A Voat (Reddit alternative, 2013–2020) dataset of 3,010 submissions, 6,188 comments, 1,554 users, 3,394 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/TheGreatAwakening, covering its full lifetime on the platform (2013–2020). Includes all 3,010 submissions and 6,188 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A Voat (Reddit alternative, 2013–2020) dataset of 1,833 submissions, 5,374 comments, 1,060 users, 2,221 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/CalmBeforeTheStorm, covering its full lifetime on the platform (2013–2020). Includes all 1,833 submissions and 5,374 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A CLEF CheckThat! 2024 shared task (arabic) dataset of 7,333 sentences with binary check-worthiness labels (30.6% 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 (arabic) provides 7,333 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 Reddit (state-sponsored troll comments) dataset of 7,097 Reddit comments from verified Russian state-sponsored troll accounts, collected by Zannettou et al. via Reddit API (2019). Each record contains comment body, subreddit, author, score, controversiality, created_utc, gilded, parent_id. 7,097 Reddit comments from verified Russian IRA troll accounts across 649 subreddits — enabling study of coordinated influence operations on Reddit. Suitable for information operations detection, coordinated inauthentic behavior, Reddit manipulation analysis, troll comment classification, cross-platform influence campaign research.
A Rumble (alt-right video platform) dataset of 6,735 Rumble videos from 245 channels (2020-2023), collected by iDRAMA Lab via Rumble API (2024). Each record contains video ID, title, channel name, view/upvote/dislike/comment counts, follower count, video duration, description, named entities, tags, URL. 6,735 Rumble videos with full engagement metrics and NER annotations — one of few open datasets covering Rumble, a major alternative video platform popular with right-wing creators post-YouTube deplatforming. Suitable for alternative video platform research, deplatforming effects, political video discourse, engagement analysis, NER on video metadata.
A Rumble (alt-right video platform) — speech transcriptions dataset of 6,735 lite speech transcriptions from Rumble podcasts (2020-2023), collected by iDRAMA Lab via Whisper ASR (2024). Each record contains video ID, lite transcription segments, named entities (person/org/GPE). 6,735 Rumble podcast transcriptions enabling text-based analysis of spoken political content — rare speech-to-text dataset from an alternative video platform. Suitable for spoken political discourse analysis, NER on speech, alternative platform audio content research, misinformation in video content.
A Twitter dataset of 32 nodes, 496 edges, collected by Yang, Ferrara, and Menczer (OSoMe/Indiana University). Each record contains user network (GML), suspicious tweet text, message cascades, targeted media accounts. Network and content evidence of a coordinated inauthentic influence operation targeting the 2020 US presidential election on Twitter.. Suitable for Coordinated inauthentic behavior detection, influence operations research, election integrity analysis..
A CoAID (COVID-19 misinformation dataset) dataset of 5,975 news articles and social media posts, collected by Cui and Lee via COVID-19 pandemic (2020-2021). Each record contains news articles, fact-check URLs, binary label (0=real, 1=fake), and temporal data. The dataset is notable for its temporally-structured dataset tracking COVID-19 misinformation evolution. Intended for research into COVID-19 misinformation detection, healthcare communication, and temporal social media analysis.
A Twitter dataset of 4,952 tweets (1,238 rumour, 3,714 non-rumour) from the 2013 Boston Marathon bombings, collected by Zubiaga, Liakata, and Procter — Augmented PHEME (Zenodo, 2019). Each record contains tweet text, binary rumour/non-rumour label, timestamp, reaction thread structure linking source tweets to replies. Augmented extension of PHEME covering the Boston Marathon bombings event with both rumour threads and non-rumour threads, providing a balanced benchmark for rumour detection with reaction graph structure. Suitable for rumour detection, misinformation classification, crisis communication analysis, stance detection, conversational thread modelling.
A Twitter (state-sponsored IO accounts) dataset of 3,836 Russian IRA and 770 Iranian state-sponsored troll accounts with profile metadata, collected by Zannettou et al. via Twitter API (2019). Each record contains hashed user ID, display name, screen name, reported location, profile description, follower/following counts, account creation date, account language. Hashed and anonymized profiles of 4,606 verified state-sponsored troll accounts from Russian IRA and Iranian IO campaigns, enabling detection and characterization of coordinated inauthentic behavior. Suitable for information operations detection, state-sponsored troll identification, coordinated inauthentic behavior, influence campaign analysis, social media manipulation research.
Twitter conversations around the Germanwings plane crash in the French Alps (March 2015), from the PHEME rumour dataset (Zubiaga, Liakata, Procter, Kochkina and colleagues; the complete release for rumour detection and veracity classification, figshare 6392078): 469 threads, each a source tweet and its replies, of which 238 were judged rumours by journalists and 231 not. Every rumour also carries its veracity as later established: 94 true, 111 false and 33 unverified. 4,489 tweets in all, with full tweet JSON. ECHO builds the reply network (4,020 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 (spanish) dataset of 3,458 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 (spanish) provides 3,458 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 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 Weibo (CHECKED dataset) dataset of 2,104 microblogs with engagement and expert analysis, collected by Yang, Zhou, and Zafarani via Weibo (2020-2021). Each record contains microblog text, images, videos, engagement metrics (comments, reposts, likes), and expert analysis for fake news. The dataset is notable for its first Chinese COVID-19 fake news dataset with expert analysis, anonymized user IDs, and rich social interaction data. Intended for research into fake news detection, misinformation analysis, Chinese NLP, social network analysis, and public health communication.
A CLEF CheckThat! 2025 Task 2 (portuguese) dataset of 1,735 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 (portuguese) provides 1,735 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 4chan dataset of 1,400 multimodal posts (1,062 hate, 338 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 4chan subset hate speech dataset combining image OCR text and post text with fine-grained category labels across 8 hate dimensions — 76% 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 1,400 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 CLEF CheckThat! 2025 Task 2 (french) dataset of 1,174 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 (french) provides 1,174 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 CLEF CheckThat! 2025 Task 2 (hindi) dataset of 1,081 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 (hindi) provides 1,081 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 CLEF CheckThat! 2024 shared task (dutch) dataset of 995 sentences with binary check-worthiness labels (40.7% 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 (dutch) provides 995 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 321 submissions, 422 comments, 42 users, 63 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/PizzagateNoMod, covering its full lifetime on the platform (2013–2020). Includes all 321 submissions and 422 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
A CLEF CheckThat! 2025 Task 2 (arabic-msa) dataset of 540 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 (arabic-msa) provides 540 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 Weibo dataset of 529 multimodal posts (124 hate, 405 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 Weibo subset hate speech dataset combining image OCR text and post text with fine-grained category labels across 8 hate dimensions — 23% 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 529 meme images (img/) that the records name.
A Twitter/X dataset of 526 multimodal posts (132 hate, 394 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 Twitter subset hate speech dataset combining image OCR text and post text with fine-grained category labels across 8 hate dimensions — 25% 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 526 meme images (img/) that the records name.
A CLEF CheckThat! 2025 Task 2 (arabic) dataset of 470 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 (arabic) provides 470 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 CLEF CheckThat! 2025 Task 2 (punjabi) dataset of 445 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 (punjabi) provides 445 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 CLEF CheckThat! 2025 Task 2 (german) dataset of 386 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 (german) provides 386 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 CLEF CheckThat! 2025 Task 2 (thai) dataset of 244 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 (thai) provides 244 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 CLEF CheckThat! 2025 Task 2 (polish) dataset of 163 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 (polish) provides 163 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.
Wikipedia hoax and non-hoax articles from Kumar, West & Leskovec, 'Disinformation on the Web' (WWW 2016), distributed by SNAP. The README describes 64 hoaxes, each paired with a non-hoax article created the same day with similar appearance; the parsed folders hold those 64 + 64 articles, while the cleaned-markup folder holds 76 hoaxes and 64 non-hoaxes (140 articles). The archive is SNAP's own and also contains 65 macOS '._' resource-fork files, which are not articles. Released under the CRAPL licence (CRAPL-LICENSE.txt is included).
A CLEF CheckThat! 2025 Task 2 (marathi) dataset of 137 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 (marathi) provides 137 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 CLEF CheckThat! 2025 Task 2 (tamil) dataset of 102 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 (tamil) provides 102 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.
Voat
Reddit
Rumble
Twitter (X)
4chan
Weibo
Wikipedia