Mendelsohn Julia, Tsvetkov Yulia, Jurafsky Dan
School of Information, University of Michigan, Ann Arbor, MI, United States.
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, United States.
Front Artif Intell. 2020 Aug 7;3:55. doi: 10.3389/frai.2020.00055. eCollection 2020.
Dehumanization is a pernicious psychological process that often leads to extreme intergroup bias, hate speech, and violence aimed at targeted social groups. Despite these serious consequences and the wealth of available data, dehumanization has not yet been computationally studied on a large scale. Drawing upon social psychology research, we create a computational linguistic framework for analyzing dehumanizing language by identifying linguistic correlates of salient components of dehumanization. We then apply this framework to analyze discussions of LGBTQ people in the from 1986 to 2015. Overall, we find increasingly humanizing descriptions of LGBTQ people over time. However, we find that the label has emerged to be much more strongly associated with dehumanizing attitudes than other labels, such as . Our proposed techniques highlight processes of linguistic variation and change in discourses surrounding marginalized groups. Furthermore, the ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
去人性化是一种有害的心理过程，常常导致针对特定社会群体的极端群体间偏见、仇恨言论和暴力行为。尽管存在这些严重后果以及丰富的现有数据，但去人性化尚未得到大规模的计算研究。借鉴社会心理学研究，我们创建了一个计算语言框架，通过识别去人性化显著成分的语言关联来分析去人性化语言。然后，我们应用这个框架来分析1986年至2015年期间关于 LGBTQ 人群的讨论。总体而言，我们发现随着时间的推移，对 LGBTQ 人群的描述越来越人性化。然而，我们发现，与其他标签（如 ）相比，标签 与去人性化态度的关联要强得多。我们提出的技术突出了围绕边缘化群体的话语中的语言变异和变化过程。此外，大规模分析去人性化语言的能力对于自动检测和理解媒体偏见以及在线辱骂性语言具有重要意义。
