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 (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 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.
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 6,184 tweets from Fortune 100 companies, collected by Kevin McElwee via Twitter API. Each record contains tweet text, corporation name, sector, timestamp, hashtags, racial justice label, BLM mention, Juneteenth mention, donation mention. A manually annotated dataset of tweets from Fortune 100 companies during the 2020 Black Lives Matter protests, capturing corporate responses to a major social movement. Suitable for corporate activism, social movement response, political communication, crisis communication, public relations, brand activism.
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 Twitter, Facebook, and Instagram dataset of 44 quarterly records spanning Q1 2010 to Q4 2020, collected by Margaretha Martinez via CNBC and Statista (2021). Each record contains quarter label, Twitter MAU (millions), Facebook MAU (millions), Instagram MAU (millions). A longitudinal quarterly time-series tracking monthly active user counts for three major social media platforms across a decade of growth. Suitable for platform growth analysis, social media market trends, digital media landscape studies, longitudinal comparison research.
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