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

TL;DR

This paper introduces REACT, a collection of hate speech datasets for marginalized communities in low-resource languages, and proposes a federated learning approach for privacy-preserving, few-shot hate speech detection that adapts to specific groups.

Contribution

It provides the first culture-specific hate speech datasets for marginalized groups and develops a federated, few-shot detection method with personalized models for improved accuracy.

Findings

Federated learning shows robustness across diverse target groups.

Personalized models outperform generic models in hate speech detection.

REACT datasets enable research in low-resource, culture-specific hate speech detection.

Abstract

Hate speech online remains an understudied issue for marginalized communities, particularly in the Global South, which includes developing societies with increasing internet penetration. 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.