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 Multiple Platforms dataset of 2,469,836 posts, 26,496,468 responses across 5 platforms, collected by Peixian and Zhang. Each record contains platform, posts, responses, op_gender, responder_gender, sentiment, relevance. Cumulative rtGender dataset combining all platforms for cross-platform gender analysis. Suitable for cross-platform gender analysis, sentiment comparison, response prediction.
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 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 (Voat, funny) dataset of 17,128 users and 82,319 reply edges from 143,945 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 (Voat, technology) dataset of 14,397 users and 41,519 reply edges from 83,185 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 Voat (Reddit alternative, 2013–2020) dataset of 6,901 submissions, 75,310 comments, 15,009 users, 48,640 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/MeanwhileOnReddit, covering its full lifetime on the platform (2013–2020). Includes all 6,901 submissions and 75,310 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
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 YikYak / Reddit dataset of 26,304 posts with engagement and controversy labels, collected by Riana Doctor via YikYak API and Reddit scraping (2025). Each record contains post text, vote total, comment count, controversy flag, timestamp, user alias, group ID. Dataset combining YikYak-style anonymous posts and Reddit content with explicit engagement and controversy annotations, enabling comparative analysis of controversial content across platforms. Suitable for controversy detection, engagement prediction, content moderation, sentiment analysis, social media dynamics.
A MADOC (Voat, gifs) dataset of 5,022 users and 11,435 reply edges from 21,573 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 (Voat, milliondollarextreme) dataset of 3,063 users and 10,819 reply edges from 21,190 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 YouTube (with cross-platform spread on Twitter and Facebook) dataset of 7,659 removed YouTube videos with multilingual titles and descriptions, 249,346 associated tweets, and 18,897,277 Facebook shares, collected by Knuutila (2021) via YouTube Data API and CrowdTangle, tracking videos shared on public social media that were subsequently removed by YouTube for COVID-19 misinformation. Each record contains video titles and descriptions in multiple languages, view counts and subscriber counts where recoverable, Facebook reactions/comments/shares, Twitter and Facebook post ID lists for social spread tracking, removal and publication timestamps, and Wayback Machine archive URLs. The dataset is notable for its one of the few public datasets documenting the complete lifecycle of platform-removed health misinformation, linking video metadata to cross-platform social spread and enabling research into both content and dissemination patterns of COVID-19 misinformation. Intended for research into health misinformation detection, content moderation research, cross-platform information spread analysis, COVID-19 infodemic studies, multilingual misinformation narrative analysis, and platform policy impact assessment.
A MADOC (Voat, MensRights) dataset of 992 users and 1,628 reply edges from 3,735 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 (Voat, CringeAnarchy) dataset of 876 users and 1,740 reply edges from 3,030 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 4chan, Twitter, and Weibo (cross-platform) dataset of 2,455 multimodal posts (1,318 hate, 1,137 non-hate), collected by Mira AI Lab (2024). Each record contains image filename, image OCR text, post text, hate/non-hate binary label, hate category (gender/race/religion/politics/violence/health/etc), and human-annotated reason for classification. Multimodal cross-platform collection hate speech dataset combining image OCR text and post text with fine-grained category labels across 8 hate dimensions — 54% of posts are hate speech with human-annotated reasoning. Suitable for multimodal hate speech detection, cross-platform analysis, hate category classification, image-text hate speech, content moderation research. The package includes the 2,455 meme images (img/) that the records name.
A Cross-platform meme corpus (4chan, Twitter, Weibo) dataset of 1,318 hate-speech labeled meme records from 3 platforms, collected by Mira AI Lab via cross-platform meme collection. Each record contains image filename, OCR-extracted image text, post caption text, hate speech label, category list (politics/race/religion/etc.), and human-readable annotation reason per record. M3-Hate is the hate-speech filtered subset of the M3 meme dataset, with 1,318 hateful memes across 4chan (1062), Twitter (132), and Weibo (124) — multimodal with both image OCR text and post captions. Suitable for hate speech detection, multimodal meme analysis, cross-platform content moderation, image-text classification, harmful content research. The package includes the 1,318 meme images (img/) alongside the JSON records.
A Multiple (Wikipedia, GDELT, Google Trends) dataset of 51 weeks of cross-platform attention data across 6 regions, collected by Luke Steuber via Wikipedia API, GDELT Project, and Google Trends (2020-2025). Each record contains weekly attention scores by region, component metrics (Google Trends, GDELT, Wikipedia), and regional composite scores. A unified, weekly time-series of public attention to the United States from three independent sources, broken down by global region, enabling multi-perspective analysis of attention dynamics. Suitable for information cascade, event detection, public health monitoring, political communication, media analysis, attention tracking, regional sentiment analysis.
Reddit
Facebook
Fitocracy
Voat
Instagram
Yik Yak
Twitter (X)
YouTube
4chan
Weibo
Google
Wikipedia