Detect All Abuse! Toward Universal Abusive Language Detection Models

By Kunze Wang, Soyeon Caren Han, Siqu Long, Josiah Poon. 2020

26 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Much of this focuses on human-produced content on social media platforms, such as Twitter (e.g. Waseem & Hovy, 2016; Wang et al., 2020; Zampieri et al., 2019; 2020), Facebook (Glava ˇ s et al., 2020; Zampieri et al., 2020), or Reddit (Han & Tsvetkov, 2020; Zampieri et al., 2020).”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “…to detect problematic content – often centred on content written by humans on social media platforms, such as Twitter (e.g. Waseem and Hovy, 2016; Wang et al., 2020; Zampieri et al., 2019, 2020; Zhang et al., 2020), Facebook (Glavaš et al., 2020; Zampieri et al., 2020), or Reddit (Han and…”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “For such a real world noise setting , we take 180 examples from the Twitter validation dataset accessible via ParlAI.”
Towards multidomain and multilingual abusive language detection: a survey (2021, methodology): “Some other studies adopted several neural-based models, including convolutional neural networks (CNN) [75, 141], long short-term memory (LSTM) [8, 75, 92, 94, 145], bidirectional LSTM (Bi-LSTM) [115], and gated recurrent unit (GRU) [27].”
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (2021, methodology): “Work has also been undertaken on data from comments on news media (Glavaš et al., 2020; Razo and Kübler, 2020; Wang et al., 2020; Zampieri et al., 2020), chatrooms and discussion forums (Gao et al., 2020; Wang et al., 2020), Wikipedia discussions (Fortuna et al., 2020; Glavaš et al., 2020; Gröndahl…”