  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 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. The resources related
to the study are available at
https://claws-lab.github.io/violence-provoking-speech/.
