mlsanityData to 14 Oct 2026

Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats

arXiv 2412.12072 ↗First posted 16 Dec 2024cs.CLcited by 6

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Abstract (arXiv)

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 novel dog whistles in massive social media corpora. We find that state-of-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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 21 of its 66 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

21 of 66 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  6. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  7. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  8. 2020Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detectioncited by 221
  9. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 217
  10. 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
  11. 2017Automated Phrase Mining from Massive Text Corporacited by 70
  12. 2023From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Modelscited by 26
9 more
  1. 2021Self-Supervised Euphemism Detection and Identification for Content Moderationcited by 25
  2. 2024Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detectioncited by 25
  3. 2017Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approachcited by 17
  4. 2021Euphemistic Phrase Detection by Masked Language Modelcited by 13
  5. 2022A Report on the Euphemisms Detection Shared Taskcited by 7
  6. 2024Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistlescited by 7
  7. 2017Surfacing contextual hate speech words within social mediacited by 6
  8. 2022Exploring Euphemism Detection in Few-Shot and Zero-Shot Settingscited by 5
  9. 2019Learning to Decipher Hate Symbolscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026From sunblock to softblock: Analyzing the correlates of neology in published writing and on social mediacited by 0
  2. 2026Multimodal QUD: Inquisitive Questions from Scientific Figurescited by 0
  3. 2026Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detectioncited by 0
  4. 2026Evaluating Large Language Models for Antisemitic Incident Classificationcited by 0
  5. 2026SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderationcited by 0
  6. 2026MUCnoHARM@GermEval Shared Task 2026: Retrieval-based In-Context Learning for Defamatory Offences, and Where It Falls Shortcited by 0