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

• URL: http://arxiv.org/abs/2010.00038v2
• Date: Thu, 8 Oct 2020 17:42:33 GMT
• Title: AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts
• Authors: Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, Ponnurangam Kumaraguru
• Abstract summary: 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.
• Score: 19.32095911241636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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