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        <title>A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech</title>
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    <abstract>Violence-provoking speech – speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the COVID-19 pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech (F₁ = 0.89), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task (F₁ = 0.69). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises.</abstract>
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%0 Conference Proceedings
%T A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech
%A Verma, Gaurav
%A Grover, Rynaa
%A Zhou, Jiawei
%A Mathew, Binny
%A Kraemer, Jordan
%A Choudhury, Munmun
%A Kumar, Srijan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F verma-etal-2024-community
%X Violence-provoking speech – speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the COVID-19 pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech (F₁ = 0.89), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task (F₁ = 0.69). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises.
%U https://aclanthology.org/2024.acl-long.684
%P 12672-12684

Markdown (Informal)

[A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech](https://aclanthology.org/2024.acl-long.684) (Verma et al., ACL 2024)

• A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech (Verma et al., ACL 2024)

ACL

• Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun Choudhury, and Srijan Kumar. 2024. A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12672–12684, Bangkok, Thailand. Association for Computational Linguistics.