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.
Anthology ID:
2024.acl-long.675
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12493–12509
Language:
URL:
https://aclanthology.org/2024.acl-long.675
DOI:
Bibkey:
Cite (ACL):
Julia Kruk, Michela Marchini, Rijul Magu, Caleb Ziems, David Muchlinski, and Diyi Yang. 2024. Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12493–12509, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles (Kruk et al., ACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.675.pdf
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