标题： 统一极端情况：开发一种统一的模型，用于检测和预测极端主义特征和激进化 显示英文标题

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

摘要： 通过社交媒体，意识形态运动扩散到极端主义派系已成为全球关注的问题。 尽管在特定意识形态的背景下对激进化进行了广泛研究，但我们准确地以更具普遍性的方式描述极端主义的能力仍不成熟。 在本文中，我们提出了一种新方法，用于提取和分析一系列在线社区论坛中的极端主义言论。 通过专注于极端主义特征的语言行为特征，我们开发了一个框架，用于在用户和社区层面量化极端主义。 我们的研究识别出11个不同的因素，我们将其称为“极端主义十一项”，作为极端主义的通用心理学和社会学模型。 将我们的方法应用于各种在线社区，我们展示了能够跨11个极端主义特征对意识形态多样化的社区进行描述的能力。 我们通过分析incel社区成员的用户历史来展示该方法的力量。 我们发现，我们的框架能够在用户实际加入incel社区前10个月准确预测哪些用户会加入，AUC为$>0.6$，事件发生前三到四个月时AUC增加到约0.9。 此外，我们发现，当用户进入一个极端主义论坛时，他们倾向于在社区内保持其极端主义水平，同时仍然可与一般网络言论区分开来。 我们的研究通过引入一种更全面、跨意识形态的方法，超越传统的特定特质模型，为极端主义的研究做出了贡献。 显示英文摘要

摘要： 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.