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While extensive popularity of online social media platforms has madeinformation dissemination faster, it has also resulted in widespread onlineabuse of different types like hate speech, offensive language, sexist andracist opinions, etc. Detection and curtailment of such abusive content iscritical for avoiding its psychological impact on victim communities, andthereby preventing hate crimes. Previous works have focused on classifying userposts into various forms of abusive behavior. But there has hardly been anyfocus on estimating the severity of abuse and the target. In this paper, wepresent a first of the kind dataset with 7601 posts from Gab which looks atonline abuse from the perspective of presence of abuse, severity and target ofabusive behavior. We also propose a system to address these tasks, obtaining anaccuracy of ~80% for abuse presence, ~82% for abuse target prediction, and ~65%for abuse severity prediction.

AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts