# From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models

arXiv: https://arxiv.org/abs/2305.17174 · HTML: https://mlsanity.com/paper/2305.17174/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 65 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [HateCheck: Functional Tests for Hate Speech Detection Models](https://mlsanity.com/paper/2012.15606/) (2020)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018)
- [A Framework for the Computational Linguistic Analysis of Dehumanization](https://mlsanity.com/paper/2003.03014/) (2020)
- [Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities](https://mlsanity.com/paper/2011.08320/) (2020)
- [Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection](https://mlsanity.com/paper/2103.14916/) (2021)
- [Detecting the Hate Code on Social Media](https://mlsanity.com/paper/1703.05443/) (2017)
- [Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge](https://mlsanity.com/paper/2104.02704/) (2021)
- [Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate](https://mlsanity.com/paper/2106.05903/) (2021)
- [It's a Thin Line Between Love and Hate: Using the Echo in Modeling Dynamics of Racist Online Communities](https://mlsanity.com/paper/2012.01133/) (2020)
- [Learning to Decipher Hate Symbols](https://mlsanity.com/paper/1904.02418/) (2019)

## Built on by (most cited first, 26 of 26)

- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps](https://mlsanity.com/paper/2404.01651/) (2024)
- [Measurement in the Age of LLMs: An Application to Ideological Scaling](https://mlsanity.com/paper/2312.09203/) (2023)
- [Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale](https://mlsanity.com/paper/2505.23785/) (2025)
- [Rater Cohesion and Quality from a Vicarious Perspective](https://mlsanity.com/paper/2408.08411/) (2024)
- [Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats](https://mlsanity.com/paper/2412.12072/) (2024)
- [Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models](https://mlsanity.com/paper/2404.17985/) (2024)
- [Composable Interventions for Language Models](https://mlsanity.com/paper/2407.06483/) (2024)
- [Adaptively profiling models with task elicitation](https://mlsanity.com/paper/2503.01986/) (2025)
- [A large-scale evaluation of commonsense knowledge in humans and large language models](https://mlsanity.com/paper/2505.10309/) (2025)
- [IYKYK: Using language models to decode extremist cryptolects](https://mlsanity.com/paper/2506.05635/) (2025)
- ["Newspaper Eat" Means "Not Tasty": A Taxonomy and Benchmark for Coded Language in Real-World Chinese Online Reviews](https://mlsanity.com/paper/2601.19932/) (2026)
- [Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments](https://mlsanity.com/paper/2312.03726/) (2023)
- [Jointly modelling the evolution of social structure and language in online communities](https://mlsanity.com/paper/2409.19243/) (2024)
- [Inducing lexicons of in-group language with socio-temporal context](https://mlsanity.com/paper/2409.19257/) (2024)
- [Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities](https://mlsanity.com/paper/2312.03330/) (2023)
- [HebID: Detecting Social Identities in Hebrew-language Political Text](https://mlsanity.com/paper/2508.15483/) (2025)
- [Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms](https://mlsanity.com/paper/2604.23842/) (2026)
- [Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructure](https://mlsanity.com/paper/2604.25580/) (2026)
- [Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding](https://mlsanity.com/paper/2309.13561/) (2023)
- [DRInQ: Evaluating Conversational Implicature with Controlled Context Variation](https://mlsanity.com/paper/2605.24267/) (2026)
- [Evaluating Large Language Models for Antisemitic Incident Classification](https://mlsanity.com/paper/2607.04890/) (2026)
- [You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism](https://mlsanity.com/paper/2607.04945/) (2026)
- [Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations](https://mlsanity.com/paper/2607.14491/) (2026)
- [DeflectBench: A Benchmark for Evaluating Rhetorical Fallacy Generation in LLMs](https://mlsanity.com/paper/2608.26119/) (2026)
- [Finding Common Ground: Graded Communal Knowledge in Bluesky Starter Packs](https://mlsanity.com/paper/2609.19549/) (2026)

## Abstract (arXiv)

Dogwhistles are coded expressions that simultaneously convey one meaning to a broad audience and a second one, often hateful or provocative, to a narrow in-group; they are deployed to evade both political repercussions and algorithmic content moderation. For example, in the sentence 'we need to end the cosmopolitan experiment,' the word 'cosmopolitan' likely means 'worldly' to many, but secretly means 'Jewish' to a select few. We present the first large-scale computational investigation of dogwhistles. We develop a typology of dogwhistles, curate the largest-to-date glossary of over 300 dogwhistles with rich contextual information and examples, and analyze their usage in historical U.S. politicians' speeches. We then assess whether a large language model (GPT-3) can identify dogwhistles and their meanings, and find that GPT-3's performance varies widely across types of dogwhistles and targeted groups. Finally, we show that harmful content containing dogwhistles avoids toxicity detection, highlighting online risks of such coded language. This work sheds light on the theoretical and applied importance of dogwhistles in both NLP and computational social science, and provides resources for future research in modeling dogwhistles and mitigating their online harms.