论文标题
查找QS：在Parler上分析QANON的支持者
Finding Qs: Profiling QAnon Supporters on Parler
论文作者
论文摘要
社交媒体平台“ Parler”已出现到一个著名的边缘社区，在该社区中，用户群的很大一部分是Qanon的自我报告的支持者，Qanon是一个极右翼的阴谋论，指控精英团队控制着全球政治。在2020年美国总统大选期间，Qanon被认为在公共话语中发挥了重要作用。但是，对于帕尔勒（Parler）上的Qanon支持者以及除其他用户外的原因知之甚少。在社会身份理论的基础上，我们旨在分析Qanon支持者在Parler上的特征。我们分析了一个大规模数据集，其中包含超过60万个说英语用户的数据集。基于用户的个人资料，帖子和注释，我们提取了一组全面的用户功能，语言功能，网络功能和内容功能。这使我们可以执行用户分析，并了解这些功能在Qanon和Parler上的非Qanon支持者之间进行区分。我们的分析是三倍：（1）我们量化了Parler上的Qanon支持者的数量，发现34,913位用户（占所有用户的5.5％）公开报告以支持阴谋。 （2）我们研究了Qanon与非Qanon支持者之间的差异。我们发现，Qanon支持者在统计学上与多个维度的非Qanon支持者有显着差异。例如，他们平均具有更多的追随者，关注者和职位，因此对Parler网络产生了很大的影响。 （3）我们使用机器学习来确定哪些用户特征将Qanon与非Qanon支持者区分开。我们发现用户功能，语言功能，网络功能和内容功能在很大程度上可以区分Qanon vs. Parler上的非Qanon支持者。特别是，我们发现用户功能具有很高的歧视性，其次是内容功能和语言特征。
The social media platform "Parler" has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspiracy theory alleging that a cabal of elites controls global politics. QAnon is considered to have had an influential role in the public discourse during the 2020 U.S. presidential election. However, little is known about QAnon supporters on Parler and what sets them aside from other users. Building up on social identity theory, we aim at profiling the characteristics of QAnon supporters on Parler. We analyze a large-scale dataset with more than 600,000 profiles of English-speaking users on Parler. Based on users' profiles, posts, and comments, we then extract a comprehensive set of user features, linguistic features, network features, and content features. This allows us to perform user profiling and understand to what extent these features discriminate between QAnon and non-QAnon supporters on Parler. Our analysis is three-fold: (1) We quantify the number of QAnon supporters on Parler, finding that 34,913 users (5.5% of all users) openly report to support the conspiracy. (2) We examine differences between QAnon vs. non-QAnon supporters. We find that QAnon supporters differ statistically significantly from non-QAnon supporters across multiple dimensions. For example, they have, on average, a larger number of followers, followees, and posts, and thus have a large impact on the Parler network. (3) We use machine learning to identify which user characteristics discriminate QAnon from non-QAnon supporters. We find that user features, linguistic features, network features, and content features, can - to a large extent - discriminate QAnon vs. non-QAnon supporters on Parler. In particular, we find that user features are highly discriminatory, followed by content features and linguistic features.
