license: cc-by-4.0
tags:
- concept-bottleneck-models
- phishing-detection
- nlp
NWeak/emails-user-study
Our model's computed results for the 1000-email user-study set (see
emails/scripts/train_concept_extractor2.ipynb in
user-study-CBMs): per email,
the 6 ground-truth concepts (c1..6_gt), the 6 predicted concept activations
(c1..6_pred, continuous -- tanh of the concept-SVM decision function), the
predicted task label (task_pred), and the ground-truth label (task_gt).
Concept order: Fear+Authority, Urgency, Curiosity, Neutral, Reply, Open attachment.
Also includes Subject/Body/Sender for readability. Join on
Original email No..
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.