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 twitter dataset of 767,139 tweet IDs from #BlackLivesMatter and #AllLivesMatter (10% Gardenhose sample, Aug 2014–Aug 2015), collected by Gallagher (Northeastern University, 2018). Each record contains tweet IDs only — full content requires hydration. Paired collection of #BLM and #ALM tweets enabling contrastive social movement analysis. Suitable for social movement analysis, racial discourse, political polarization.
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 Twitter (hateful user network) dataset of 100,386 users (544 hateful, 4,427 normal, 0 suspended) with 2,286,592 follower edges, collected by Ribeiro et al. via Twitter API (2018). Each record contains user ID, hate label (hateful/normal/suspended), follower/followee/statuses counts, network centrality metrics, 200+ Empath emotional category scores, 300-dim GloVe embeddings, sentiment, subjectivity, tweet engagement statistics. One of the most feature-rich hateful user datasets, with 100,386 Twitter users labeled for hatefulness and supplemented with full network topology, Empath psychological features, and GloVe embeddings. Suitable for hate speech detection, hateful user classification, network-based toxicity analysis, suspended account detection, social influence of hate, graph-based hate propagation.
A Reddit dataset of 54,075 subreddits and 571,927 directed hyperlinks (510,787 positive sentiment, 61,140 negative sentiment) from post hyperlinks embedded in post titles, collected by Kumar et al. via Reddit API, January 2014 to April 2017, distributed by SNAP. Each record contains directed edges from source to target subreddit with binary sentiment label (+1/-1) and 86-dimension text property vector; post ID and timestamp per edge. captures cross-community interaction and conflict on Reddit with sentiment polarity per hyperlink, enabling study of how communities reference each other positively vs. antagonistically over time. Suitable for community conflict detection, cross-community interaction modeling, signed network analysis, temporal network analysis, and online community dynamics research.
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 Reddit dataset of 35,776 subreddits and 286,561 directed hyperlinks (265,491 positive sentiment, 21,070 negative sentiment) from post hyperlinks embedded in post bodies, collected by Kumar et al. via Reddit API, January 2014 to April 2017, distributed by SNAP. Each record contains directed edges from source to target subreddit with binary sentiment label (+1/-1) and 86-dimension text property vector; post ID and timestamp per edge. captures cross-community interaction and conflict on Reddit with sentiment polarity per hyperlink, enabling study of how communities reference each other positively vs. antagonistically over time. Suitable for community conflict detection, cross-community interaction modeling, signed network analysis, temporal network analysis, and online community dynamics research.
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 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).
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`.
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 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 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).
Meta's Social Connectedness Index (SCI): the relative probability that two Facebook users in two places are friends, scaled so that the most connected pair is 1,000,000,000. This package is the release distributed on HDX, at several resolutions: country to country, GADM level 1 and 2 regions, geoBoundaries ADM1 and ADM2, EU NUTS 1 to 3 (2024), US counties and US ZIP code tabulation areas, with region-to-country lookups and the methodology paper. Suitable for research on social ties, migration, trade and economic spillovers between regions.
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 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 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 tweet IDs from the 2017 Charlottesville protests, collected by Littman via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. Focused crisis dataset from a pivotal far-right rally and counter-protest event. Suitable for crisis informatics, hate speech, political extremism, social media during unrest.
Gab
Twitter (X)
Reddit
Facebook