A Federated Approach for Hate Speech Detection

Authors

Jay Gala, AI4Bharat
Deep Gandhi, University of Alberta
Jash Mehta, Georgia Institute of Technology
Zeerak Talat, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Abstract

Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied. The majority of research has focused on centralised machine learning infrastructures which risk leaking data. In this paper, we show that using federated machine learning can help address privacy the concerns that are inherent to hate speech detection while obtaining up to 6.81% improvement in terms of F1-score.

First Page

3240

Last Page

3251

Publication Date

1-1-2023

Recommended Citation

J. Gala et al., "A Federated Approach for Hate Speech Detection," EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 3240 - 3251, Jan 2023.