A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities

ACL ARR 2025 May Submission198 Authors

Abstract:

In this paper, we aim to provide marginalized communities in societies where the dominant language is low-resource with a privacy-preserving tool to protect themselves from online hate speech by filtering offensive content in their native languages.
Our contributions are twofold: 1) we release REACT (REsponsive hate speech datasets Across ConTexts, a collection of high-quality, culture-specific hate speech detection datasets comprising multiple target groups and low-resource languages, curated by experienced data collectors; 2) we propose a few-shot hate speech detection approach based on federated learning (FL), a privacy-preserving method for collaboratively training a central model that exhibits robustness when tackling different target groups and languages. By keeping training local to user devices, we ensure data privacy while leveraging the collective learning benefits of FL. Furthermore, we explore personalized client models tailored to specific target groups and evaluate their performance. Our findings indicate the overall effectiveness of FL across different target groups, and point to personalization as a promising direction.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: hate speech detection, data-efficient training, multilingualism, NLP datasets, evaluation

Contribution Types: Approaches to low-resource settings, Data resources

Languages Studied: Afrikaans, Ukrainian, Russian, Korean

Submission Number: 198