Fat shaming

4 datasets tagged as the tag “Fat shaming”.

4 datasets.

MADOC: Reddit / fatpeoplehate Analyzed

A MADOC (Reddit, fatpeoplehate) dataset of 1,409,008 interactions, collected by Dankulov et al. via platform APIs, published at ICWSM 2025. Each record contains user-user reply edges, post and comment text, per-record sentiment scores (VADER and TextBlob), subjectivity scores, and toxicity scores (ToxiGen). The dataset is notable for its FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Intended for research into social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.

1.4M records2025

Voat — v/fatpeoplehate Analyzed

A Voat (Reddit alternative, 2013–2020) dataset of 64,404 submissions, 887,981 comments, 42,319 users, 511,082 collaboration edges, collected by Mekacher & Papasavva (ICWSM 2022) via full platform crawl. Each record contains user nodes (registration date, moderation status), directed weighted edges (commenter→submitter, weight=upvotes+1), submission titles and bodies, comment text, upvotes, downvotes, depth. Complete dataset for the Voat subverse v/fatpeoplehate, covering its full lifetime on the platform (2013–2020). Includes all 64,404 submissions and 887,981 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.

904.5K records42.3K nodes511.1K edges2022

MADOC: Voat / fatpeoplehate Analyzed

A MADOC (Voat, fatpeoplehate) dataset of 20,942 users and 226,749 reply edges from 352,003 interactions, collected by Dankulov et al. via platform APIs, published at ICWSM 2025. Each record contains user-user reply edges, post and comment text, per-record sentiment scores (VADER and TextBlob), subjectivity scores, and toxicity scores (ToxiGen). The dataset is notable for its FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Intended for research into social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.

351.9K records20.9K nodes226.7K edges2025

TBBT: r/fatpeoplehate Ban Intervention (2015) Analyzed

A dataset from The Big Ban Theory (Cerulli et al., ICWSM 2026) on the June 2015 ban of r/fatpeoplehate: 111,751 posts in the subreddit in the three months before the ban, and 39,427 posts in v/fatpeoplehate on Voat, where the community moved, in the three months after it. Each record holds the post ID, text, pseudonymized author, community, score and time. Suitable for deplatforming research, community migration and pre/post intervention analysis.

151.2K records1.5K nodes2K edges2026