Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles

• URL: http://arxiv.org/abs/2406.06840v2
• Date: Tue, 18 Jun 2024 19:11:11 GMT
• Title: Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles
• Authors: Julia Kruk, Michela Marchini, Rijul Magu, Caleb Ziems, David Muchlinski, Diyi Yang,
• Abstract summary: 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. We present an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs) We leverage this technique to create a dataset of 16,550 high-confidence coded examples of dog whistles used in formal and informal communication.
• Score: 47.61526125774749
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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. The dataset can be found at https://huggingface.co/datasets/SALT-NLP/silent_signals.

Related papers

• Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales [15.458557611029518]
  Social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions. There arises a need to automatically identify and flag instances of hate speech. We propose to use state-of-the-art Large Language Models (LLMs) to extract features in the form of rationales from the input text.
  arXiv  Detail & Related papers  (2024-03-19T03:22:35Z)
• Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation [1.9506923346234724]
  We propose a data augmentation approach that addresses the problem of lack of data for online hate speech detection in limited data contexts. We present three methods to synthesize new examples of hate speech data in a target language that retains the hate sentiment in the original examples but transfers the hate targets. Our findings show that a model trained on synthetic data performs comparably to, and in some cases outperforms, a model trained only on the samples available in the target domain.
  arXiv  Detail & Related papers  (2023-10-04T15:10:06Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models [73.25963871034858]
  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, and analyze their usage in historical U.S. politicians' speeches. We show that harmful content containing dogwhistles avoids toxicity detection, highlighting online risks of such coded language.
  arXiv  Detail & Related papers  (2023-05-26T18:00:57Z)
• Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
  We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
  arXiv  Detail & Related papers  (2022-02-19T03:48:20Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model [0.5801044612920815]
  This paper investigates the feasibility of leveraging domain-specific word embedding in Bidirectional LSTM based deep model to automatically detect/classify hate speech. The experiments showed that domainspecific word embedding with the Bidirectional LSTM based deep model achieved a 93% f1-score while BERT achieved up to 96% f1-score on a combined balanced dataset from available hate speech datasets.
  arXiv  Detail & Related papers  (2021-11-02T11:42:54Z)
• Speaker De-identification System using Autoencoders and Adversarial Training [58.720142291102135]
  We propose a speaker de-identification system based on adversarial training and autoencoders. Experimental results show that combining adversarial learning and autoencoders increase the equal error rate of a speaker verification system.
  arXiv  Detail & Related papers  (2020-11-09T19:22:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.