Challenge: This work sheds light on the theoretical and applied importance of dogwhistles in both NLP and computational social science.
Approach: They propose a typology of dogwhistles, curate a glossary of over 300 dogwhitles and analyze their usage in historical U.S. politicians’ speeches.
Outcome: The proposed model identifies dogwhistles and their meanings and shows that harmful content containing dogwhitles avoids toxicity detection.

Similar Papers

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

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Challenge: Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach: They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome: The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.
Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats (2025.acl-long)

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Challenge: Dog whistles are coded expressions with dual meanings that slip by content moderation filters . a new study finds that state-of-the-art systems fail to identify novel dog whistles .
Approach: They propose a task to find novel dog whistles in massive social media corpora . they use a strong baseline system that combines vector databases and Large Language Models to identify new dog whistle.
Outcome: The proposed system fails to identify dog whistles across three social media cases . it combines vector databases and Large Language Models to efficiently and effectively identify new dog whistle expressions.
Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles (2024.acl-long)

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Challenge: a dog whistle is a coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination.
Approach: They propose an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models.
Outcome: The proposed method allows disambiguation of dog whistles from standard speech using large language models.
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge (2021.naacl-main)

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Challenge: Cant is important for understanding advertising, comedies and dogwhistle politics . currently, there are very few resources available for the research of cant .
Approach: They propose a large and diverse dataset for creating and understanding cant from a computational linguistics perspective.
Outcome: The proposed dataset can be used to test word embedding similarity and pretrained language models.
Counterspeech Generation using Small Language Models (2026.acl-srw)

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Challenge: Social media use is growing annually with about 68.5% of the global population active on these platforms as of July 2025.
Approach: They evaluate SLMs ranging from 100 million to 3 billion parameters using simple prompting strategies as well as fine-tuning, combining automatic and robust human evaluations.
Outcome: The proposed models generate relevant, coherent, and high-quality counterspeech, suggesting their suitability for efficient and responsible deployments.
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations (N18-5)

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Challenge: iii: 39 outstanding papers accepted for presentation at NAACL HLT 2018 in New Orleans, Louisiana . iv: 20 outstanding papers will be displayed during the conference .
Approach: iii: 39 outstanding papers accepted for presentation at NAACL HLT 2018 in new orleans, la . iv: demonstrations session is an opportunity for researchers and developers to present their systems .
Outcome: the demonstrations session at NAACL HLT 2018 in New Orleans, Louisiana, USA, received 39 outstanding papers .
Explain the Flag: Contextualizing Hate Speech Beyond Censorship (2026.findings-acl)

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Challenge: a hybrid approach to detect and explain hate speech combines large language models with vocabularies to detect hate speech in three languages . authors: the spread of hate speech online has serious personal, social, and legal consequences . eu has launched initiatives to analyze, regulate, and counteract online hate speech, authors say .
Approach: They propose a hybrid approach that combines Large Language Models with vocabularies to detect hate speech in English, French, and Greek.
Outcome: The proposed approach outperforms baselines in English, French, and Greek . it uses large language models and vocabularies to detect and explain hate speech . human evaluation shows that the proposed approach is accurate and clear .
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2025.acl-demo)

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Challenge: ACL 2025 System Demonstration Track accepted 64 papers based on reviews . short-listed 7 papers for Best System Demo award .
Approach: the ACL 2025 System Demonstration Track is a conference for papers describing system demonstrations . the track received a record 187 submissions, of which 178 papers were valid with required materials .
Outcome: the ACL 2025 System Demonstration Track received 187 submissions . 178 papers were valid with required materials .
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts (N18-6)

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Challenge: NAACL-HLT tutorials are an opportunity for conference attendees to participate in a tutorial on a timely topic of importance to the field.
Approach: NAACL-HLT 2018 is a tutorial session for conference attendees to participate in . a total of 51 tutorial submissions were received, of which 6 were selected for presentation .
Outcome: the tutorials committee at the 2018 NAACL-HLT in New Orleans received 51 submissions . the six tutorials demonstrate the increasing relevance of NLP to a wide range of fields .

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