@inproceedings{verma-etal-2024-community,
    title = "A Community-Centric Perspective for Characterizing and Detecting Anti-{A}sian Violence-Provoking Speech",
    author = "Verma, Gaurav  and
      Grover, Rynaa  and
      Zhou, Jiawei  and
      Mathew, Binny  and
      Kraemer, Jordan  and
      Choudhury, Munmun  and
      Kumar, Srijan",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.acl-long.684/",
    doi = "10.18653/v1/2024.acl-long.684",
    pages = "12672--12684",
    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 {\textasciitilde}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."
}