Abstract

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 US 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.

Metadata

publication

ICWSM 2022 - 16th International AAAI Conference on Web and Social Media, 2022

year

2022

publication date

2022

authors

Karishma Sharma, Emilio Ferrara, Yan Liu

link

https://ojs.aaai.org/index.php/ICWSM/article/view/19345

resource_link

https://ojs.aaai.org/index.php/ICWSM/article/download/19345/19117

conference

ICWSM 2022 - 16th International AAAI Conference on Web and Social Media

publisher

arXiv preprint arXiv:2107.08319