arXiv:2501.04820cs.SIcs.CL2025-01被引 4

提出11个通用极端主义特征,可提前10个月预测用户加入极端社区

Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization

  • 通过分析言语行为特征,构建跨意识形态的极端主义量化框架
  • 在incel社区数据中,提前3-4个月预测准确率达AUC~0.9
  • 适用于研究极端化趋势,尤其适合社会安全与平台治理领域

社交媒体上意识形态运动演变为极端派系的现象日益严重。尽管针对特定意识形态的极端化已有广泛研究,但如何以更具普适性的方式刻画极端主义仍不充分。本文提出一种新方法,用于提取和分析多种在线社区论坛中的极端话语。聚焦极端主义者的言语行为特征,构建了用户与社区层面的极端主义量化框架。研究识别出11个关键因素,称为“极端主义十一项”,构成一个通用的心理社会模型。将该方法应用于多个在线社区,成功刻画了意识形态多样社区在11个极端主义维度上的表现。通过对incel社区成员历史数据的分析,发现该框架能提前10个月预测用户加入该社区,预测性能AUC >0.6,且在进入前3-4个月时提升至AUC ~0.9。此外,用户一旦加入极端论坛,其极端水平保持稳定,并与主流网络话语显著区分。本研究为极端主义研究提供了更全面、跨意识形态的新视角,突破了传统以特定特质为中心的模型局限。

原文摘要 · Abstract (English)

The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across a range of online community forums. By focusing on verbal behavioral signatures of extremist traits, we develop a framework for quantifying extremism at both user and community levels. Our research identifies 11 distinct factors, which we term ``The Extremist Eleven,'' as a generalized psychosocial model of extremism. Applying our method to various online communities, we demonstrate an ability to characterize ideologically diverse communities across the 11 extremist traits. We demonstrate the power of this method by analyzing user histories from members of the incel community. We find that our framework accurately predicts which users join the incel community up to 10 months before their actual entry with an AUC of $>0.6$, steadily increasing to AUC ~0.9 three to four months before the event. Further, we find that upon entry into an extremist forum, the users tend to maintain their level of extremism within the community, while still remaining distinguishable from the general online discourse. Our findings contribute to the study of extremism by introducing a more holistic, cross-ideological approach that transcends traditional, trait-specific models.

极端主义检测行为预测心理建模社交网络

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