## Bias, Risks, and Limitations
+
+- **Survivorship Bias:** The dataset only contains tweets that were available at the time of collection. Extremely toxic content removed by X's team prior to collection is missing.
+- **Western-Centric:** While multilingual, the dataset skews heavily towards English and Western political contexts.
+- **Platform Specifics:** The interactions are shaped by X's specific affordances (character limits, "blue check" verification culture) and may not generalize to other platforms.
+
+## Citation
+
+If you use @GROKSET in your research, please cite the following paper:
+
+```
+@article{migliarini2026grokset,
+  title={@GROKSET: Multi-party Human-LLM Interactions in Social Media},
+  author={Migliarini, Matteo and Ercevik, Berat and Olowe, Oluwagbemike and Fatima, Saira and Zhao, Sarah and Le, Minh Anh and Sharma, Vasu and Panda, Ashwinee},
+  journal={arXiv preprint},
+  year={2026}
+}
+```
+
+## LICENSE
+The dataset annotations and structure are licensed under **CC BY-NC 4.0** The tweet content is subject to the [terms of service](https://x.com/en/tos) of X, while the original content within is owned by the original creators. This dataset is provided in a dehydrated format to respect these rights.

dehydrated.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73cd8f70d6991a85155afdcb9b0a16d8a2b2a0025d023edf7c3e08c1369f9c07
+size 1046669097