Detect All Abuse! Toward Universal Abusive Language Detection Models

Proceedings of the 28th International Conference on Computational Linguistics, pages 6366–6376 Barcelona, Spain (Online), December 8-13, 2020 6366 Detect All Abuse! Toward Universal Abusive Language Detection Models Kunze Wang1*, Dong Lu1*, Soyeon Caren Han2**, Siqu Long1, Josiah Poon2 The University of Sydney, Sydney, Australia 1{kwan4418,dolu4031,slon6753}@uni.sydney.edu.au 2{caren.han,josiah.poon}@sydney.edu.au Abstract 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. 1 Introduction Abusive language in online communities has become a significant societal problem (Nobata et al., 2016) and online abusive language detection (ALD) aims to identify any type of insult, vulgarity, or profanity that debases a target or group online. It is not only limited to detecting offensive language (Razavi et al., 2010), cyberbullying (Xu et al., 2012), and hate speech (Djuric et al., 2015), but also to more nebulous or implicit forms of abuse. Many social media companies and researchers have utilised multiple resources, including machine learning, human reviewers and lexicon-based text analytics to detect abusive language (Waseem, 2016; Qian et al., 2018). However, none of them can perfectly resolve the ALD task because of the difficulties of moderating user content and in classifying ambiguous posts (Metz and Issac, 2019). On the technical side, previous ALD models were developed on only a few subtasks (e.g. hate speech, racism, sexism) in a single domain (like Twitter), and each specialised model is not successfully transferable to general ALD in different online communities. Our research question is, “What would be the best generic ALD model that can be used for different types of abusive language detection sub-tasks and in different online communities?” To solve this, we found that Waseem et al. (2017) reviewed the existing online abusive language detection literature, and defined a generic abusive language typology that can encompass the targets of a wide range of abusive language subtasks in different types of domain. The typology is categorised in the following two aspects: 1) Target aspect: The abuse can be directed towards either a) a specific individual/entity or b) a generalised group. This is an essential sociological distinction as the latter refers to a whole category of people, like a race or gender, rather …

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