  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.
