Multilingual and Multitarget Hate Speech Detection in Tweets
Multilingual and Multitarget Hate Speech Detection in Tweets
复制标题
推文中的多语言和多目标仇恨言论检测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Emmanuel Macron
中科院分区：
文献类型：
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作者：
Patricia Chiril;Farah Benamara;Véronique Moriceau;Marlène Coulomb;Abhishek Kumar;Emmanuel Macron
社交媒体网络已成为用户自由表达意见和情感的空间，这可能导致仇恨或辱骂信息的大量传播，必须加以节制。本文提出了一种有监督的方法，从多语言的角度来检测仇恨言论。我们特别关注英文推文中针对两个不同目标（移民和妇女）的仇恨信息，以及英文和法文中的性别歧视信息。已经开发了几种模型，从特征工程方法到神经方法。我们的实验在两种语言上都显示出非常令人鼓舞的结果。
Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated. This paper proposes a supervised approach to hate speech detection from a multilingual perspective. We focus in particular on hateful messages towards two different targets (immigrants and women) in English tweets, as well as sexist messages in both English and French. Several models have been developed ranging from feature-engineering approaches to neural ones. Our experiments show very encouraging results on both languages.