超越兔子洞：映射QAnon激进化关系危害
Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization
AI总结：
本研究通过系统映射QAnon激进化轨迹，揭示了不同人设与情绪伤害的关联，为理解激进化作为关系现象提供了实证框架。
摘要：
阴谋论的兴起已在公共讨论中造成了深远的社会危害，通过侵蚀信任并加剧极化。在这一公共影响之下，阴谋论信徒的朋友和家人所承受的深刻个人代价往往被大规模计算研究所忽视。本研究填补了这一空白，通过系统地映射激进化历程并量化对亲人造成的情绪代价。我们以QAnon的典型案例进行研究，分析了r/QAnonCasualties支持社区中的12747个叙述。首先，我们使用主题建模（BERTopic）来映射激进化轨迹，识别出关键的原有条件、触发因素和激进化后的特征。从这里，我们应用基于LDA的图形模型，揭示出六个反复出现的QAnon信徒类型，我们称之为“激进化人设”。最后，利用LLM辅助的情绪检测和回归模型，我们将这些人设与叙述者报告的具体情绪代价联系起来。我们的发现表明，这些人设不仅仅是描述性的；它们是预测叙述者所经历特定情绪伤害的强大预测因素。被感知为有意识意识形态选择的激进化与叙述者的愤怒和厌恶有关，而那些被个人和认知崩溃标记的则与恐惧和悲伤有关。这项工作为理解激进化作为一种关系现象提供了首个实证框架，为研究人员和实践者提供了导航其人际后果的重要路线图。
英文摘要：
Large-scale computational research on conspiracy theories has focused exclusively on believers' online behavior, leaving the harm experienced by those closest to them under-examined. This paper bridges this gap by analyzing 12747 stories from r/QAnonCasualties, an online support group for people who have ``lost'' someone to conspiracy beliefs. We design a computational pipeline to extract fine-grained thematic traits from personal narratives and cluster them into six coherent radicalization personas, which we then link to the emotional toll reported by narrators via LLM-assisted emotion detection and regression modeling. We find that personas are meaningful predictors of specific emotional harms: radicalization perceived as a deliberate ideological choice is associated with anger and disgust, while personas marked by personal and cognitive collapse correspond to fear and sadness. This work provides an empirically grounded computational framework for understanding the relational harms of radicalization, opening new avenues for research into its wider social consequences.
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。
