A MADOC (Reddit, KotakuInAction) dataset of 83,581 users and 1,758,653 reply edges from 6,793,795 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.
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.
A twitter dataset of 957,956 tweet IDs from the #MeToo movement (Q3 2020), collected by Maiorana, Morales Henry, and Weintraub via Social Feed Manager. Each record contains tweet IDs only — full content requires hydration. Quarterly snapshot of #MeToo discourse enabling longitudinal analysis when combined with other quarterly collections. Suitable for social movement analysis, gender studies, harassment detection, discourse trends.
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.
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.
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.
Synthetic, templated data: 75,000 short texts labelled with one of 6 harassment categories or non-toxic (10,714 or 10,715 each), with a 'platform' value (Facebook, Instagram, Reddit, news portal) and character offsets of the toxic span. The texts are generated from a small set of templates: the toxic rows use only 30 distinct toxic phrases, and every row ends in a '(Ref Node: #NNNNNN)' token. The platform values are assigned, not observed. Published on Mendeley Data. Suitable for testing span-tagging code, not for measuring real cyberbullying.
A MADOC (Voat, KotakuInAction) dataset of 1,878 users and 3,854 reply edges from 7,853 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.
A HOT Speech (hate/offensive/toxic speech dataset) dataset of 3,481 social media comments with multi-dimensional annotations, collected by SOMAR / ICPSR via Twitter, YouTube, and Reddit APIs (August 2021). Each record contains comment text, hard and soft labels (hateful, offensive, toxic), composite scores, and 21 researcher codes. multi-platform dataset with rich annotations including both MTurk worker labels and researcher codes. Suitable for hate speech detection, offensive language identification, toxicity classification, and political discourse analysis.
Reddit
Twitter (X)
Voat
Facebook
Instagram
YouTube