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
Abstract
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 high-confidence coded examples of dog whistles used in formal and informal communication. Silent Signals is the largest dataset of disambiguated dog whistle usage, created for applications in hate speech detection, neology, and political science.
BibTeX
@inproceedings{kruk-etal-2024-silent,
title = "Silent Signals, Loud Impact: {LLM}s for Word-Sense Disambiguation of Coded Dog Whistles",
author = "Kruk, Julia and
Marchini, Michela and
Magu, Rijul and
Ziems, Caleb and
Muchlinski, David and
Yang, Diyi",
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://aclanthology.org/2024.acl-long.675/",
doi = "10.18653/v1/2024.acl-long.675",
pages = "12493--12509"
}