license: cc-by-4.0
tags:
- concept-bottleneck-models
- phishing-detection
- nlp
NWeak/emails-mirror
Preprocessed phishing-email concept-bottleneck data (see emails/scripts/preprocessing.ipynb
in user-study-CBMs): sentence
embeddings (all-MiniLM-L6-v2), the full 19-dim concept ground truth
(concept_gts) and the 6-dim merged/filtered concept ground truth used for
modeling (concept_gts_f -- Fear+Authority combined, plus Urgency, Curiosity,
Neutral, Reply, Open attachment), and the task label (label: 0=Phishing,
1=Valid, 2=Spam).
test is user_study_data.parquet -- the 1000-email class-balanced subset
used for the concept-bottleneck user study (same split train_concept_extractor2.ipynb
evaluates against).
Source dataset and attribution
The emails themselves are not ours. They come from the PhishingSpamDataSet of Toth, Bisztray and Dubniczky, released under CC-BY-4.0 and redistributed here under that licence:
Rebeka Toth, Tamas Bisztray, Richard A. Dubniczky. Constructing and Benchmarking: a Labeled Email Dataset for Text-Based Phishing and Spam Detection Framework. arXiv:2511.21448 | github.com/DataPhish/PhishingSpamDataSet
Theirs: Subject, Body, Sender, URL(s), Type, Created by, Source,
Year, and the LLM detected emotion / LLM detected motivation
annotations. Ours: the filtering and split, the embeddings, the 6-concept
merge, and the trained models' outputs.
Please cite Toth et al. alongside our paper if you use this data.
Note: Sender and Body are real email content carried over from the
source dataset.
Citation
Produced for Are Concept Bottleneck Models Effective as Decision-Support Systems? (arXiv:2608.25581) -- Bogani, Debole, Marconato, Pugnana, Tentori, Passerini. Code: github.com/debryu/user-study-CBMs.