Enriching Abusive Language Detection with Community Context

Abstract

Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with nondominant perspectives is to add context around conversations. Previous research has leveraged user-and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research.Productive conversations containing slurs are common, and they take many forms (Hom, 2008). Research inspired by the #MeToo movement has focused on the detection of sexual harassment disclosures by victims (Deal et al., 2020), but this research has not been sufficiently integrated into the literature on abusive language detection. The distinction between actual sexist messages and messages calling out sexism is rarely addressed in the field (Chiril et al., 2020). A similar trend is seen with sarcasm. Humor and selfirony can be employed as coping mechanisms by victims of abuse (Garrick, 2006), yet they constitute frequent sources of error for state-of-theart classifiers (Vidgen et al., 2019). For example, the median toxicity score for language on transgendercirclejerk, a "parody [subreddit] for trans people", is as high as 90% (Kurrek

The Impact of Fake News on Traveling and Antisocial Behavior in Online Communities: Overview

“…The talk in [25] is approximately the part of modeling the client and the online community within the discovery of misuses. This paper highlights how the community setting can improve the classification that occurs within the discovery of damaging dialect [26,27].…”

“…The following are some of the techniques that have been used to address the issues that law enforcement has: This involves the employment of the criminal justice system and police to hold offenders responsible for their deeds and discourage others from repeating them. Social assistance entails offering assistance and services to people in danger of misbehavior, as well as to those who have been harmed by it in the community [26].…”

“…It is crucial to remember that no one approach will be sufficient to deal with all instances of antisocial and community abuse. The most efficient method of minimizing and eliminating these hazardous habits is probably to take a thorough and individualized approach that takes into account the unique requirements and conditions of each community [26,27].…”