A NDS-VU signed network repository dataset of 5,882 nodes and 21,492 signed edges (synthetic Bitcoin OTC variant), collected by NDS-VU (Vrije Universiteit Amsterdam). Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Synthetic version of the Bitcoin OTC web-of-trust network with reversed edge polarities, enabling adversarial and robustness testing of signed network algorithms. Suitable for signed network analysis, algorithm benchmarking, robustness testing.
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 NDS-VU signed network repository dataset of 220 Congress members and 521 signed co-sponsorship edges, collected by NDS-VU (Vrije Universiteit Amsterdam). Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Signed network of U.S. Congress members based on bill co-sponsorship patterns, where positive edges indicate co-sponsorship and negative edges indicate opposition, enabling political alignment and polarization analysis. Suitable for political polarization, signed network analysis, legislative behavior.
A NDS-VU signed network repository dataset of 16 tribes and 58 signed alliance/opposition edges, collected by Read (1954), 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 ethnographic signed network of New Guinea Highland tribes with alliance (+1) and opposition (-1) relationships, one of the earliest and most cited networks for structural balance theory validation. Suitable for balance theory, structural balance, signed network benchmarking.
A NDS-VU signed network repository dataset of 10,885 users and 251,406 signed interaction edges from Italian referendum Twitter, collected by NDS-VU (Vrije Universiteit Amsterdam, 2016). Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Signed retweet/mention network from the 2016 Italian constitutional referendum on Twitter, where edges represent support (+1) or opposition (-1) interactions between users discussing the referendum. Suitable for political polarization, signed social network analysis, referendum discourse.
A NDS-VU signed network repository dataset of 7,116 Wikipedia editors and 1,048,576 signed conflict/collaboration edges, collected by NDS-VU from Wikipedia edit history. Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Large signed network of Wikipedia editor interactions derived from edit conflict and revert patterns, where positive edges indicate collaborative edits and negative edges indicate conflicts and reverts between editors. Suitable for signed network analysis, conflict detection, collaborative platform dynamics.
A NDS-VU signed network repository dataset of 7,116 Wikipedia users and 100,693 signed RfA vote edges, collected by NDS-VU from Wikipedia RfA archives. Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Signed network of Wikipedia requests for adminship (RfA) votes where edges represent support (+1) or oppose (-1) votes cast during admin election campaigns, enabling trust and reputation analysis in collaborative platforms. Suitable for signed network analysis, reputation systems, voting behavior, trust modeling.
A NDS-VU signed network repository dataset of 138,588 Wikipedia editors and 715,883 signed interaction edges on political articles, collected by NDS-VU from Wikipedia edit history. Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Large signed network of Wikipedia editors working on political articles, capturing support and opposition relationships derived from edit patterns and talk page interactions in politically charged content areas. Suitable for political discourse analysis, signed network analysis, editor behavior.
A NELA-PS (pink slime local news) dataset of 7,910,965 articles from 1,093 pink slime news sources, collected by Horne and Gruppi, published via Harvard Dataverse (2024). Each record contains full article text, headline, source outlet name, publication date, author, location, network, and URL. The dataset is notable for its largest available dataset of pink slime news — low-quality partisan content systematically framed as authentic local news, spanning 1,093 outlets over 2.5 years with stratified cross-outlet topic modeling. Intended for research into misinformation detection, local news ecosystem analysis, partisan content identification, and pink slime outlet characterisation.
A Reddit dataset of 398,487 comments from NFL subreddits during the 2020 draft, collected by Caio Brighenti via Reddit API (PRAW). Each record contains comment body, team, timestamp, VADER sentiment score, author. A real-time snapshot of NFL fan sentiment during the 2020 draft, capturing reactions to each pick across team subreddits. Suitable for sentiment analysis, sports analytics, social media analysis, fan behavior, natural language processing.
A Twitter dataset of 5,171 sentiment-labeled tweets about NFL teams, collected by Cameron C (Kaggle user cammyc) via Twitter API. Each record contains tweet text, sentiment (positive/negative/neutral), team, username, timestamp, confidence score. A curated dataset of 3,701 NFL-related tweets with sentiment labels, enabling research on sports fan sentiment, team-specific public opinion, and social media engagement patterns during the NFL season. Suitable for sentiment analysis, sports marketing, fan engagement, public opinion tracking, temporal sentiment analysis.
A NoisyHate (hate speech benchmark) dataset of 1,339 labeled sentences with text perturbations and toxicity scores, collected by NoisyHate Authors via ICWSM 2025. Each record contains sentence text, binary hate label, toxicity scores (toxicity, severe, obscene, identity_attack, insult, threat), identity group indicators, and engagement metrics. The dataset is notable for its benchmark dataset with human-written text perturbations and fine-grained toxicity metrics. Intended for research into hate speech detection, model robustness, adversarial testing, toxicity analysis, and identity-based hate speech research.
A Twitter (#NoMore hashtag campaign) dataset of 1,048,574 tweets from the Ethiopian #NoMore movement (2021-11 to 2022-08), collected by Zenodo contributors via Twitter API. Each record contains tweet text, language, timestamp; Amharic+English bilingual subset also available. 1,048,574 tweets covering the #NoMore solidarity movement in Ethiopia and Eritrea — one of few African social movement datasets with Amharic content, spanning 2021-11 to 2022-08. Suitable for African social movement analysis, multilingual NLP, Amharic text processing, political communication research, hashtag activism, Global South social media studies.
A Twitter (aggregate) dataset of 4,709,404 topic-related tweets across 6 social issues, collected by NORC at the University of Chicago. Each record contains date, topic, daily volume, VADER compound sentiment, pos/neg ratio, account type (community/bot/news/government/activist), US state. Aggregate Twitter data on six politically salient social issues with daily volume and VADER sentiment scores 2016-2022, including unique bot-vs-community account type breakdowns and US state-level marijuana discourse data. Suitable for political communication, sentiment trends, bot detection, state-level public opinion, longitudinal social issue analysis.
A OECD dataset of 44 countries spanning 2008–2023, collected by OECD via Zenodo. Each record contains Country, year, indicator, observation value, breakdowns by age/gender/education. Detailed ICT usage data with demographic breakdowns. Suitable for digital divide analysis, ICT adoption trends.
A Twitter (OffensEval 2020 — 5 languages) dataset of 61,922 tweets across 5 languages with offensive/non-offensive labels, collected by Zampieri et al. for SemEval-2020 Task 12. Each record contains tweet text, binary offensive label (OFF/NOT), language, split. Combined OffensEval 2020 benchmark across Arabic, Danish, English, Greek, and Turkish — 61,922 tweets from the most-participated SemEval task ever (528 teams). Suitable for multilingual offensive language detection, cross-lingual transfer, content moderation, NLP benchmarking.
A Twitter (OffensEval 2020 — Arabic) dataset of 9,666 Arabic tweets (1,919 offensive, 7,747 non-offensive), collected by Zampieri et al. for SemEval-2020 Task 12. Each record contains tweet text, binary offensive label (OFF/NOT), split (train/test). OffensEval 2020 Arabic — part of the most popular SemEval task of all time (528 teams), enabling multilingual offensive language research. Suitable for offensive language detection, multilingual NLP, cross-lingual transfer learning, content moderation benchmarking.
A Twitter (OffensEval 2020 — Danish) dataset of 3,290 Danish tweets (425 offensive, 2,865 non-offensive), collected by Zampieri et al. for SemEval-2020 Task 12. Each record contains tweet text, binary offensive label (OFF/NOT), split (train/test). OffensEval 2020 Danish — part of the most popular SemEval task of all time (528 teams), enabling multilingual offensive language research. Suitable for offensive language detection, multilingual NLP, cross-lingual transfer learning, content moderation benchmarking.
A Twitter (OffensEval 2020 — English) dataset of 3,887 English tweets (1,080 offensive, 2,807 non-offensive), collected by Zampieri et al. for SemEval-2020 Task 12. Each record contains tweet text, binary offensive label (OFF/NOT), split (train/test). OffensEval 2020 English — part of the most popular SemEval task of all time (528 teams), enabling multilingual offensive language research. Suitable for offensive language detection, multilingual NLP, cross-lingual transfer learning, content moderation benchmarking.
A Twitter (OffensEval 2020 — Greek) dataset of 10,287 Greek tweets (2,728 offensive, 7,559 non-offensive), collected by Zampieri et al. for SemEval-2020 Task 12. Each record contains tweet text, binary offensive label (OFF/NOT), split (train/test). OffensEval 2020 Greek — part of the most popular SemEval task of all time (528 teams), enabling multilingual offensive language research. Suitable for offensive language detection, multilingual NLP, cross-lingual transfer learning, content moderation benchmarking.
A Twitter (OffensEval 2020 — Turkish) dataset of 34,792 Turkish tweets (6,757 offensive, 28,035 non-offensive), collected by Zampieri et al. for SemEval-2020 Task 12. Each record contains tweet text, binary offensive label (OFF/NOT), split (train/test). OffensEval 2020 Turkish — part of the most popular SemEval task of all time (528 teams), enabling multilingual offensive language research. Suitable for offensive language detection, multilingual NLP, cross-lingual transfer learning, content moderation benchmarking.
A OpenFlights.org airport route database dataset of 3,425 nodes and 37,595 edges, collected by OpenFlights.org. Each record contains directed: True, weighted: True. Directed network of airports connected by flight routes — nodes are airports, edges are direct routes.. Suitable for transportation network analysis, network robustness, hub detection.
A Academic co-authorship bipartite network dataset of 38,741 nodes and 58,595 edges, collected by T. Opsahl. Each record contains directed: False, weighted: False, signed: False. Large bipartite collaboration network of authors and papers — useful for studying academic collaboration patterns.. Suitable for bipartite network analysis, academic collaboration, community detection.
A US Western States power grid infrastructure dataset of 4,941 nodes and 6,594 edges, collected by D.J. Watts & S.H. Strogatz. Each record contains directed: False, weighted: False, signed: False. Classic small-world network of the US western power grid — nodes are generators/transformers, edges are transmission lines.. Suitable for infrastructure network analysis, small-world networks, robustness analysis.
A Southern women event attendance bipartite network (1930s) dataset of 32 nodes and 89 edges, collected by T. Opsahl. Each record contains directed: False, weighted: False, signed: False. Bipartite network of women and social events — Opsahl's version of the classic Davis dataset.. Suitable for bipartite network analysis, classic network benchmark.
A UC Irvine online student forum dataset of 1,421 nodes and 7,089 edges, collected by T. Opsahl. Each record contains directed: False, weighted: True, signed: False. Temporal bipartite network of students posting to forum threads with Unix timestamps.. Suitable for temporal bipartite network analysis, online community analysis.
A UC Irvine online messaging system dataset of 1,899 nodes and 20,296 edges, collected by T. Opsahl. Each record contains directed: True, weighted: True, signed: False. Temporal directed messaging network among UC Irvine students with Unix timestamps.. Suitable for temporal network analysis, online social network analysis. The file holds 59,835 messages, one row per message; the edge count of 20,296 counts distinct pairs.
Orkut users and the groups they joined, as distributed by KONECT (orkut-groupmemberships), from the crawl of Mislove et al. (2007): a bipartite network linking 2.8 million users to the 8.7 million Orkut groups they belonged to, with some 327 million memberships. Orkut was Google's social network, active 2004–2014. Useful for large-scale community detection, affiliation-network analysis and the structure of online groups.
The Orkut friendship network crawled by Mislove et al. (2007), as distributed by Network Repository (soc-orkut-dir): 3,072,441 users and 117,185,083 undirected friendships, each pair listed once. Despite the "-dir" in Network Repository's name, the file is marked symmetric and Orkut friendships were mutual. Orkut was Google's social network, active 2004–2014 and especially popular in Brazil and India. Useful for large-scale social network analysis, community detection and graph mining.
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 Twitter dataset of 2,280 user accounts, 368,028 tweets, collected by Yang and Menczer (OSoMe/Indiana University), 2023. Each record contains user_id, label (bot/human), dataset source, full tweet objects with text and metadata. First large-scale dataset of AI-powered social bots (Fox8 botnet) with GPT-generated tweet content; includes human baseline accounts for comparison.. Suitable for AI bot detection, LLM-generated content identification, social botnet analysis..
A Last.fm dataset of 976741 users, 3151283 friendships, 10.9M annotations, collected by Kompan and Bielikova, first half of 2009. Each record contains user friendship graph, user-item-tag annotations, group membership, neighbourhood scores. Last.fm social network with 99k users, 10.9M music annotations, 281k tags, and 66k groups from H1 2009.. Suitable for Social influence analysis, music recommendation, tag-based community detection..
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 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 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 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 Delicious dataset of 61,665 bookmarks, collected by OSoMe / GiveALink project, November 2009. Each record contains bookmark URL, timestamp, user tags. Daily Delicious social bookmarking snapshots from November 2009; each bookmark includes URL, Unix timestamp, and free-form user tags.. Suitable for Social bookmarking analysis, tag-based topic modeling, information diffusion, temporal content analysis..
A Parler (alternative social network) dataset of ~183,000,000 posts from Parler's full history before deplatforming, collected by Aliapoulios et al. via Parler API (2021). Each record contains post body text, username, timestamp, hashtags, upvotes, reposts, impressions, comment count, sensitive flag, media attachments, reply depth, parent post ID. Complete Parler post archive from before the platform's deplatforming — includes Jan 6 Capitol riot content, far-right discourse, and cross-posts from Twitter. Suitable for deplatforming research, far-right discourse analysis, January 6th research, hate speech detection, alternative platform behavior, content moderation effects.
A Parler (alternative social network) dataset of 13,254,060 Parler user profiles with bios and activity metrics, collected by Aliapoulios et al. via Parler API (2021). Each record contains user ID, username, bio text, follower/following counts, post/comment/like/media counts, join date, verified/banned/human flags. 13,254,060 Parler user profiles from the platform's peak period before its deplatforming — 605 verified accounts, 287,391 banned users. Suitable for deplatforming research, far-right social network analysis, user behavior on alternative platforms, bot detection, political communication research.
A US Patent and Trademark Office (1963–1999) dataset of 3,774,768 nodes and 16,518,947 edges, collected by B.H. Hall et al.. Each record contains directed: True, weighted: False. Massive directed citation network of 3.7M US patents spanning 1963–1999 — one of the largest citation networks available.. Suitable for citation network analysis, technology diffusion, innovation research.
A #petpeeve (annoying behaviors) dataset of 3,375 tweets labeled with annoying behavior categories, collected by Wang and Yang via Twitter API (2015). Each record contains tweet text and annoying behavior category label. The dataset is notable for its dataset for automatic categorization of annoying behaviors using lexical and frame-semantic embeddings. Intended for research into social media analysis, text classification, and behavioral categorization.
A Petster.com (pet social network — carnivores) dataset of 623,766 nodes and 15,699,276 edges, collected by KONECT via Petster crawl. Each record contains directed: False, weighted: False, signed: False. Friendship network of carnivore pet profiles on a pet social networking site.. Suitable for social network analysis, community detection.
A Petster.com (pet social network — cats) dataset of 149,700 nodes and 5,449,275 edges, collected by KONECT via Petster crawl. Each record contains directed: False, weighted: False, signed: False. Friendship network of cat profiles on a pet social networking site.. Suitable for social network analysis, community detection.
A Petster.com (pet social network — dogs) dataset of 426,820 nodes and 8,546,581 edges, collected by KONECT via Petster crawl. Each record contains directed: False, weighted: False, signed: False. Friendship network of dog profiles on a pet social networking site.. Suitable for social network analysis, community detection.
A Petster.com (pet social network — hamsters) dataset of 1,858 nodes and 12,534 edges, collected by KONECT via Petster crawl. Each record contains directed: False, weighted: False, signed: False. Friendship network of hamster profiles on a pet social networking site.. Suitable for social network analysis, community detection.
A Pew Research Center dataset of 11 annual survey waves spanning 2012–2025, collected by Pew Research Center via annual US adult surveys. Each record contains survey year, platform-specific adoption percentages for US adults. Annual survey tracking US adult adoption rates across all major social media platforms from 2012 to the present. Suitable for social media adoption trends, platform popularity analysis, digital behavior research, longitudinal survey analysis.
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`.
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 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`.
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