Context-Aware Attention for Understanding Twitter Abuse

Abstract

The original goal of any social media platform is to facilitate users to indulge in healthy and meaningful conversations. But more often than not, it has been found that it becomes an avenue for wanton attacks. We want to alleviate this issue and hence we try to provide a detailed analysis of how abusive behavior can be monitored in Twitter. The complexity of the natural language constructs makes this task challenging. We show how applying contextual attention to Long Short Term Memory networks help us give near state of art results on multiple benchmarks abuse detection data sets from Twitter.

The full published version of this work is available at: \url{https://www.aclweb.org/anthology/W19-3508/}. Please use the version published in the ACL anthology for citation purposes

References in corpus (2)

• Attention is not Explanation
• Ex Machina: Personal Attacks Seen at Scale

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• Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention
• Toxicity Detection can be Sensitive to the Conversational Context
• MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection