Twitter, renamed X in 2023, is a microblogging service launched in 2006 on which users post short public messages, reply, repost and follow one another. Its open follower graph and, until 2023, its research API made it one of the most studied platforms in social media research. Website ↗
136 datasets tagged as the platform “Twitter (X)”.
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 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 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/X dataset of 42,014,825 tweets from the 2024 US presidential election period across 39 data parts with full engagement metadata (41,561,521 records with engagement metrics), collected by USC Information Sciences Institute via Twitter/X API during the 2024 US election period. Each record contains tweet text, engagement metrics (reply count, retweet count, like count, quote count, view count), language, media attachments, reply chain metadata, user location, and publication timestamp. The dataset is notable for its one of the largest publicly available datasets from the 2024 US presidential election, with full engagement metadata per tweet enabling study of information amplification and political discourse dynamics. Intended for research into political opinion mining, election discourse analysis, misinformation detection, engagement prediction, and temporal analysis of political social media.
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.
Twitter History and Image Sharing (THIS) v1.0: country-level aggregates of about 14.6 billion geolocated tweets, 2013 to 2023, released by Zachary Steinert-Threlkeld on Harvard Dataverse. Four tables, 17,928,584 rows in all: tweets and users per country and day (792,313 rows), tweets per country, language and day (15,407,849), new accounts per country and day (943,361), and image-sharing availability per country and day (785,061). No individual tweets or users are included. Suitable for country-level trends in platform use, language use and image sharing.
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 dataset of 5,072,124 tweets from 1,835 congressional accounts, collected by Alex Litel via Twitter API (2017-2023). Each record contains tweet text, screen name, user ID, timestamp, retweet count, favorite count, source, retweet status. A daily collection of tweets from U.S. Congress members, committees, caucuses and party accounts, one JSON file per day; 11 days have no file in the source repository (for example 2022-01-15, 2022-03-17 and 2022-10-24 to 2022-10-31). metadata/historical-users-filtered.json, from the collection's companion repository, maps each account to its member or office, chamber, party and type. Suitable for political communication, sentiment analysis, election studies, congressional behavior, social media analysis.
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 dataset of 2,041,399 tweet IDs from all members of the 115th U.S. Congress, collected by Littman via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. Complete tweet archive from all 115th Congress members enabling legislator communication and retweet network analysis. Suitable for political discourse, legislator communication patterns, congressional social media behavior.
A twitter dataset of 1,352,000 Spanish tweet IDs from Pride Day (June 28) across 11 years, collected by Ramiro Ortega and Hassan (2025). Each record contains tweet IDs, LGBTIQ+ vs random label — full content requires hydration. 10-year longitudinal Spanish-language Pride Day dataset enabling temporal hate speech trend analysis. Suitable for hate speech detection, temporal discourse analysis, LGBTIQ+ online safety, Spanish NLP.
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 (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 dataset of 957,956 tweet IDs from the #MeToo movement (Q3 2020), collected by Maiorana, Morales Henry, and Weintraub via Social Feed Manager. Each record contains tweet IDs only — full content requires hydration. Quarterly snapshot of #MeToo discourse enabling longitudinal analysis when combined with other quarterly collections. Suitable for social movement analysis, gender studies, harassment detection, discourse trends.
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 (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 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 Twitter dataset of 1,600,000 tweets with binary sentiment labels (positive/negative) assigned via emoticon distant supervision, collected by Go, Bhayani and Huang at Stanford University via Twitter streaming API in 2009. Each record contains tweet text with polarity label (0=negative, 4=positive), tweet ID, timestamp, search query used for collection, and username. The dataset is notable for its one of the largest and most widely used Twitter sentiment benchmarks, notable for using emoticons as a noisy but scalable distant supervision signal rather than manual annotation, enabling 1.6M labeled examples. Intended for research into sentiment analysis, opinion mining, distant supervision research, text classification benchmarking, and social media language modeling.
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 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 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 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 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 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 X (Twitter) with Community Notes annotations dataset of 152,266 multimodal posts (21,845 AI-generated, 104,915 miscaptioned), collected by Chrysidis, Papadopoulos & Papadopoulos via X Community Notes (2026). Each record contains tweet ID, reply/favorite/view/retweet counts, Community Notes text and timestamp, and misinformation type label (miscaptioned / ai_generated / other). Large-scale dataset of 152,266 multimodal X posts with Community Notes — tracking the rise of AI-generated misinformation with 21,845 confirmed AI-generated posts. Includes engagement metrics showing that AI-generated content achieves disproportionate virality despite slower initial reporting.. Suitable for AI-generated content detection, misinformation classification, Community Notes effectiveness, multimodal misinformation analysis, virality and engagement modeling, temporal degradation of detectors.
Synthetic data: 150,000 generated social-media posts about Nigerian telecom operators (MTN, Airtel, Glo, 9mobile), published on Hugging Face by THUgewu as 'africa-synth'. The posts are not real user content. The file groups them into 978 rows by platform, operator, city and sentiment, with one list per field (post ID, time, text, issue category, likes, shares, comments, reach, sentiment score, operator response). Suitable for testing sentiment and customer-service pipelines, not for studying real users.
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 Twitter dataset of 128,484 tweets with political leaning labels, collected by Chatelet via Hugging Face. Each record contains text, political label (liberal, conservative, neutral). 100K tweets labeled for political ideology with balanced classes. Suitable for political NLP, ideology classification, discourse analysis.
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 76,484 tweets across 19 events, 10 humanitarian categories, collected by Alam et al. for 19 natural disaster events (2016-2019). Each record contains tweet text, humanitarian category label, event name, train/dev/test split. Largest publicly available human-annotated crisis dataset with 76,484 tweets across 19 events. Suitable for crisis informatics, humanitarian response, text classification, social media analysis.
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 (Nigerian language communities) dataset of 60,823 tweet IDs across 4 Nigerian languages with sentiment labels, collected by Muhammad et al. via Twitter API (2022). Each record contains tweet ID, sentiment label (positive/negative/neutral), language, split (train/dev/test). First large-scale sentiment corpus for Nigerian languages — 60,823 annotated tweet IDs across Hausa, Igbo, Yoruba, and Nigerian Pidgin, enabling low-resource African NLP research.. Suitable for low-resource NLP, African language sentiment analysis, multilingual classification, cross-lingual transfer..
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 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.
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 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 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 Multi-Platform (Social Media) dataset of 26,985 bilingual (Malay-English) social media posts, collected by Mendeley Data. Each record contains text, label (hate/non-hate), language. Balanced bilingual Malay-English hate speech dataset with cleaned text. Suitable for hate speech detection, multilingual NLP, content moderation.
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 (crowdsourced hate speech annotation) dataset of 24,783 tweets labeled as hate speech (1,430), offensive (19,190), or neither (4,163), collected by Davidson et al. via CrowdFlower crowdsourcing (2017). Each record contains tweet text, majority class label (0=hate/1=offensive/2=neither), per-label coder counts, total coder count. Davidson et al. 2017 is one of the most cited hate speech datasets, with 24,783 tweets annotated by multiple crowdworkers — distinguished by separating hate speech from merely offensive language. Suitable for hate speech detection, offensive language classification, content moderation, NLP benchmarking, crowdsourced annotation research.
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 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 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).
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