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 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 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.