预测推文发布后会收到多少辱骂回复，帮用户提前规避风险。
Will I Get Hate Speech Predicting the Volume of Abusive Replies before Posting in Social Media
- 基于内容、元数据和账户特征构建预测模型
- 内容特征比用户身份更能预测辱骂回复数量
- 适合想减少网络暴力的社交平台使用者
尽管已有大量研究关注社交媒体中的攻击性语言，但多为事后的判断。本研究填补了事前预测的空白，旨在预测某条推文发布后可能收到的辱骂回复数量。我们从用户视角出发，分析文本、文本元数据、推文元数据及账户特征四类因素，探讨内容与用户身份对辱骂回复量的影响。实验表明，仅依靠内容相关特征即可构建性能优异的预测模型；而用户身份特征对模型表现影响甚微，说明辱骂回复主要由内容触发，而非发帖人身份所致。
原文摘要 · Abstract (English)
Despite the growing body of research tackling offensive language in social media, this research is predominantly reactive, determining if content already posted in social media is abusive. There is a gap in predictive approaches, which we address in our study by enabling to predict the volume of abusive replies a tweet will receive after being posted. We formulate the problem from the perspective of a social media user asking: ``if I post a certain message on social media, is it possible to predict the volume of abusive replies it might receive?'' We look at four types of features, namely text, text metadata, tweet metadata, and account features, which also help us understand the extent to which the user or the content helps predict the number of abusive replies. This, in turn, helps us develop a model to support social media users in finding the best way to post content. One of our objectives is also to determine the extent to which the volume of abusive replies that a tweet will get are motivated by the content of the tweet or by the identity of the user posting it. Our study finds that one can build a model that performs competitively by developing a comprehensive set of features derived from the content of the message that is going to be posted. In addition, our study suggests that features derived from the user's identity do not impact model performance, hence suggesting that it is especially the content of a post that triggers abusive replies rather than who the user is.
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