A Twitter dataset of 1,000 tweets, collected by Olteanu et al. via Twitter API during the 2013 Manila floods crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Manila 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 2013 NY train crash crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 NY train crash 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 1,200 tweets, collected by Olteanu et al. via Twitter API during the 2013 Queensland floods crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Queensland 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 1,442 tweets, collected by Olteanu et al. via Twitter API during the 2013 Russia meteor crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Russia meteor 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 Sardinia floods crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Sardinia 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 1,250 tweets, collected by Olteanu et al. via Twitter API during the 2013 Savar building collapse crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Savar building collapse 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 Singapore haze crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Singapore haze 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 Spain train crash crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Spain train crash 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 1,048 tweets, collected by Olteanu et al. via Twitter API during the 2013 Typhoon Yolanda crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 Typhoon Yolanda 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 West Texas explosion crisis. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its tweets from the 2013 West Texas explosion 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 27,933 tweets across 26 crisis events (2012-2013), collected by Olteanu et al. via Twitter API during 26 crisis events. Each record contains tweet text, crisis type, country, event name. The dataset is notable for its Systematically collected dataset of 26 crisis events with balanced sampling across event types, enabling comparative analysis of social media communications across different crises. Cumulative version combines all events into a single corpus.. Intended for research into crisis informatics, disaster response, social media analysis, cross-crisis comparison, emergency communication patterns, situation awareness, multi-event NLP benchmarking.
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 1,486 tweets referencing 1,590 images by URL, collected by Alam et al. for the California Wildfires 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the California Wildfires 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 3,992 tweets referencing 4,569 images by URL, collected by Alam et al. for the Hurricane Harvey 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the Hurricane Harvey 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 4,021 tweets referencing 3,611 images by URL, collected by Alam et al. for the Hurricane Irma 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the Hurricane Irma 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 3,995 tweets referencing 4,691 images by URL, collected by Alam et al. for the Hurricane Maria 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the Hurricane Maria 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 496 tweets referencing 597 images by URL, collected by Alam et al. for the Iraq-Iran Earthquake 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the Iraq-Iran Earthquake 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 1,237 tweets referencing 1,380 images by URL, collected by Alam et al. for the Mexico Earthquake 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the Mexico Earthquake 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 830 tweets referencing 1,027 images by URL, collected by Alam et al. for the Southeast Asia Floods 2017 crisis. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its multimodal dataset from the Southeast Asia Floods 2017 crisis, collected from Twitter during the event with full Twitter API metadata. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
A Twitter dataset of 16,057 tweets across 7 crisis events referencing 17,465 images by URL, collected by Alam et al. for 7 major natural disasters in 2017. Each record contains tweet text, engagement metrics (retweets, favorites, replies, quotes), user metadata, media attachments, crisis code. The dataset is notable for its Systematically collected multimodal dataset of 7 natural disasters with full Twitter API metadata. Cumulative version combines all events into a single corpus. Intended for research into crisis informatics, multimodal learning, disaster response, image classification, tweet filtering, cross-event engagement analysis. This copy (the SocialED redistribution) holds the tweet objects only: the images are referenced by URL and the image files are not included, and neither are CrisisMMD's human annotations (informativeness, humanitarian category, damage severity).
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 DBLP Computer Science Bibliography dataset of 5,425,963 nodes and 8,649,016 edges, collected by KONECT via DBLP database. Each record contains directed: False, weighted: False. Large bipartite network linking 1.4M authors to 4M papers in DBLP — one of the largest academic bipartite networks available.. Suitable for academic collaboration analysis, authorship networks, bipartite graph research.
A DBLP Computer Science Bibliography dataset of 12,591 nodes and 49,743 edges, collected by KONECT via DBLP database. Each record contains directed: True, weighted: False. Directed citation network of computer science papers in the DBLP bibliography.. Suitable for citation network analysis, academic influence, community detection.
A DBpedia (structured Wikipedia knowledge base) dataset of 18,268,991 nodes and 136,537,566 edges, collected by DBpedia community via Wikipedia extraction. Each record contains directed: True, weighted: True. Large-scale RDF knowledge graph extracted from Wikipedia — multiple relation types (starring, genre, occupation, team, etc.) across millions of entities.. Suitable for knowledge graph completion, entity linking, semantic web research.
A DBTropes (structured TV Tropes database) dataset of 152,093 nodes and 3,232,134 edges, collected by M. Kiesel et al. via TV Tropes crawl. Each record contains directed: False, weighted: False. Bipartite network of films and narrative tropes they contain from DBTropes — useful for computational narrative analysis.. Suitable for computational narrative analysis, bipartite network analysis, film studies.
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 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 Delicious (social bookmarking) dataset of 536,408 users and 1,366,663 friendship edges, collected by Aneeth Anand via Delicious crawl (December 2010). Each record contains user-user friendship edges and user IDs. The dataset is notable for its friendship network from the Delicious social bookmarking service, where users stored, shared, and discovered web bookmarks. Intended for research into social network analysis, community detection, information sharing behavior, and social bookmarking platform structure studies.
A Delicious social bookmarking platform (tag-item network) dataset of 38,289,740 nodes and 137,240,382 edges, collected by KONECT via Delicious API. Each record contains directed: False, weighted: True, temporal: True. Massive temporal bipartite network of Delicious tags and bookmarked items — 301M edges across 38M tags and items with Unix timestamps.. Suitable for social tagging analysis, folksonomy research, tag-item recommendation.
A Delicious social bookmarking platform (user-item network) dataset of 34,611,302 nodes and 101,798,957 edges, collected by KONECT via Delicious API. Each record contains directed: False, weighted: True, temporal: True. Massive temporal bipartite network of Delicious users and bookmarked URLs — 301M edges across 35M users and items with Unix timestamps.. Suitable for social bookmarking analysis, recommendation systems, large-scale bipartite networks.
A Delicious social bookmarking platform (user-tag network) dataset of 5,345,180 nodes and 81,989,133 edges, collected by KONECT via Delicious API. Each record contains directed: False, weighted: True, temporal: True. Massive temporal bipartite network of Delicious users and tags they used — 301M edges across 5.3M users and tags with Unix timestamps.. Suitable for social tagging analysis, folksonomy research, large-scale bipartite networks.
A Digg (social news) dataset of 771,231 users and 5,911,205 friendship edges, collected by Mithila Nagendra via Digg crawl (December 2010). Each record contains user-user friendship edges and user IDs. The dataset is notable for its friendship network from the pre-v4 Digg social news platform, where users voted stories up (digging) or down (burying). Intended for research into social network analysis, community detection, information diffusion, and social news platform structure studies.
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 Discogs music marketplace and database dataset of 270,786 nodes and 481,661 edges, collected by KONECT via Discogs database. Each record contains directed: False, weighted: True, temporal: False. Bipartite network linking Discogs users to music genres they collect — 4.1M edges across 270k users and 15 genres.. Suitable for recommender systems, music preference analysis, bipartite network analysis.
A Democratic National Committee (email communications, 2016) dataset of 906 nodes and 10,429 edges, collected by WikiLeaks DNC email leak. Each record contains directed: False, weighted: True, signed: False, temporal: False. Weighted undirected co-recipient network from the leaked DNC emails — two people are connected if they appeared together on email threads, with edge weights indicating co-occurrence frequency.. Suitable for political network analysis, email network analysis, community detection.
A Bottlenose dolphin community (Doubtful Sound, New Zealand) dataset of 62 nodes and 159 edges, collected by D. Lusseau et al.. Each record contains directed: False, weighted: False, signed: False. Classic benchmark: 62 bottlenose dolphins with frequent associations — widely used to validate community detection algorithms.. Suitable for community detection benchmark, animal social network analysis.
A Douban (Chinese social network) dataset of 154,908 users and 327,162 friendship edges, collected by Long Qiu via Douban crawl (December 2010). Each record contains user-user friendship edges and user IDs. The dataset is notable for its friendship network from Douban, China's largest online community for book, movie, and music reviews and recommendations. Intended for research into social network analysis, community detection, Chinese social media research, and content-sharing platform structure studies.
A Edinburgh Associative Thesaurus (word association study) dataset of 23,132 nodes and 312,310 edges, collected by G.R. Kiss et al.. Each record contains directed: True, weighted: True. Directed word association network from the Edinburgh Associative Thesaurus — edges connect stimulus words to response words.. Suitable for semantic network analysis, word association modeling, NLP.
A TikTok (pro-eating disorder content) dataset of 85 videos and photo posts annotated using the EDE clinical framework, collected by Shaveet, Viranda, and Choudhury at Cornell University. Each record contains content type (video/photos), human-written content summary, EDE subscale and diagnostic item annotations, visual and audio element labels, audio tempo/valence/arousal scores, harmonic ratio, and mode. EDTT is the first clinically-grounded dataset of pro-eating-disorder TikTok content, with 47 videos and 38 photo posts annotated using the Eating Disorder Examination framework by Cornell researchers. Suitable for eating disorder content detection, harmful content moderation, multimodal annotation research, TikTok platform analysis, clinical NLP, audio-visual content analysis.
A Facebook (ego-networks) dataset of 4,039 nodes and 88,234 edges, collected by McAuley & Leskovec via Facebook app survey (2012). Each record contains user-user friendship edges and ego-network circles. classic ego-network dataset with ground-truth social circles. Suitable for social network analysis, community detection, and circle prediction.
A Twitter (ego-networks) dataset of 81,306 nodes and 1,768,149 edges, collected by McAuley & Leskovec via Twitter public crawl (2012). Each record contains directed user-user follow edges and ego-network circles. The dataset is notable for its directed ego-network dataset with ground-truth social circles. Intended for research into social network analysis, influence propagation, and community detection.
A twitter dataset of ~280 million tweet IDs from the 2016 U.S. presidential election period, collected by Littman, Wrubel, and Kerchner via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. One of the largest election tweet archives, covering the full 2016 campaign including debates, conventions, and election night. Suitable for election discourse, political polarization, misinformation spread, retweet diffusion.
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 European research institution internal email system dataset of 986 employees and 332,334 directed email interactions across 42 departments, collected by Leskovec et al. via SNAP (anonymized institutional email logs). Each record contains sender employee ID, recipient employee ID, Unix timestamp, and department label per employee (42 departments). Temporal directed email communication network from a European research institution with ground-truth department labels for all 986 employees — enabling evaluation of community detection algorithms against known organizational structure.. Suitable for community detection benchmarking, organizational network analysis, temporal email network analysis, higher-order graph clustering.
Twitter (X)
Facebook
Instagram
Reddit
Wikipedia
Bluesky
Mastodon
Delicious
Digg
Discogs
Douban
TikTok