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Identifying and characterizing disinformation in political discourse onsocial media is critical to ensure the integrity of elections and democraticprocesses around the world. Persistent manipulation of social media hasresulted in increased concerns regarding the 2020 U.S. Presidential Election,due to its potential to influence individual opinions and social dynamics. Inthis work, we focus on the identification of distorted facts, in the form ofunreliable and conspiratorial narratives in election-related tweets, tocharacterize discourse manipulation prior to the election. We apply a detectionmodel to separate factual from unreliable (or conspiratorial) claims analyzinga dataset of 242 million election-related tweets. The identified claims areused to investigate targeted topics of disinformation, and conspiracy groups,most notably the far-right QAnon conspiracy group. Further, we characterizeaccount engagements with unreliable and conspiracy tweets, and with the QAnonconspiracy group, by political leaning and tweet types. Finally, using aregression discontinuity design, we investigate whether Twitter's actions tocurb QAnon activity on the platform were effective, and how QAnon accountsadapt to Twitter's restrictions.

Characterizing Online Engagement with Disinformation and Conspiracies in the 2020 U.S. Presidential Election