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VIDEO DOI: https://doi.org/10.48448/95ay-xn73

poster

ACL 2024

August 13, 2024

Bangkok, Thailand

A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech

keywords:

community-centric approaches

violence-provoking speech

characterization

detection

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 ($F_1$ = 0.89), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task ($F_1$ = 0.69). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises.

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Transcript English (automatic)

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