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

Abstract

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.

Online engagement with 2020 election misinformation and turnout in the 2021 Georgia runoff election

“…Twitter data primarily come from two sources: 1) a large sample of Twitter accounts with tweet histories since 2018, and 2) a corpus of fraud-related tweets from Abilov et al ( 5 ). Additional linked Twitter and survey data were collected via the COVID States Project ( https://www.covidstates.org/ ), a large-scale, multiwave online survey that, among other things, asks respondents to volunteer their Twitter handle (if they have one) at the conclusion of the survey.…”

“…We then compare user activity within our sample of 45,431 voter-file-matched Georgians against a large collection of tweets identified in Abilov et al ( 5 ) as either supporting or detracting from election-fraud conspiracy theories. In these data, Abilov et al ( 5 ) identified fraud-related tweets through using hand-curated keywords and hashtags—for example, “voter fraud” and “#voterfraud”—with some machine assistance for identifying very similar variants of the keywords. The researchers added the hashtag “#stopthesteal” on November 3, 2020—on Election Day and after that hashtag began trending.…”

“…Abilov et al ( 5 ) separated tweets supporting or detracting from election-fraud conspiracy theories using community detection, using Infomap ( 82 ), on the retweet networks of Twitter users in their corpus (community detection on these data finds clusters in which users more often retweet each other than retweet users in other clusters.). Community detection on these data identified five communities that accounted for 90% of the users in the data, and these communities were further separated into two distinct clusters—one cluster (four communities) that promoted conspiracy theories and another (one community) that detracted from them.…”

“…Our primary dataset links Twitter users to voter file records in Georgia. For this matched sample of users, we identify those who expressed stances regarding election-related conspiracy theories (either endorsing or rejecting them) by examining whether they retweeted or liked tweets labeled for such stances by Abilov et al ( 5 ). (For likes, we use Twitter’s “decahose,” or 10% sample, discussed in greater detail below.)…”

Replication Data for: ITA-ELECTION-2022: A multi-platform dataset of social media conversations around the 2022 Italian general election

“…(Chen, Deb, and Ferrara 2022) provide a longitudinal dataset of over 1.2 billion U.S. politics-and electionrelated tweets shared around the period of the 2020 U.S. Presidential election. Related to the same election, (Abilov et al 2021) released a multi-modal dataset of 7.6 M tweets…”