mlsanityData to 14 Oct 2026

A Federated Approach for Hate Speech Detection

arXiv 2302.09243 ↗First posted 18 Feb 2023cs.LG cs.AI cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 53 references that are in the map. First posted on arXiv 18 Feb 2023.

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26 of 53 references.

References · 26

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14 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communitiescited by 2