自动识别仇恨言论 - - 一个对极右翼YouTube视频的案例研究
Johan Eddebo1, Mika Hietanen2, Mathias Johansson3
1Centre for Multidisciplinary Research on Religion and Society (CRS), Uppsala University, Uppsala, Sweden.
F1000Research
|August 12, 2024
概括
在YouTube上识别仇恨言论 (HS) 是一个挑战. 自动化方法需要定性分析来理解alt-right视频中的间接语言,改善仇恨言论的检测.
科学领域:
- 社会科学 社会科学 社会科学
- 人文科学 人文科学
- 计算语言学 计算语言学
背景情况:
- 在线仇恨言论 (HS) 的识别至关重要,但目前的自动化方法不足以进行先发制人的检测.
- 这项研究分析了在流行的alt-right YouTube视频中自动识别HS的情况.
研究的目的:
- 概述一种跨学科的混合方法方法来实现HS自动识别,将技术和实证研究结合起来.
- 在alt-right YouTube话语中发现HS的挑战.
主要方法:
- 使用跨学科的混合方法方法,将机器学习,深度学习和自然语言处理 (NLP) 与传统的实证研究相结合.
- 案例研究侧重于来自YouTube当代激进右翼话语的数据.
主要成果:
- 有效的HS识别需要进行定性干预,以避免任意分类,并解决通信的间接性质.
- 仇恨/非仇恨言论二元方法是不够的;需要一种情境敏感的定性方法.
结论:
- 自动HS识别必须考虑到不断发展的间接语言和更广泛的话语语境.
- 有效的识别需要技术定量处理和定性分析,特别是对alt-right内容的间接框架.
- 适应间接表达提出了适度和抑制的伦理和法律挑战.
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