---
language:
- en
license: cc-by-sa-4.0
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: dog_whistle
    dtype: string
  - name: dog_whistle_root
    dtype: string
  - name: ingroup
    dtype: string
  - name: content
    dtype: string
  - name: date
    dtype: string
  - name: speaker
    dtype: string
  - name: chamber
    dtype: string
  - name: subreddit
    dtype: string
  - name: source
    dtype: string
  - name: definition
    dtype: string
  - name: type
    dtype: string
  - name: party
    dtype: string
  splits:
  - name: train
    num_bytes: 6286465
    num_examples: 16258
  download_size: 2726316
  dataset_size: 6286465
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Silent Signals #
**A dataset of dog whistle use cases in informal and formal discourse.** 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.

We developed an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs), and leveraged this technique to create a dataset of 16,550 high-confidence coded examples of dog whistles used in formal and informal communication. Silent Signals is the largest dataset of disambiguated dog whistle usage, created for applications in hate speech detection, neology, and political science.

<p style="color:red;">Please note, this dataset contains content that may be upsetting or offensive to some readers.</p>

**Published at ACL 2024!**

📄 **Paper Link** - [Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles](https://aclanthology.org/2024.acl-long.675/)<br>
💻 **Dataset webpage** - Coming soon 🚀

<!-- ![head_figure_large.png](https://cdn-uploads.huggingface.co/production/uploads/632d02054a4991e711591c34/m70hfQTN2Aw7t3Ilkga4u.png) -->
<centering><img src="https://cdn-uploads.huggingface.co/production/uploads/632d02054a4991e711591c34/m70hfQTN2Aw7t3Ilkga4u.png" alt="head_figure" width="400"/></centering>


## Dataset Schema ##

| <nobr>Field Name </nobr>| <nobr>Type</nobr> | <nobr>Example</nobr> | <nobr>Description</nobr> |
|:------------|:------|:---------|:-------------|
| <nobr>**dog_whistle**</nobr> | <nobr>str</nobr> | <nobr>"illegals"</nobr> | <nobr>Dog whistle word or term.</nobr> |
| <nobr>**dog_whistle_root**</nobr> | <nobr>str</nobr> | <nobr>"illegal immigrant"</nobr> | <nobr>The root form of the dog whistle,<br> as there could be multiple variations.</nobr> |
| <nobr>**ingroup**</nobr> | <nobr>str</nobr> | <nobr>"anti-Latino"</nobr> | <nobr>The community that uses the dog whistle.</nobr> |
| <nobr>**content**</nobr> | <nobr>str</nobr> | <nobr>"In my State of Virginia, the governor put a stop <br>to the independent audits that were finding <br>thousands of illegals on the roll."</nobr> | <nobr>Text containing the dog whistle.</nobr> |
| <nobr>**date**</nobr> | <nobr>str</nobr> | <nobr>"11/14/2016"</nobr> | <nobr>Date of comment, formatted as `mm/dd/yyyy`.</nobr> |
| <nobr>**speaker**</nobr> | <nobr>str</nobr> | <nobr>None</nobr> | <nobr>Speaker, included for U.S. Congressional speech <br>excerpts and Null for Reddit comments.</nobr> |
| <nobr>**chamber**</nobr> | <nobr>str</nobr> | <nobr>None</nobr> | <nobr>Chamber of Congress, 'S' for Senate, <br>'H' for House of Representatives, and <br>Null for Reddit comments.</nobr> |
| <nobr>**subreddit**</nobr> | <nobr>str</nobr> | <nobr>"The_Donald"</nobr> | <nobr>Subreddit where the comment was posted,<br> Null for Congressional data.</nobr> |
| <nobr>**source**</nobr> | <nobr>str</nobr> | <nobr>"PRAW API"</nobr> | <nobr>The source or method of data collection.</nobr> |
| <nobr>**definition**</nobr> | <nobr>str</nobr> | <nobr>"Latino, especially Mexican, immigrants <br>regardless of documentation."</nobr> | <nobr>Definition of the dog whistle, sourced from the <br>Allen AI Dog Whistle Glossary.</nobr> |
| <nobr>**type**</nobr> | <nobr>str</nobr> | <nobr>"Informal"</nobr> | <nobr>Type of content, formal or informal.</nobr> |
| <nobr>**party**</nobr> | <nobr>str</nobr> | <nobr>None</nobr> | <nobr>The political party affiliation of the speaker, <br>available only for U.S. Congressional excerpts.</nobr> |
> NOTE: The dog whistles terms and definitions that enabled this research and data collection were sourced from the [Allen AI Dogwhistle Glossary](https://dogwhistles.allen.ai/).

<br>
# Citations #

### MLA ###

Julia Kruk, Michela Marchini, Rijul Magu, Caleb Ziems, David Muchlinski, and Diyi Yang. 2024. Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12493–12509, Bangkok, Thailand. Association for Computational Linguistics.

### Bibtex ###

```
@inproceedings{kruk-etal-2024-silent,
    title = "Silent Signals, Loud Impact: {LLM}s for Word-Sense Disambiguation of Coded Dog Whistles",
    author = "Kruk, Julia  and
      Marchini, Michela  and
      Magu, Rijul  and
      Ziems, Caleb  and
      Muchlinski, David  and
      Yang, Diyi",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.675",
    pages = "12493--12509",
    abstract = "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 high-confidence coded examples of dog whistles used in formal and informal communication. Silent Signals is the largest dataset of disambiguated dog whistle usage, created for applications in hate speech detection, neology, and political science.",
}
```