  Identifying and characterizing disinformation in political discourse on
social media is critical to ensure the integrity of elections and democratic
processes around the world. Persistent manipulation of social media has
resulted in increased concerns regarding the 2020 U.S. Presidential Election,
due to its potential to influence individual opinions and social dynamics. In
this work, we focus on the identification of distorted facts, in the form of
unreliable and conspiratorial narratives in election-related tweets, to
characterize discourse manipulation prior to the election. We apply a detection
model to separate factual from unreliable (or conspiratorial) claims analyzing
a dataset of 242 million election-related tweets. The identified claims are
used to investigate targeted topics of disinformation, and conspiracy groups,
most notably the far-right QAnon conspiracy group. Further, we characterize
account engagements with unreliable and conspiracy tweets, and with the QAnon
conspiracy group, by political leaning and tweet types. Finally, using a
regression discontinuity design, we investigate whether Twitter's actions to
curb QAnon activity on the platform were effective, and how QAnon accounts
adapt to Twitter's restrictions.
