ACL2024
Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles
Julia Kruk, Michela Marchini, Rijul Magu, Caleb Ziems, David Muchlinski, Diyi Yang
被引用 2 次
摘要
Warning: This paper contains content that may be upsetting or offensive to some readers. A dog whistle is a form of coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination. Dog whistling historically originated from United States politics, but in recent years has taken root in social media as a means of evading hate speech detection systems and maintaining plausible deniability. In this paper, we present an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs), and leverage this technique to create a dataset of 16,550 highconfidence coded examples of dog whistles used in formal and informal communication. Silent Signals 1 is the largest dataset of disambiguated dog whistle usage, created for applications in hate speech detection, neology, and political science.