A Twitter (2010 snapshot) dataset of 41,652,230 users and 1,468,365,182 directed follower edges, collected by Kwak, Lee, Park & Moon via Twitter API (2010). Each record contains source user ID, follower user ID (edge from i to j means j follows i). Complete snapshot of the Twitter follower graph from 2010 — one of the largest social network datasets ever released publicly, capturing the full follower structure of 41.6M Twitter users with 1.47 billion directed edges.. Suitable for large-scale social network analysis, community detection, influence propagation, graph algorithm benchmarking, Twitter network evolution studies.
A Twitter dataset of 11,316,811 users and 85,331,843 directed follower/followee edges, collected by Zafarani and Liu at Syracuse University Data Lab. Each record contains directed edges from follower to followee; bare integer node IDs (anonymized users). one of the largest publicly available Twitter follower networks, with 85M directed edges enabling large-scale social network analysis at the full platform scale. Suitable for large-scale network analysis, influence propagation, community detection, centrality computation, and information diffusion modeling.
The five Twitter multiplex event networks (Cannes 2013, MLK Day 2013, Moscow Athletics 2013, NY Climate March 2014, Obama in Israel 2013) side by side in one graph: 3,238,746 users and 5,175,900 weighted retweet, mention and reply edges. Each release numbers its users independently, so node IDs are event:user and no account is linked across events. Suitable for multiplex network analysis and comparing information spreading across events.
A Twitter (Obama in Israel 2013) dataset of 2,281,259 users, 4,061,960 multiplex edges (retweets, mentions, replies), collected by D. R. G. and D. M.. Each record contains source, target, weight, layer (1=retweet, 2=mention, 3=reply). Multiplex Twitter network during Obama in Israel 2013 with three interaction layers. Suitable for multiplex network analysis, event dynamics, information propagation.
A Twitter (Cannes Film Festival 2013) dataset of 438,537 users, 991,854 multiplex edges (retweets, mentions, replies), collected by D. R. G. and D. M.. Each record contains source, target, weight, layer (1=retweet, 2=mention, 3=reply). Multiplex Twitter network during Cannes Film Festival 2013 with three interaction layers. Suitable for multiplex network analysis, event dynamics, information propagation.
A Twitter (user-hashtag interactions) dataset of 705,632 nodes and 1,890,661 edges, collected by Munmun De Choudhury et al. via Twitter API. Each record contains directed: False, weighted: True, signed: False, temporal: True. Bipartite temporal network linking Twitter users to hashtags they used, with Unix timestamps. Useful for studying hashtag adoption, information diffusion, and user interest modeling.. Suitable for hashtag analysis, bipartite network analysis, information diffusion.
A Twitter (following network sample) dataset of 465,017 nodes and 834,797 edges, collected by Munmun De Choudhury et al. via Twitter API. Each record contains directed: True, weighted: False, signed: False, temporal: False. Large directed Twitter following network of 465k users and 835k edges, collected via API sampling. Unweighted directed graph suitable for large-scale social network analysis.. Suitable for Twitter network analysis, community detection, influence propagation.
A Twitter dataset of 456,631 users, 14,855,875 follow/mention edges, collected by De Domenico et al. (2013) via Twitter API around Higgs boson announcement (July 4, 2012). Each record contains directed edges between Twitter users representing follows or mentions, capturing information propagation during a major scientific announcement. Temporal network capturing information spreading around the Higgs boson discovery announcement, enabling study of scientific rumor propagation and information cascades in real-time social media. Suitable for information propagation, rumor dynamics, temporal network analysis, scientific communication studies, social media epidemiology.
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 Twitter (MLK Day 2013) dataset of 327,707 users, 396,671 multiplex edges (retweets, mentions, replies), collected by D. R. G. and D. M.. Each record contains source, target, weight, layer (1=retweet, 2=mention, 3=reply). Multiplex Twitter network during MLK Day 2013 with three interaction layers. Suitable for multiplex network analysis, event dynamics, information propagation.
A Twitter (NY Climate March 2014) dataset of 102,439 users, 353,495 multiplex edges (retweets, mentions, replies), collected by D. R. G. and D. M.. Each record contains source, target, weight, layer (1=retweet, 2=mention, 3=reply). Multiplex Twitter network during NY Climate March 2014 with three interaction layers. Suitable for multiplex network analysis, event dynamics, information propagation.
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 (Moscow Athletics 2013) dataset of 88,804 users, 210,250 multiplex edges (retweets, mentions, replies), collected by D. R. G. and D. M.. Each record contains source, target, weight, layer (1=retweet, 2=mention, 3=reply). Multiplex Twitter network during Moscow Athletics 2013 with three interaction layers. Suitable for multiplex network analysis, event dynamics, information propagation.
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 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 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.
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..
A Twitter dataset of 18470 nodes, 48365 edges, collected by Nikolov et al. (OSoMe/Indiana University), 2010. Each record contains retweet edgelist, mention edgelist, node political labels (left/right/-). Twitter retweet and mention networks from 2010 with political leaning labels per user; three graph types for polarization analysis.. Suitable for Political polarization, echo chamber detection, social bubble measurement..
A Twitter dataset of 4,465 labeled Twitter accounts (991 bots, 3474 genuine), collected by Ashu273k — curated subset of Cresci et al. (2017). Each record contains user profile features (followers, friends, statuses, favourites, listed count), binary bot/genuine label, user bio text. Small, clean subset of the Cresci-2017 dataset curated for rapid prototyping and benchmarking of bot detection models, with pre-extracted user-level features and binary labels. Suitable for bot detection, social spambot classification, feature selection for account authenticity, rapid prototyping of detection algorithms.
