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The wide spread of unfounded election fraud claims surrounding the U.S. 2020election had resulted in undermining of trust in the election, culminating inviolence inside the U.S. capitol. Under these circumstances, it is critical tounderstand the discussions surrounding these claims on Twitter, a majorplatform where the claims were disseminated. To this end, we collected andreleased the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and25.6M retweets from 2.6M users related to voter fraud claims. To make this dataimmediately useful for a diverse set of research projects, we further enhancethe data with cluster labels computed from the retweet graph, each user'ssuspension status, and the perceptual hashes of tweeted images. The datasetalso includes aggregate data for all external links and YouTube videos thatappear in the tweets. Preliminary analyses of the data show that Twitter's usersuspension actions mostly affected a specific community of voter fraud claimpromoters, and exposes the most common URLs, images and YouTube videos sharedin the data.

VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter