A Federated Approach for Hate Speech Detection

Jay Gala, Deep Gandhi, Jash Mehta, Zeerak Talat


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.
Anthology ID:
2023.eacl-main.237
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3248–3259
Language:
URL:
https://aclanthology.org/2023.eacl-main.237
DOI:
10.18653/v1/2023.eacl-main.237
Bibkey:
Cite (ACL):
Jay Gala, Deep Gandhi, Jash Mehta, and Zeerak Talat. 2023. A Federated Approach for Hate Speech Detection. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3248–3259, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
A Federated Approach for Hate Speech Detection (Gala et al., EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-main.237.pdf
Video:
 https://aclanthology.org/2023.eacl-main.237.mp4