A twitter dataset of 2.7B+ tweet IDs across 9+ languages (ongoing collection since Jan 2020), collected by Chen, Lerman, and Ferrara (USC Information Sciences Institute). Each record contains tweet IDs only — full content requires hydration. Largest ongoing COVID-19 Twitter collection with multilingual coverage and continuous updates since pandemic onset. Suitable for pandemic discourse, public health surveillance, infodemic analysis, multilingual NLP.
A twitter dataset of 295,670,111 tweet IDs from the 2020 U.S. presidential election, collected by Wrubel and Kerchner via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. Complete tweet archive from the 2020 election including post-election period and January 6 lead-up. Suitable for election discourse, misinformation, political polarization, post-election unrest.
A Parler (alternative social network) dataset of ~183,000,000 posts from Parler's full history before deplatforming, collected by Aliapoulios et al. via Parler API (2021). Each record contains post body text, username, timestamp, hashtags, upvotes, reposts, impressions, comment count, sensitive flag, media attachments, reply depth, parent post ID. Complete Parler post archive from before the platform's deplatforming — includes Jan 6 Capitol riot content, far-right discourse, and cross-posts from Twitter. Suitable for deplatforming research, far-right discourse analysis, January 6th research, hate speech detection, alternative platform behavior, content moderation effects.
A 4chan /pol/ (Politically Incorrect board) dataset of 134,529,233 posts across 3,397,911 threads (4,894,179 with 7-score Perspective API annotations), collected by Papasavva et al. via 4chan API + Perspective API (2020). Each record contains post text (com), thread ID, post number, country, timestamp, image metadata, 7 Perspective API scores (TOXICITY, SEVERE_TOXICITY, INFLAMMATORY, PROFANITY, INSULT, OBSCENE, SPAM), named entities. 134,529,233 4chan /pol/ posts with per-post Perspective API toxicity scores across 7 dimensions — one of the most comprehensively annotated fringe platform datasets covering 3.5 years of /pol/ discourse. Suitable for toxicity measurement, hate speech analysis, fringe platform research, political discourse on imageboards, cross-platform toxicity comparison, longitudinal hate speech trends.
A Telegram (German-language conspiracy channels) dataset of 63,908,365 messages from 4,404 unique channels, collected by Hagen, Wiencierz, and Freudenthaler via Telegram API. Each record contains message UUID, channel name, author, reply structure, conspiracy label, language, media type (photo/video/voice), audio/video duration, forwarding metadata, duplicate flag. Schwurbelarchiv is a large-scale German Telegram corpus with 3.1% of messages labeled as conspiracy content, 23.8% reply edges enabling thread analysis, and rich media metadata across 4,404 channels. Suitable for conspiracy theory detection, German NLP, social network analysis, Telegram platform research, misinformation propagation, reply network analysis, media sharing patterns.
A Scored.co (alternative Reddit platform) dataset of 56,815,584 total posts and comments (6,293,980 submissions + 50,521,604 comments) from 964 communities, 2020-2023, collected by iDRAMA Lab via Scored.co API (2024). Each record contains post/comment text, author, community, score up/down, NSFW/image/video/deleted/twitter-crosspost flags, post flair, date, UUID. iDRAMA-Scored covers 6,293,980 submissions from Scored.co including banned communities like TheDonald and GreatAwakening that migrated from Reddit after deplatforming — 964 distinct communities, 131,691 unique authors. Suitable for deplatforming research, community migration, hate speech detection, alternative platform analysis, far-right discourse, content moderation effects.
A Twitter/X dataset of 42,014,825 tweets from the 2024 US presidential election period across 39 data parts with full engagement metadata (41,561,521 records with engagement metrics), collected by USC Information Sciences Institute via Twitter/X API during the 2024 US election period. Each record contains tweet text, engagement metrics (reply count, retweet count, like count, quote count, view count), language, media attachments, reply chain metadata, user location, and publication timestamp. The dataset is notable for its one of the largest publicly available datasets from the 2024 US presidential election, with full engagement metadata per tweet enabling study of information amplification and political discourse dynamics. Intended for research into political opinion mining, election discourse analysis, misinformation detection, engagement prediction, and temporal analysis of political social media.
A Gab (alt-right social network) dataset of 22,112,812 Gab posts from January 2018 (13,871,421 original posts, 8,241,391 reposts), collected by Zannettou et al. via Gab API (2018). Each record contains post body text, username, verified flag, published_at, post type (post/repost), topic category, language, likes/reposts/replies counts, sentiment score, isPopular. 22,112,812 Gab posts from January 2018 — one of the earliest large-scale Gab datasets, capturing the platform during its growth phase as an alt-right Twitter alternative. Suitable for alt-right discourse analysis, fringe platform research, meme propagation, hate speech detection, cross-platform information flow, deplatforming research.
A Parler (alternative social network) dataset of 13,254,060 Parler user profiles with bios and activity metrics, collected by Aliapoulios et al. via Parler API (2021). Each record contains user ID, username, bio text, follower/following counts, post/comment/like/media counts, join date, verified/banned/human flags. 13,254,060 Parler user profiles from the platform's peak period before its deplatforming — 605 verified accounts, 287,391 banned users. Suitable for deplatforming research, far-right social network analysis, user behavior on alternative platforms, bot detection, political communication research.
A NELA-PS (pink slime local news) dataset of 7,910,965 articles from 1,093 pink slime news sources, collected by Horne and Gruppi, published via Harvard Dataverse (2024). Each record contains full article text, headline, source outlet name, publication date, author, location, network, and URL. The dataset is notable for its largest available dataset of pink slime news — low-quality partisan content systematically framed as authentic local news, spanning 1,093 outlets over 2.5 years with stratified cross-outlet topic modeling. Intended for research into misinformation detection, local news ecosystem analysis, partisan content identification, and pink slime outlet characterisation.
A Scored.co (c/GreatAwakening) dataset of 499,023 submissions and 5,662,346 comments from 15,594 unique authors in c/GreatAwakening (2020-2023), collected by iDRAMA Lab via Scored.co API (2024). Each record contains post/comment text, author, score up/down, NSFW/deleted/twitter-crosspost flags, date, UUID. c/GreatAwakening on Scored.co — QAnon conspiracy community, migrated from Reddit r/greatawakening. 6,161,369 total posts from 15,594 authors over 4 years. Suitable for deplatforming research, community behavior analysis, alternative platform discourse, content moderation effects, longitudinal community evolution.
A 4chan (/pol/) dataset of 4,998,945 posts from /pol/, collected by kjj0 via 4chan API. Each record contains post text content. A 5 million post sample from 4chan's /pol/ board, capturing political discourse, extremism, and online communities. Suitable for misinformation, propaganda, hate speech, political communication, online communities, extremist discourse.
Posts from v/QRV, the QAnon research subverse on Voat, 2018-2020, from the Voat archive of Mekacher & Papasavva (ICWSM 2022): 187,158 submissions and 2,229,684 comments with their text, votes and dates. The archive did not recover the author of almost any v/QRV post (it writes a placeholder instead), so this entry has no user or reply graph. Suitable for QAnon and conspiracy discourse, topic modelling and moderation research.
A Reddit (r/greatawakening ban intervention, 2018) dataset of 329,183 pre-ban posts from banned community, 690,552 pre-ban control posts, 620,307 post-ban control posts, collected by Cerulli et al. via Reddit API (ICWSM 2026). Each record contains comment ID, selftext (post body), author (pseudonymized), subreddit, score, created_utc, split label (in-before/out-before/out-after). TBBT r/greatawakening: pre- and post-ban Reddit dataset from The Big Ban Theory — standardized 3-month window around the 2018 moderation intervention with matched control communities. Suitable for content moderation effects, community migration, deplatforming research, pre/post intervention analysis, Reddit ban studies.
A Twitter (#NoMore hashtag campaign) dataset of 1,048,574 tweets from the Ethiopian #NoMore movement (2021-11 to 2022-08), collected by Zenodo contributors via Twitter API. Each record contains tweet text, language, timestamp; Amharic+English bilingual subset also available. 1,048,574 tweets covering the #NoMore solidarity movement in Ethiopia and Eritrea — one of few African social movement datasets with Amharic content, spanning 2021-11 to 2022-08. Suitable for African social movement analysis, multilingual NLP, Amharic text processing, political communication research, hashtag activism, Global South social media studies.
A Fakeddit (multimodal fake news benchmark) dataset of 965,647 Reddit submissions with text, images and metadata, collected by Nakamura et al. via the Reddit API (2020). Each record holds the post title, text, image URL, comment count, score, timestamp and 2-, 3- and 6-way veracity labels (6-way: true, satire/parody, misleading content, imposter content, false connection, manipulated content). Suitable for fake news detection, multimodal learning and misinformation classification.
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 Voat (Reddit alternative, 2013–2020) dataset of 103,156 submissions, 844,868 comments, 15,037 users, 280,241 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/GreatAwakening, covering its full lifetime on the platform (2013–2020). Includes all 103,156 submissions and 844,868 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A Scored.co (c/Conspiracies) dataset of 67,774 submissions and 471,390 comments from 3,940 unique authors in c/Conspiracies (2020-2023), collected by iDRAMA Lab via Scored.co API (2024). Each record contains post/comment text, author, score up/down, NSFW/deleted/twitter-crosspost flags, date, UUID. c/Conspiracies on Scored.co — General conspiracy theory discussion community. 539,164 total posts from 3,940 authors over 4 years. Suitable for deplatforming research, community behavior analysis, alternative platform discourse, content moderation effects, longitudinal community evolution.
A 4chan (/pol/) dataset of 499,723 posts with moderation scores, collected by kjj0 via 4chan API and OpenAI moderation model. Each record contains post text, sexual score, hate score, violence score, self-harm score, sexual/minors score, hate/threatening score, violence/graphic score. A dataset of 500,000 /pol/ posts with OpenAI moderation scores, enabling research on content moderation, hate speech detection, and harmful content analysis. Suitable for content moderation, hate speech detection, toxicity analysis, misinformation, political communication.
A Voat (Reddit alternative, 2013–2020) dataset of 54,170 submissions, 382,422 comments, 11,439 users, 130,857 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/theawakening, covering its full lifetime on the platform (2013–2020). Includes all 54,170 submissions and 382,422 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A Twitter dataset of 404,615 nodes, 434,963 edges, collected by Pierri et al. (OSoMe/Indiana University + Politecnico di Milano), 2020. Each record contains retweet network (uid1, uid2), user-domain bipartite network, user co-sharing network. Twitter news-sharing network revealing information polluters through retweet and co-sharing patterns.. Suitable for Information polluter identification, news sharing network analysis, source credibility..
A Voat (Reddit alternative, 2013–2020) dataset of 36,749 submissions, 341,207 comments, 16,507 users, 152,119 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/pizzagate, covering its full lifetime on the platform (2013–2020). Includes all 36,749 submissions and 341,207 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A Twitter dataset of 346,573 nodes, 1,091,552 edges, collected by Shao et al. via Hoaxy API (OSoMe/Indiana University). Each record contains from_user_id, to_user_id, tweet_id, tweet_created_at, site_type (claim/fact-checking). Retweet diffusion network of misinformation and fact-checking content surrounding the 2016 US election; distinguishes claim vs. fact-checking retweets.. Suitable for Misinformation diffusion, fact-checking network analysis, network epidemiology..
A MADOC (Voat, greatawakening) dataset of 11,863 users and 155,890 reply edges from 324,835 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 Scored.co (c/NoNewNormal) dataset of 30,770 submissions and 291,530 comments from 3,101 unique authors in c/NoNewNormal (2020-2023), collected by iDRAMA Lab via Scored.co API (2024). Each record contains post/comment text, author, score up/down, NSFW/deleted/twitter-crosspost flags, date, UUID. c/NoNewNormal on Scored.co — Anti-lockdown / COVID skepticism community. 322,300 total posts from 3,101 authors over 4 years. Suitable for deplatforming research, community behavior analysis, alternative platform discourse, content moderation effects, longitudinal community evolution.
A Telegram (86 disinformation/conspiracy channels) dataset of 279,682 cleaned messages across 83 channels, collected by Federation of American Scientists via Telegram API. Each record contains message text (cleaned), channel name, timestamp, extracted URL, domain, and detected language per message. SMIDGE is a curated Telegram corpus spanning 2023-09-25 to 2024-09-24, focused on 83 channels selected for disinformation and conspiracy content, with 5,334 unique domains linked. Suitable for disinformation detection, conspiracy theory analysis, NLP text classification, content moderation, cross-platform information spread research.
A Bluesky (2024 presidential debate coverage) dataset of 279,249 bluesky posts from June 25 - July 2, 2024, collected by Zenodo contributors via public API. Each record contains post text, user ID, post ID, creation date, hashtags, mentioned users, URLs, post type (post/reply/repost), linked post, root post, language. 279,249 bluesky posts covering the 2024 Biden-Trump presidential debate with pre-computed sentence embeddings. Suitable for political communication, debate analysis, cross-platform comparison, decentralized social media research, NLP with embeddings.
A Twitter dataset of 263,555 tweets with binary event labels (14,129 disruptive events, 248,499 non-events) related to Bitcoin and cryptocurrency markets, collected by Mehta et al. via Twitter API, 2021. Each record contains tweet text, binary event/non-event label, follower count, location, username, Bitcoin price at time of tweet, and retweet count. one of few datasets explicitly linking social media content to financial market disruption events, with synchronized Bitcoin price data enabling direct study of social signal → market movement relationships. Suitable for event detection, financial market prediction from social media, cryptocurrency sentiment analysis, and disruptive event classification.
A X (Twitter) with Community Notes annotations dataset of 152,266 multimodal posts (21,845 AI-generated, 104,915 miscaptioned), collected by Chrysidis, Papadopoulos & Papadopoulos via X Community Notes (2026). Each record contains tweet ID, reply/favorite/view/retweet counts, Community Notes text and timestamp, and misinformation type label (miscaptioned / ai_generated / other). Large-scale dataset of 152,266 multimodal X posts with Community Notes — tracking the rise of AI-generated misinformation with 21,845 confirmed AI-generated posts. Includes engagement metrics showing that AI-generated content achieves disproportionate virality despite slower initial reporting.. Suitable for AI-generated content detection, misinformation classification, Community Notes effectiveness, multimodal misinformation analysis, virality and engagement modeling, temporal degradation of detectors.
A ReCOVery (news credibility repository) dataset of 2,029 news articles with reliability labels and social media engagement, collected by Zhou, Mulay, Ferrara, and Zafarani via news websites and Twitter (2020). Each record contains news article title and body, reliability label (1=reliable, 0=unreliable), publisher, political bias, country, author, and social media engagement data. The dataset is notable for its multimodal repository combining news content with social media engagement, designed for COVID-19 news credibility research. Intended for research into fake news detection, credibility assessment, political bias analysis, and multimodal misinformation research.
A Voat (Reddit alternative, 2013–2020) dataset of 25,989 submissions, 94,638 comments, 10,272 users, 46,388 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/Conspiracy, covering its full lifetime on the platform (2013–2020). Includes all 25,989 submissions and 94,638 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A Truth Social (user account corpus) dataset of 90,683 user accounts with 1,611 verified and 0 bot-labeled, collected by TruthStance project team via Truth Social API. Each record contains username, follower/following counts, account creation date, profile bio text, bot label, verified status. TruthStance covers 90,683 Truth Social accounts spanning 2022-02 to 2025-12, with 1,611 verified accounts and 61,226 users with bio text — one of few datasets focused on Truth Social platform users. Suitable for Truth Social platform analysis, bot detection, political social media research, user behavior modeling, alternative platform studies.
A News Articles (multiple sources) dataset of 85,917 articles (43,610 fake, 34,941 true, 7,366 russian propaganda), collected by Steven Peutz via Kaggle (2022); Russian propaganda subset sourced from the EUvsDisinfo Project (2015-present). Each record contains full article text and veracity label (fake, true, or russian_propaganda). combination of general fake/true news articles with a dedicated Russian propaganda subset from the EUvsDisinfo database, enabling comparative analysis across disinformation types. Suitable for fake news detection, misinformation topic modeling, propaganda characterization, and classification benchmarking.
A Mastodon (2024 presidential debate coverage) dataset of 73,789 mastodon posts from June 25 - July 2, 2024, collected by Zenodo contributors via public API. Each record contains post text, user ID, post ID, creation date, hashtags, mentioned users, URLs, post type (post/reply/repost), linked post, root post, language. 73,789 mastodon posts covering the 2024 Biden-Trump presidential debate with pre-computed sentence embeddings. Suitable for political communication, debate analysis, cross-platform comparison, decentralized social media research, NLP with embeddings.
A ISOT (fake news dataset) dataset of 44,898 news articles labeled fake/real, collected by ISOT via Reuters and various sources (2017). Each record contains news article text (title + content) and binary label (is_fake: 1=fake, 0=real). The dataset is notable for its well-balanced dataset of fake and real news articles for misinformation research. Intended for research into fake news detection, text classification, and misinformation analysis.
A Twitter dataset of 38,918 users and 32,523 directed, weighted reply edges (36,902 replies in all), collected by De Domenico et al. via the Twitter Streaming API around the Higgs boson announcement, 1-7 July 2012. Each record holds the replying user ID, the replied-to user ID and the number of replies between them. Complements the Higgs retweet and mention networks from the same collection. Suitable for information diffusion, scientific communication on social media and reply network structure.
Twitter conversations around the Charlie Hebdo shooting in Paris (January 2015), from the PHEME rumour dataset (Zubiaga, Liakata, Procter, Kochkina and colleagues; the complete release for rumour detection and veracity classification, figshare 6392078): 2,079 threads, each a source tweet and its replies, of which 458 were judged rumours by journalists and 1,621 not. Every rumour also carries its veracity as later established: 193 true, 116 false and 149 unverified. 38,268 tweets in all, with full tweet JSON. ECHO builds the reply network (36,189 reply links, each reply pointing to the tweet it answers) and labels each tweet with its thread's rumour status and, for source tweets, the veracity. Useful for rumour detection, rumour verification and modelling how conversations unfold. Annotations CC BY 4.0; Twitter retains the rights to tweet content. The archive PHEME_veracity.tar.bz2 is gzip-compressed despite its name (the name is the authors' own, kept so the checksum matches figshare): extract it with `tar xzf`.
A Twitter dataset of 25,327 nodes, 629,514 edges, collected by Shao et al. via Hoaxy + Botometer (OSoMe/Indiana University). Each record contains user centralities (betweenness, pagerank), bot scores, retweet graph, user-domain bipartite graph. Retweet network of low-credibility content with per-user bot scores; shows bots systematically amplify misinformation early in diffusion cascades.. Suitable for Bot-mediated misinformation diffusion, social bot impact measurement..
Twitter conversations around the Ferguson unrest after the shooting of Michael Brown (August 2014), from the PHEME rumour dataset (Zubiaga, Liakata, Procter, Kochkina and colleagues; the complete release for rumour detection and veracity classification, figshare 6392078): 1,143 threads, each a source tweet and its replies, of which 284 were judged rumours by journalists and 859 not. Every rumour also carries its veracity as later established: 10 true, 8 false and 266 unverified. 24,175 tweets in all, with full tweet JSON. ECHO builds the reply network (22,260 reply links, each reply pointing to the tweet it answers) and labels each tweet with its thread's rumour status and, for source tweets, the veracity. Useful for rumour detection, rumour verification and modelling how conversations unfold. Annotations CC BY 4.0; Twitter retains the rights to tweet content. The archive PHEME_veracity.tar.bz2 is gzip-compressed despite its name (the name is the authors' own, kept so the checksum matches figshare): extract it with `tar xzf`.
Twitter conversations around the Sydney Lindt café siege (December 2014), from the PHEME rumour dataset (Zubiaga, Liakata, Procter, Kochkina and colleagues; the complete release for rumour detection and veracity classification, figshare 6392078): 1,221 threads, each a source tweet and its replies, of which 522 were judged rumours by journalists and 699 not. Every rumour also carries its veracity as later established: 382 true, 86 false and 54 unverified. 23,996 tweets in all, with full tweet JSON. ECHO builds the reply network (22,775 reply links, each reply pointing to the tweet it answers) and labels each tweet with its thread's rumour status and, for source tweets, the veracity. Useful for rumour detection, rumour verification and modelling how conversations unfold. Annotations CC BY 4.0; Twitter retains the rights to tweet content. The archive PHEME_veracity.tar.bz2 is gzip-compressed despite its name (the name is the authors' own, kept so the checksum matches figshare): extract it with `tar xzf`.
A FakeNewsNet (fake news with social context) dataset of 23,196 news articles labeled fake/real with social media context, collected by Shu et al. via Politifact and GossipCop (2019). Each record contains news title, article text, binary label (0=real, 1=fake), source (politifact/gossipcop), publish date, and URL. The dataset is notable for its dataset with social context and spatiotemporal information for studying fake news propagation. Intended for research into fake news detection, social media analysis, misinformation propagation, and multimodal research.
A CLEF CheckThat! 2024 shared task (english) dataset of 22,501 sentences with binary check-worthiness labels (24.1% check-worthy), collected by CLEF CheckThat! Lab organizers (2024). Each record contains sentence text, binary check-worthiness label (Yes/No), sentence ID, source document. CLEF 2024 CheckThat! Task 1 (english) provides 22,501 sentences from political debates and social media annotated for check-worthiness — whether the claim merits fact-checking. Suitable for check-worthiness detection, fact-checking automation, misinformation research, NLP classification benchmarks, political discourse analysis.
A CLEF CheckThat! 2024 shared task (spanish) dataset of 19,948 sentences with binary check-worthiness labels (15.7% check-worthy), collected by CLEF CheckThat! Lab organizers (2024). Each record contains sentence text, binary check-worthiness label (Yes/No), sentence ID, source document. CLEF 2024 CheckThat! Task 1 (spanish) provides 19,948 sentences from political debates and social media annotated for check-worthiness — whether the claim merits fact-checking. Suitable for check-worthiness detection, fact-checking automation, misinformation research, NLP classification benchmarks, political discourse analysis.
A Voat (Reddit alternative, 2013–2020) dataset of 4,041 submissions, 11,284 comments, 1,882 users, 4,527 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/pizzagatewhatever, covering its full lifetime on the platform (2013–2020). Includes all 4,041 submissions and 11,284 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
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 LIAR (fact-checking benchmark) dataset of 12,789 labeled claim statements from Politifact, collected by Wang via Politifact fact-checking (2017). Each record contains claim text, 6-class veracity label, speaker metadata (party, state, job title), credit history counts (barely_true/false/half_true/mostly_true/pants_fire), and context. The dataset is notable for its benchmark dataset for fake news detection with fine-grained 6-class labels, rich speaker metadata, and credit history. Intended for research into fake news detection, fact-checking, political discourse analysis, and misinformation research.
Twitter conversations around the Ottawa Parliament Hill shooting (October 2014), from the PHEME rumour dataset (Zubiaga, Liakata, Procter, Kochkina and colleagues; the complete release for rumour detection and veracity classification, figshare 6392078): 890 threads, each a source tweet and its replies, of which 470 were judged rumours by journalists and 420 not. Every rumour also carries its veracity as later established: 329 true, 72 false and 69 unverified. 12,284 tweets in all, with full tweet JSON. ECHO builds the reply network (11,394 reply links, each reply pointing to the tweet it answers) and labels each tweet with its thread's rumour status and, for source tweets, the veracity. Useful for rumour detection, rumour verification and modelling how conversations unfold. Annotations CC BY 4.0; Twitter retains the rights to tweet content. The archive PHEME_veracity.tar.bz2 is gzip-compressed despite its name (the name is the authors' own, kept so the checksum matches figshare): extract it with `tar xzf`.
A CLEF CheckThat! 2025 Task 2 (english) dataset of 11,374 social media post and normalized claim pairs, collected by CLEF CheckThat! Lab organizers (2025). Each record contains social media post text containing a factual claim, normalized canonical claim statement. CLEF 2025 CheckThat! Task 2 (english) provides 11,374 pairs of social media posts and their normalized factual claims, enabling claim normalization and canonicalization research. Suitable for claim normalization, fact-checking automation, NLP text generation, multilingual claim extraction, social media fact verification.
A News Articles (multiple sources) dataset of 9,900 news articles (5,000 fake, 4,900 real), collected by Vishakh Dapat via Kaggle (2023). Each record contains full article text and binary veracity label (Fake or Real). The dataset is notable for its balanced binary-labeled news corpus with approximately equal proportions of fake and real articles, making it well-suited as a classification benchmark. Intended for research into fake news detection, text classification benchmarking, misinformation topic modeling, and NLP feature engineering.
Twitter (X)
Parler
4chan
Telegram
Scored
Gab
Voat
Reddit
Bluesky
Truth Social
Mastodon
Facebook
YouTube