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 690,281 tweet IDs from state health agencies, CDC, and FDA (2012–2022), collected by Mendez via academictwitteR (Harvard Dataverse, 2023). Each record contains tweet IDs, account state/agency labels — full content requires hydration. Longitudinal institutional public health Twitter archive spanning pre-pandemic baseline through COVID-19 response. Suitable for public health communication, pandemic response analysis, institutional messaging.
A Instagram (fitness influencers) dataset of 319,414 posts from fitness influencers, collected by Seungbae Kim and Jinyoung Han (2020). Each record contains post caption text, like count, comment count, video/ad flags, tagged user count, location presence, image dimensions, timestamp, and influencer follower count. The dataset is notable for its subset of 9.7M-post Instagram influencer dataset covering the fitness category, with rich per-post engagement signals and influencer-level metadata. Intended for research into influencer marketing analysis, engagement prediction, social media content analysis, and sponsorship detection. This package holds the post metadata (one JSON .info file per post) and influencer lists; the post images, which the authors distribute separately on request, are not included.
A twitter dataset of 255,363 tweet IDs about the 2022 monkeypox outbreak, collected by Thakur (University of Cincinnati, 2022). Each record contains tweet IDs only — full content requires hydration. First public Twitter dataset on the 2022 monkeypox outbreak, enabling early-stage disease discourse analysis. Suitable for disease surveillance, public health communication, health misinformation.
A Reddit (r/sanctionedsuicide ban intervention, 2018) dataset of 27,002 pre-ban posts from banned community, 142,493 pre-ban control posts, 52,442 post-ban control posts, collected by Cerulli et al. via Reddit API (ICWSM 2026). Each record contains comment ID, selftext (post body), author (pseudonymized), subreddit, score, created_utc, split label (in-before/out-before/out-after). TBBT r/sanctionedsuicide: pre- and post-ban Reddit dataset from The Big Ban Theory — standardized 3-month window around the 2018 moderation intervention with matched control communities. Suitable for content moderation effects, community migration, deplatforming research, pre/post intervention analysis, Reddit ban studies.
A ReCOVery (news credibility repository) dataset of 2,029 news articles with reliability labels and social media engagement, collected by Zhou, Mulay, Ferrara, and Zafarani via news websites and Twitter (2020). Each record contains news article title and body, reliability label (1=reliable, 0=unreliable), publisher, political bias, country, author, and social media engagement data. The dataset is notable for its multimodal repository combining news content with social media engagement, designed for COVID-19 news credibility research. Intended for research into fake news detection, credibility assessment, political bias analysis, and multimodal misinformation research.
A Stack Exchange (fitness.stackexchange.com) dataset of 7,228 users, 15,385 answer-collaboration edges, 10,217 questions, 19,171 answers, collected by Stack Exchange, Inc. via the annual Stack Exchange Data Dump. Each record contains user nodes (reputation, badge counts, vote history), directed weighted edges (answerer→asker, weight=answer score, count=interactions, accepted=bool), post text, tag distribution. User collaboration network from fitness.stackexchange.com — directed weighted graph where edges represent knowledge-sharing relationships (A answered B's question), with edge weight reflecting answer quality. Enables prestige hierarchy, community detection, and expertise flow analysis.. Suitable for expertise network analysis, reputation systems, community detection, knowledge flow, PageRank-based authority ranking, Q&A platform dynamics..
A Amazon dataset of 80,496 QA pairs in Health & Personal Care, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Health & Personal Care. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Stack Exchange (health.stackexchange.com) dataset of 3,692 users, 5,430 answer-collaboration edges, 7,930 questions, 6,673 answers, collected by Stack Exchange, Inc. via the annual Stack Exchange Data Dump. Each record contains user nodes (reputation, badge counts, vote history), directed weighted edges (answerer→asker, weight=answer score, count=interactions, accepted=bool), post text, tag distribution. User collaboration network from health.stackexchange.com — directed weighted graph where edges represent knowledge-sharing relationships (A answered B's question), with edge weight reflecting answer quality. Enables prestige hierarchy, community detection, and expertise flow analysis.. Suitable for expertise network analysis, reputation systems, community detection, knowledge flow, PageRank-based authority ranking, Q&A platform dynamics..
Contacts between people measured by small wireless devices they carried, from the Haggle project (Chaintreau et al.), as distributed by KONECT (contact): 28,244 timestamped contacts among 274 people, forming 2,124 distinct pairs. An edge means two people's devices were within range of each other; timestamps are relative and the records span about four days. Useful for opportunistic networking, temporal network analysis and epidemic modelling on human contact data.
A PubMed dataset of 19,717 nodes, 44,327 edges, 3 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. PubMed 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 Voat (Reddit alternative, 2013–2020) dataset of 2,256 submissions, 3,898 comments, 1,833 users, 2,721 collaboration edges, collected by Mekacher & Papasavva (ICWSM 2022) via full platform crawl. Each record contains user nodes (registration date, moderation status), directed weighted edges (commenter→submitter, weight=upvotes+1), submission titles and bodies, comment text, upvotes, downvotes, depth. Complete dataset for the Voat subverse v/Health, covering its full lifetime on the platform (2013–2020). Includes all 2,256 submissions and 3,898 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.
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 Science Gallery Dublin (Infectious exhibition, 2009) dataset of 410 nodes and 2,765 edges, collected by SocioPatterns collaboration via RFID proximity sensing. Each record contains directed: False, weighted: True, signed: False, temporal: True. High-resolution face-to-face contact network of 410 visitors at a science exhibition, recorded via RFID sensors at 20-second resolution. One of the canonical SocioPatterns datasets for epidemic modeling.. Suitable for epidemic modeling, contact network analysis, temporal networks. The file holds 17,298 contacts, one row per 20-second face-to-face contact; the edge count of 2,765 counts distinct pairs.
A Medical innovation adoption network dataset of 241 nodes and 1,098 edges, collected by Coleman et al.. Each record contains directed edges: True, weighted: False, signed: False. This directed network captures innovation spread among 246 physicians in for towns in Illinois, Peoria, Bloomington, Quincy and Galesburg. The data was collected in 1966. A node represents a physician and an edge between two physicians shows that the left physician told that the righ physician is hi. Suitable for diffusion of innovations, social influence.
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.
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