  The wide spread of unfounded election fraud claims surrounding the U.S. 2020
election had resulted in undermining of trust in the election, culminating in
violence inside the U.S. capitol. Under these circumstances, it is critical to
understand the discussions surrounding these claims on Twitter, a major
platform where the claims were disseminated. To this end, we collected and
released the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and
25.6M retweets from 2.6M users related to voter fraud claims. To make this data
immediately useful for a diverse set of research projects, we further enhance
the data with cluster labels computed from the retweet graph, each user's
suspension status, and the perceptual hashes of tweeted images. The dataset
also includes aggregate data for all external links and YouTube videos that
appear in the tweets. Preliminary analyses of the data show that Twitter's user
suspension actions mostly affected a specific community of voter fraud claim
promoters, and exposes the most common URLs, images and YouTube videos shared
in the data.
