Repeat Spreaders and Election Delegitimization: A Comprehensive Dataset of Misinformation Tweets from the 2020 U.S. Election

Abstract

This paper introduces and presents a first analysis of a uniquely curated dataset of misinformation, disinformation, and rumors spreading on Twitter about the 2020 U.S. Election. Previous research on misinformation — an umbrella term for false and misleading content — has largely focused either on broad categories, using a finite set of keywords to cover a complex topic, or on a few, focused case studies, with increased precision but limited scope. Our approach, by comparison, leverages real-time reports collected from September through November 2020 to develop a comprehensive dataset of tweets connected to 456 distinct misinformation stories from the 2020 US Presidential election (our ElectionMisinfo2020 dataset), 307 of which sowed doubt in the legitimacy of the election. By relying on real-time incidents and streaming data, we generate a curated dataset that not only provides more granularity than a large collection based on a finite number of search terms, but also an improved opportunity for generalization compared to a small set of case studies. Though the emphasis is on misleading content, not all of the tweets linked to a misinformation story are false: some are questions, opinions, corrections, or factual content that nonetheless contributes to misperceptions. Along with a detailed description of the data, this paper provides an analysis of a critical subset of election-delegitimizing misinformation in terms of size, content, temporal diffusion, and partisanship. We label key ideological clusters of accounts within interaction networks, describe common misinformation narratives, and identify those accounts which repeatedly spread misinformation. We document the asymmetry of misinformation spread: accounts associated with support for President Biden shared stories in ElectionMisinfo2020 far less than accounts supporting his opponent. That asymmetry remained among the accounts who were repeatedly influential in the spread of misleading content that sowed doubt in the election: all but two of the top 100 ‘repeat spreader’ accounts were supporters of then-President Trump. These findings support the implementation and enforcement of ‘strike rules’ on social media platforms, directly addressing the outsized role of repeat spreaders.

Tracking Fringe and Coordinated Activity on Twitter Leading Up to the US Capitol Attack

“…• Leveraging manually curated annotations (Kennedy et al 2022;Abilov et al 2021), we observed how the "Stop the Steal" rhetoric was by far the narrative mostly amplified by coordinated actors. Further, we uncovered and verified that the vast majority of users involved in such manipulation actions were supporting and promoting claims about voter fraud (Abilov et al 2021).…”

Facebook’s News Feed Algorithm and the 2020 US Election

“…Many are thus concerned that platforms may be undermining democratic processes: in the United States, 73% of adults had "little or no confidence in tech companies to prevent the misuse of their platforms to influence the 2020 election" (Green, 2020). Given the widespread concern, urgency, and ambiguity surrounding the topic, researchers have devoted increasing attention toward understanding how platforms contribute to news audiences during elections (Edelson et al, 2021;Hecht et al, 2017;Kennedy et al, 2022;Mustafaraj et al, 2020).…”

Identification and characterization of misinformation superspreaders on social media

“…Recent research suggests that superspreaders of misinformation -users who consistently disseminate a disproportionately large amount of lowcredibility content -may be at the center of this problem [15,45,21,12,51,6].…”

“…Social bots also played a disproportionate role in spreading content from lowcredibility sources [44]. The Election Integrity Partnership (a consortium of academic and industry experts) reported that during the 2020 presidential election, a small group of "repeat spreaders" aggressively pushed false election claims across various social media platforms for political gain [21,12].…”