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 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 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 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 (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 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 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 (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 (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! 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 (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.
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 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 (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 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.
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 NDS-VU signed network repository dataset of 19 nodes and 125 signed edges from a monastery social network, collected by Sampson (1969), distributed by NDS-VU. Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Classic small signed social network from a monastery study by Sampson (1969), widely used as a benchmark for signed community detection and balance theory validation. Suitable for signed network analysis, balance theory, community detection benchmarking.
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 5,072,124 tweets from 1,835 congressional accounts, collected by Alex Litel via Twitter API (2017-2023). Each record contains tweet text, screen name, user ID, timestamp, retweet count, favorite count, source, retweet status. A daily collection of tweets from U.S. Congress members, committees, caucuses and party accounts, one JSON file per day; 11 days have no file in the source repository (for example 2022-01-15, 2022-03-17 and 2022-10-24 to 2022-10-31). metadata/historical-users-filtered.json, from the collection's companion repository, maps each account to its member or office, chamber, party and type. Suitable for political communication, sentiment analysis, election studies, congressional behavior, social media analysis.
A USA state border adjacency (geographic network) dataset of 49 nodes and 107 edges, collected by KONECT. Each record contains directed: False, weighted: False, signed: False. Geographic adjacency network of the 48 contiguous US states — nodes are states, edges connect bordering states.. Suitable for geographic network analysis, graph coloring, planar graph benchmarking.
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 Cora machine learning paper database dataset of 23,166 nodes and 91,500 edges, collected by McCallum et al.. Each record contains directed: True, weighted: False. Classic citation network of machine learning papers — one of the most used benchmarks for graph neural network node classification.. Suitable for graph neural networks, node classification, citation network analysis.
A Cora dataset of 2,708 nodes, 5,278 edges, 7 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. Cora network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.
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 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 10,008 tweets (0 on-topic, 10,008 off-topic), collected by Olteanu et al. via crowdsourcing for the Sandy Hurricane 2012 crisis. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its tweets from the Sandy Hurricane 2012 crisis, labeled by crowdsourcing workers for on-topic/off-topic classification with majority voting from at least 3 workers per tweet. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness.
A Twitter dataset of 10,031 tweets (0 on-topic, 10,031 off-topic), collected by Olteanu et al. via crowdsourcing for the Alberta Floods 2013 crisis. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its tweets from the Alberta Floods 2013 crisis, labeled by crowdsourcing workers for on-topic/off-topic classification with majority voting from at least 3 workers per tweet. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness.
A Twitter dataset of 10,012 tweets (0 on-topic, 10,012 off-topic), collected by Olteanu et al. via crowdsourcing for the Boston Bombings 2013 crisis. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its tweets from the Boston Bombings 2013 crisis, labeled by crowdsourcing workers for on-topic/off-topic classification with majority voting from at least 3 workers per tweet. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness.
A Twitter dataset of 9,992 tweets (0 on-topic, 9,992 off-topic), collected by Olteanu et al. via crowdsourcing for the Oklahoma Tornado Season 2013 crisis. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its tweets from the Oklahoma Tornado Season 2013 crisis, labeled by crowdsourcing workers for on-topic/off-topic classification with majority voting from at least 3 workers per tweet. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness.
A Twitter dataset of 10,033 tweets (0 on-topic, 10,033 off-topic), collected by Olteanu et al. via crowdsourcing for the Queensland Floods 2013 crisis. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its tweets from the Queensland Floods 2013 crisis, labeled by crowdsourcing workers for on-topic/off-topic classification with majority voting from at least 3 workers per tweet. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness.
A Twitter dataset of 10,006 tweets (0 on-topic, 10,006 off-topic), collected by Olteanu et al. via crowdsourcing for the West Texas Explosion 2013 crisis. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its tweets from the West Texas Explosion 2013 crisis, labeled by crowdsourcing workers for on-topic/off-topic classification with majority voting from at least 3 workers per tweet. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness.
A Twitter dataset of 60,082 tweets across 6 crisis events (0 on-topic, 60,082 off-topic), collected by Olteanu et al. via crowdsourcing for 6 crisis events. Each record contains tweet text, on-topic/off-topic label (crowdsourced majority voting), crisis type, country, year. The dataset is notable for its Systematically collected dataset of 6 crisis events with crowdsourced on-topic/off-topic labels from at least 3 workers per tweet, enabling comparative analysis of tweet relevance across different crises. Cumulative version combines all events into a single corpus.. Intended for research into crisis informatics, tweet filtering, on-topic detection, information extraction, crowdsourcing evaluation, event detection, situation awareness, multi-event NLP benchmarking.
A Twitter dataset of 1,200 tweets, collected by Olteanu et al. via Twitter API during the 2012 Colorado wildfires crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Colorado wildfires crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,412 tweets, collected by Olteanu et al. via Twitter API during the 2012 Costa Rica earthquake crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Costa Rica earthquake crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,050 tweets, collected by Olteanu et al. via Twitter API during the 2012 Guatemala earthquake crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Guatemala earthquake crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,000 tweets, collected by Olteanu et al. via Twitter API during the 2012 Italy earthquakes crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Italy earthquakes crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,000 tweets, collected by Olteanu et al. via Twitter API during the 2012 Philipinnes floods crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Philipinnes floods crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,000 tweets, collected by Olteanu et al. via Twitter API during the 2012 Typhoon Pablo crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Typhoon Pablo crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,000 tweets, collected by Olteanu et al. via Twitter API during the 2012 Venezuela refinery crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2012 Venezuela refinery crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
A Twitter dataset of 1,000 tweets, collected by Olteanu et al. via Twitter API during the 2013 Alberta floods crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Alberta floods crisis, collected systematically across 26 events for comparative analysis. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness.
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