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 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 Bluesky Social dataset of approximately 1.8 million public posts, collected by Alpin Dale from the AT Protocol firehose API in 2024. Each record contains post text, author handle, timestamp, image presence flag, and reply-to URI for constructing directed reply-thread networks. multilingual corpus from a decentralized AT Protocol platform with English dominant alongside Spanish, Portuguese, Japanese, and other languages. Suitable for decentralized social media analysis, reply network structure, and multilingual content modeling.
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 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 Truth Social (user account corpus) dataset of 90,683 user accounts with 1,611 verified and 0 bot-labeled, collected by TruthStance project team via Truth Social API. Each record contains username, follower/following counts, account creation date, profile bio text, bot label, verified status. TruthStance covers 90,683 Truth Social accounts spanning 2022-02 to 2025-12, with 1,611 verified accounts and 61,226 users with bio text — one of few datasets focused on Truth Social platform users. Suitable for Truth Social platform analysis, bot detection, political social media research, user behavior modeling, alternative platform studies.
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 Reddit (state-sponsored troll comments) dataset of 7,097 Reddit comments from verified Russian state-sponsored troll accounts, collected by Zannettou et al. via Reddit API (2019). Each record contains comment body, subreddit, author, score, controversiality, created_utc, gilded, parent_id. 7,097 Reddit comments from verified Russian IRA troll accounts across 649 subreddits — enabling study of coordinated influence operations on Reddit. Suitable for information operations detection, coordinated inauthentic behavior, Reddit manipulation analysis, troll comment classification, cross-platform influence campaign research.
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 (state-sponsored IO accounts) dataset of 3,836 Russian IRA and 770 Iranian state-sponsored troll accounts with profile metadata, collected by Zannettou et al. via Twitter API (2019). Each record contains hashed user ID, display name, screen name, reported location, profile description, follower/following counts, account creation date, account language. Hashed and anonymized profiles of 4,606 verified state-sponsored troll accounts from Russian IRA and Iranian IO campaigns, enabling detection and characterization of coordinated inauthentic behavior. Suitable for information operations detection, state-sponsored troll identification, coordinated inauthentic behavior, influence campaign analysis, social media manipulation research.
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.
A LinkedIn dataset of 3,600 profiles: 1,800 legitimate, 600 fake and 1,200 generated by ChatGPT (600 from legitimate profiles' statistics, 600 from fake ones'), released by Ayoobi, Shahriar and Mukherjee (ACM Hypertext 2023). Each record holds the profile sections visible before connecting: intro, about, experience, education, skills, languages, interests, licences and more, with counts per section. Suitable for fake and LLM-generated profile detection.
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.
Twitter (X)
Parler
Bluesky
Truth Social
Reddit
LinkedIn