A MADOC (Reddit, funny) dataset of 3,851,024 users and 21,255,711 reply edges from 61,859,095 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). FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Suitable for social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.
A MADOC (Reddit, pics) dataset of 3,453,264 users and 15,863,415 reply edges from 51,914,975 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). FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Suitable for social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.
A MADOC (Reddit, gaming) dataset of 2,870,130 users and 14,950,380 reply edges from 44,274,130 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, videos) dataset of 1,854,456 users and 8,653,524 reply edges from 36,775,460 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, gifs) dataset of 1,894,135 users and 6,785,726 reply edges from 21,090,022 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, technology) dataset of 611,131 users and 2,052,369 reply edges from 11,766,082 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, 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, CringeAnarchy) dataset of 202,705 users and 1,864,741 reply edges from 6,445,396 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 Reddit dataset of 6,252,711 posts and comments, 6,279 AITA submissions, 6,239,711 nodes, 6,133,386 reply edges, collected by Zenodo contributor via Reddit API, September–October 2023. Each record contains post/comment id, reply graph (parent_id), title, VADER sentiment, readability (Fog + FK-GL), 10 emotion lexicon scores (anticipation, disgust, fear, joy, sadness, surprise, trust, anger, positive, negative), valence/arousal/dominance. 2M+ r/AmITheAsshole posts and comments with pre-computed emotion lexicon scores, readability metrics, and VADER sentiment across multiple time windows (all-time, monthly, weekly, yearly); includes full reply graph structure.. Suitable for Emotion dynamics in online moral judgment, reply network analysis, readability and engagement research, temporal thread growth modeling..
A MADOC (Reddit, MensRights) dataset of 113,904 users and 822,329 reply edges from 3,110,425 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 MADOC (Reddit, milliondollarextreme) dataset of 19,187 users and 422,964 reply edges from 1,286,148 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, greatawakening) dataset of 18,267 users and 302,678 reply edges from 915,265 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 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.
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