论文标题
通过结构性主题建模检查在线虐待语料库中的种族偏见
Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling
论文作者
论文摘要
我们使用结构性主题建模来检查收集的数据中的种族偏见，以培训模型，以检测社交媒体帖子中的仇恨言论和滥用语言。我们通过添加附加功能来增强滥用语言数据集，该功能指示用非裔美国人英语编写的推文的预测概率。然后，我们使用结构性主题建模来检查推文的内容，以及不同主题的普遍性与滥用性注释和方言预测如何相关。我们发现某些主题不成比例地种族化，被认为是滥用的。我们讨论主题建模如何成为确定注释数据中偏见的有用方法。
We use structural topic modeling to examine racial bias in data collected to train models to detect hate speech and abusive language in social media posts. We augment the abusive language dataset by adding an additional feature indicating the predicted probability of the tweet being written in African-American English. We then use structural topic modeling to examine the content of the tweets and how the prevalence of different topics is related to both abusiveness annotation and dialect prediction. We find that certain topics are disproportionately racialized and considered abusive. We discuss how topic modeling may be a useful approach for identifying bias in annotated data.
