Adding citations

README.md

@@ -61,6 +61,8 @@ We developed an approach for word-sense disambiguation of dog whistles from stan
 
 ## Dataset Schema ##
 
+> 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/).
+
 | <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> |
@@ -76,4 +78,34 @@ We developed an approach for word-sense disambiguation of dog whistles from stan
 | <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/).
+
+# 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.",
+}
+```