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Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics, Findings of ACL
Subtitle of host publicationEMNLP 2021
EditorsMarie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-Tau Yih
PublisherAssociation for Computational Linguistics (ACL)
Pages3374-3385
Number of pages12
ISBN (Electronic)9781955917100
Published2021
Event2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 - Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Publication series

NameFindings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021

Conference

Conference2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Period7/11/202111/11/2021

Bibliographical note

Publisher Copyright: © 2021 Association for Computational Linguistics.

King's Authors

Abstract

Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms. The psychological effects of abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abusive language detection in the field of NLP. In this position paper, we discuss the role that modeling of users and online communities plays in abuse detection. Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language. We then explore the ethical challenges of incorporating user and community information, laying out considerations to guide future research. Finally, we address the topic of explainability in abusive language detection, proposing properties that an explainable method should aim to exhibit. We describe how user and community information can facilitate the realization of these properties and discuss the effective operationalization of explainability in view of the properties.

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