While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ~80% for abuse presence, ~82% for abuse target detection, and ~64% for abuse severity detection.

该论文提出了一种基于“Gab”数据集的在线滥用行为检测和评估方法，该方法可以确定滥用性内容的存在性，严重性和目标，并可达到80％的存在性检测准确率，82％的目标检测准确率以及65％的滥用严重性预测准确率

AbuseAnalyzer: 用于Gab帖子的滥用检测、严重程度和目标预测