极端的统一:开发一个统一的模型来检测和预测极端特征和激进化
Allison Lahnala1, Vasudha Varadarajan2, Lucie Flek3
1Department of Computing & Software, McMaster University.
概括
研究人员开发了一种新的方法,通过口头行为量化在线极端主义,识别"极端主义十一"的特征. 这个框架预测了几个月的极端主义社区的激进化.
科学领域:
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
- 社会学 社会学 社会学
背景情况:
- 在线意识形态运动越来越多地形成极端派系.
- 现有的关于激进化的研究往往是特定于意识形态的,缺乏可概括的特征.
- 在多样化的在线社区中量化极端主义仍然是一个挑战.
研究的目的:
- 提出一种新的,可通用的方法来提取和分析极端主义话语.
- 开发一个框架,在用户和社区层面量化极端主义.
- 确定极端主义的一般化心理社会模型.
主要方法:
- 从在线意识形态社区提取和分析极端主义话语.
- 专注于极端主义特征的口头行为特征.
- 根据11个不同的因素 ("极端主义十一个") 制定一个量化极端主义的框架.
主要成果:
- 在11个极端主义特征中,成功地描述了意识形态多样化的在线社区.
- 预测用户在10个月前进入英塞尔社区,准确度很高 (AUC>0.6,增加到~0.9).
- 观察到用户在加入论坛时保持极端主义的特征,与一般的在线话语不同.
结论:
- 引入了一种整体的,跨意识形态的方法来研究极端主义.
- "极端主义十一"框架为量化在线极端主义提供了一个通用的心理社会模型.
- 该方法证明了用于识别极端化风险的个体的预测能力.
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