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
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- Making FETCH! Happen: Finding Emergent Dog Whistles Through Common HabitatsKuleen Sasse, Carlos Alejandro Aguirre, Isabel Cachola, Sharon Levy 等ACL 2025 · 被引用 3 次
- EMODIS: A Benchmark for Context-Dependent Emoji Disambiguation in Large Language ModelsJiacheng Huang, Ning Yu, Xiaoyin YiAAAI 2026
- CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory DynamicsMing-Bin Chen, Jey Han Lau, Lea FrermannACL 2026
它引用的顶会 Paper11
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Latent Hatred: A Benchmark for Understanding Implicit Hate SpeechMai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi 等EMNLP 2021 · 被引用 159 次
- Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph InformationMichele Bevilacqua, Roberto NavigliACL 2020 · 被引用 145 次
- With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense DisambiguationBianca Scarlini, Tommaso Pasini, Roberto NavigliEMNLP 2020 · 被引用 95 次
- ConSeC: Word Sense Disambiguation as Continuous Sense ComprehensionEdoardo Barba, Luigi Procopio, Roberto NavigliEMNLP 2021 · 被引用 60 次
相关 Paper
- From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language ModelsJulia Mendelsohn, Ronan Le Bras, Yejin Choi, Maarten SapACL 2023 · 被引用 14 次
- Self-Supervised Euphemism Detection and Identification for Content ModerationWanzheng Zhu, Hongyu Gong, Rohan Bansal, Zachary Weinberg 等S&P 2021 · 被引用 56 次
- Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is OffensiveTharindu Cyril Weerasooriya, Sujan Dutta, Tharindu Ranasinghe, Marcos Zampieri 等EMNLP 2023 · 被引用 12 次
- PrivSniffer: Graph-based Contextual Privacy Leakage Detection for User-Generated TextsHangyu Ye, Liyao Xiang, Naixuan Huang, Dongyue Yu 等WWW 2026
- HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate CampaignsXinyue Shen, Yixin Wu, Yiting Qu, Michael Backes 等USENIX Security 2025
