The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists

Christina T. Lu, David Jurgens
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引用次数: 3

Abstract

Toxic language can take many forms, from explicit hate speech to more subtle microaggressions. Within this space, models identifying transphobic language have largely focused on overt forms. However, a more pernicious and subtle source of transphobic comments comes in the form of statements made by Trans-exclusionary Radical Feminists (TERFs); these statements often appear seemingly-positive and promote women’s causes and issues, while simultaneously denying the inclusion of transgender women as women. Here, we introduce two models to mitigate this antisocial behavior. The first model identifies TERF users in social media, recognizing that these users are a main source of transphobic material that enters mainstream discussion and whom other users may not desire to engage with in good faith. The second model tackles the harder task of recognizing the masked rhetoric of TERF messages and introduces a new dataset to support this task. Finally, we discuss the ethics of deploying these models to mitigate the harm of this language, arguing for a balanced approach that allows for restorative interactions.
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排斥的微妙语言:识别跨性别排斥的激进女权主义者的有毒言论
有毒语言可以有多种形式,从明确的仇恨言论到更微妙的微侵犯。在这个空间里,识别跨性别语言的模型主要集中在公开的形式上。然而,一个更有害和微妙的跨性别言论来源来自于跨性别排斥激进女权主义者(terf)的声明;这些声明通常看起来是积极的,促进了妇女的事业和问题,同时否认变性妇女是女性。在这里,我们介绍了两个模型来减轻这种反社会行为。第一个模型识别社交媒体中的TERF用户,认识到这些用户是进入主流讨论的变性材料的主要来源,其他用户可能不希望真诚地与他们接触。第二个模型处理更难的任务,即识别TERF消息的屏蔽修辞,并引入了一个新的数据集来支持该任务。最后,我们讨论了部署这些模型以减轻这种语言的危害的伦理问题,主张采用一种允许恢复性交互的平衡方法。
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