Hate speech online remains an understudied issue for marginalized communities, and has seen rising relevance, especially in the Global South, which includes developing societies with increasing internet penetration. In this paper, we aim to provide marginalized communities living in societies where the dominant language is low-resource with a privacy-preserving tool to protect themselves from hate speech on the internet by filtering offensive content in their native languages. Our contribution in this paper is twofold: 1) we release REACT (REsponsive hate speech datasets Across ConTexts), a collection of high-quality, culture-specific hate speech detection datasets comprising seven distinct target groups in eight low-resource languages, curated by experienced data collectors; 2) we propose a solution to few-shot hate speech detection utilizing federated learning (FL), a privacy-preserving and collaborative learning approach, to continuously improve a central model that exhibits robustness when tackling different target groups and languages. By keeping the training local to the users' devices, we ensure the privacy of the users' data while benefitting from the efficiency of federated learning. Furthermore, we personalize client models to target-specific training data and evaluate their performance. Our results indicate the effectiveness of FL across different target groups, whereas the benefits of personalization on few-shot learning are not clear.

翻译：在线仇恨言论对于边缘化社区而言仍是一个研究不足的问题，其重要性日益凸显，尤其是在互联网普及率不断提高的发展中社会所构成的全球南方地区。本文旨在为生活在主导语言资源匮乏社会中的边缘化群体提供一种隐私保护工具，通过过滤其母语中的冒犯性内容，使其免受网络仇恨言论的侵害。本文的贡献主要体现在两个方面：1）我们发布了REACT（跨语境响应式仇恨言论数据集），这是一套由经验丰富的数据采集者精心构建的高质量、文化特异性仇恨言论检测数据集，涵盖八种低资源语言中七个不同的目标群体；2）我们提出了一种利用联邦学习（FL）——一种隐私保护型协作学习方法——的少样本仇恨言论检测解决方案，通过持续改进中心模型，使其在处理不同目标群体和语言时表现出鲁棒性。通过将训练过程保持在用户设备本地，我们在保障用户数据隐私的同时，充分发挥联邦学习的高效性。此外，我们针对特定目标训练数据对客户端模型进行个性化定制，并评估其性能。实验结果表明，联邦学习在不同目标群体间均具有有效性，而个性化对少样本学习的增益效果尚不明确。