Year: 2025
Type: conference-paper
Abstract: WARNING: This paper contains content that maybe upsetting or offensive to some readers.Dog whistles are coded expressions with dual meanings: one intended for the general public (outgroup) and another that conveys a specific message to an intended audience (ingroup).Often, these expressions are used to convey controversial political opinions while maintaining plausible deniability and slip by content moderation filters.Identification of dog whistles relies on curated lexicons, which have trouble keeping up to date.We introduce FETCH!, a task for finding emergent dog whistles in massive social media corpora.We find that stateof-the-art systems fail to achieve meaningful results across three distinct social media case studies.We present EarShot, a strong baseline system that combines the strengths of vector databases and Large Language Models (LLMs) to efficiently and effectively identify new dog whistles.1 Scenario Filtering Model Keyword Extraction Model Threshold Max n-grams Prec DPR F 0.5 Balanced RoBERTA R4 KeyBERT 50 1 14.81 1.65 5.70 LLaMa 13B KeyBERT 100 1 11.43 1.65 5.22 Synthetic RoBERTA R4 KeyBERT 800 1 14.86 13.45 14.55 LLaMA 8B KeyBERT 400 1 19.13 7.14 14.32
Authors: Kuleen Sasse, Carlos Alejandro Aguirre, Isabel Cachola, Sharon Levy, Mark Dredze
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412945674