A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities
MCML Authors
Axel Wisiorek
Dr.
Antonis Maronikolakis
* Former Member
Hinrich Schütze
Prof. Dr.
Principal Investigator
Abstract
Axel Wisiorek
Dr.
Antonis Maronikolakis
* Former Member
Hinrich Schütze
Prof. Dr.
Principal Investigator
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.
inproceedings
MRL @EMNLP 2025
5th Multilingual Representation Learning Workshop at the Conference on Empirical Methods in Natural Language Processing. Suzhou, China, Nov 04-09, 2025. To be published. Preprint available.Authors
H. Ye • A. Wisiorek • A. Maronikolakis • Ö. Alaçam • H. SchützeLinks
Research Area
BibTeXKey: YWM+25