Repeat Spreaders and Election Delegitimization

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