Detect All Abuse! Toward Universal Abusive Language Detection Models
Kunze Wang, Dong Lu, Caren Han, Siqu Long, Josiah Poon · 2020
Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years.Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain.In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains.Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user's linguistic behaviour.Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language.Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.The code can be downloaded from https://github.com/usydnlp/MACAS.