评估极右极端分子之间的暴力风险:自然语言处理的新角色
Julia Ebner1, Christopher Kavanagh1, Harvey Whitehouse1
1Department of Anthropology and Museum Ethnography, University of Oxford, Oxford, UK.
Terrorism and political violence
|September 11, 2024
概括
对极端主义团体的语言分析可以检测暴力极端主义的风险因素. 一个新的框架确定了语言标记,表明潜在的现实世界暴力,帮助早期发现和预防工作.
科学领域:
- 极端群体的心理学 极端群体的心理学
- 计算语言学计算语言学
- 冲突的社会学冲突的社会学.
背景情况:
- 为群体而战的意愿源于个人和群体身份的融合.
- 受到威胁的群体,宽容暴力和对敌人的非人道化是关键因素.
- 早期发现暴力极端主义风险因素至关重要.
研究的目的:
- 调查极端主义语言是否可以促进早期发现暴力极端主义风险因素.
- 开发和应用一种新的基于融合的语言暴力风险评估框架.
- 分析极右翼极端主义在线团体的语言模式.
主要方法:
- 应用了基于融合的语言暴力风险评估框架.
- 利用基于R的自然语言处理 (NLP) 分析.
- 开发了一种暴力风险指数,将恐怖主义宣言中的语言标记整合在一起.
- 极端主义团体的风险分数与非极端主义对照组的风险分数进行比较.
- 结合了定性见解来将发现置于背景.
主要成果:
- 融合标记和其他语言变量准确地表明了现实世界的暴力水平.
- 在NLP分析中,每个组都获得了加权的风险评估分数.
- 确定了显著的语言标记,区分暴力与非暴力公报.
- 极右翼极端主义团体表现出不同的语言模式.
结论:
- 语言分析为发现和预防暴力恐怖主义提供了一个有希望的途径.
- 基于融合的框架有效地识别了与暴力极端主义相关的风险因素.
- 语言分析可以作为潜在暴力的早期预警系统.
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