论文标题
滥用分析仪：滥用检测，严重程度和目标的目标预测
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts
论文作者
论文摘要
尽管在线社交媒体平台的广泛流行使信息传播速度更快，但它也导致在线滥用仇恨言论，令人反感的语言，性别歧视和种族主义意见等不同类型的在线滥用。检测和削减这种虐待内容对于避免其心理对受害者的心理影响至关重要，从而避免了对受害者的影响，从而防止仇恨犯罪。以前的工作重点是将用户帖子分类为各种形式的虐待行为。但是，几乎没有任何重点是估计虐待的严重性和目标。在本文中，我们介绍了GAB的7601帖子中的第一个数据集，该数据集从存在滥用，严重性和虐待行为的目标的角度来关注在线滥用。我们还提出了一个解决这些任务的系统，获得了约80％的滥用能力，约82％的滥用目标预测，约为65％的滥用严重性预测。
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 prediction, and ~65% for abuse severity prediction.
