识别和刻画Twitter上QAnon阴谋论中的行为类别
Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter
AI总结：
本文提出一个利用社交互动与内容信号的框架，基于2.4亿条推文定义激进化指标，将Twitter用户划分为不同行为类别，并分析了Twitter审核政策对QAnon阴谋论传播的有效性与局限性。
摘要：
社交媒体为阴谋论和激进思想的滋生提供了肥沃的土壤，使其能够触达广泛的受众，有时甚至会在网络世界之外引发仇恨或暴力。QAnon是一个典型的政治阴谋论案例，它起源于社交媒体，后来却成为主流，部分原因是受到有影响力的政治人物的公开支持。如今，QAnon阴谋论经常出现在新闻中，成为政治言辞的一部分，并被美国大量民众所拥护。因此，理解这种阴谋论是如何在网上扎根的，以及是什么导致如此多的社交媒体用户接受其思想，显得至关重要。在这项工作中，我们提出了一个框架，利用社交互动和内容信号来揭示用户激进化的证据或对QAnon的支持。利用在2020年美国总统大选前夕收集的包含2.4亿条推文的大型数据集，我们定义并验证了一个多变量的激进化指标。我们使用该指标将用户划分为与激进化过程相关的、自然涌现的不同行为类别，从自称的QAnon支持者到高度活跃的阴谋论推广者。我们还分析了Twitter的审核政策对不同类别之间互动的影响：我们发现了一些成功的审核方面，使得高度活跃的QAnon账户获得的背书大幅减少。但我们也发现了审核失败的地方，表明QAnon内容放大者并未被Twitter的干预所阻止或影响。我们的发现深化了对网络激进化过程的理解，揭示了审核的有效和无效之处，并呼吁进一步调查社交媒体在阴谋论传播中所扮演的角色。
英文摘要：
Social media provide a fertile ground where conspiracy theories and radical ideas can flourish, reach broad audiences, and sometimes lead to hate or violence beyond the online world itself. QAnon represents a notable example of a political conspiracy that started out on social media but turned mainstream, in part due to public endorsement by influential political figures. Nowadays, QAnon conspiracies often appear in the news, are part of political rhetoric, and are espoused by significant swaths of people in the United States. It is therefore crucial to understand how such a conspiracy took root online, and what led so many social media users to adopt its ideas. In this work, we propose a framework that exploits both social interaction and content signals to uncover evidence of user radicalization or support for QAnon. Leveraging a large dataset of 240M tweets collected in the run-up to the 2020 US Presidential election, we define and validate a multivariate metric of radicalization. We use that to separate users in distinct, naturally-emerging, classes of behaviors associated to radicalization processes, from self-declared QAnon supporters to hyper-active conspiracy promoters. We also analyze the impact of Twitter's moderation policies on the interactions among different classes: we discover aspects of moderation that succeed, yielding a substantial reduction in the endorsement received by hyper-active QAnon accounts. But we also uncover where moderation fails, showing how QAnon content amplifiers are not deterred or affected by Twitter intervention. Our findings refine our understanding of online radicalization processes, reveal effective and ineffective aspects of moderation, and call for the need to further investigate the role social media play in the spread of conspiracies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
