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Recently, amidst growing legislation focus and public sensitivity to data security, researchers and social media operators are keen to develop effective privacy-preserving and multi-party collaborative methods, to keep early intervention against various crisis. In this study, we propose a novel multi-party collaborative hate speech detection mechanism (MC-HSD) as a macro-guidance to address the challenge. Under MC-HSD, we propose a novel HateFL-pro framework based on federated learning. We demonstrate its effectiveness via extensive experiments.

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Multi-party Collaborative Hate Speech Study on Social Media via Personalized Federated Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Junhao Yin,
  • Xiran Hu,
  • Geng Zhao,
  • Guochang Wang

摘要

Recently, amidst growing legislation focus and public sensitivity to data security, researchers and social media operators are keen to develop effective privacy-preserving and multi-party collaborative methods, to keep early intervention against various crisis. In this study, we propose a novel multi-party collaborative hate speech detection mechanism (MC-HSD) as a macro-guidance to address the challenge. Under MC-HSD, we propose a novel HateFL-pro framework based on federated learning. We demonstrate its effectiveness via extensive experiments.