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

TL;DR

This paper introduces AbuseAnalyzer, a system for detecting online abuse, estimating its severity, and identifying targets in Gab posts, supported by a new dataset and achieving around 80% accuracy in key tasks.

Contribution

It presents the first dataset focusing on abuse presence, severity, and target prediction in Gab posts, along with a novel system for these tasks.

Findings

Achieved ~80% accuracy in abuse presence detection

Achieved ~82% accuracy in abuse target prediction

Achieved ~65% accuracy in abuse severity prediction

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.