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.
翻译:在社会媒体的政治讨论中识别和定性虚假信息对于确保选举和民主进程的完整性至关重要。 持续操纵社交媒体已导致对2020年美国总统选举的关注增加,因为它有可能影响个人意见和社会动态。 在这项工作中,我们侧重于识别扭曲事实,在与选举有关的推特中以不可靠和阴谋性叙事的形式,在选举前描述操纵言论的特点。我们采用检测模式,将事实与分析与选举有关的数据集的不可靠(或共谋性)索赔区分开来,分析与选举有关的24 200万种推文。已确定的主张被用于调查虚假信息以及阴谋集团,特别是极右的QAnon阴谋集团。此外,我们通过政治倾斜和推特类型,将不可靠和阴谋性推文与QAonon阴谋集团进行记账。最后,我们使用回归中断设计,我们调查推特遏制平台上QAnon账户如何有效适应Twitter的限制。