A Federated Approach for Hate Speech Detection
It addresses privacy issues for social media content moderation, though it is incremental as it adapts an existing method to a new domain.
The paper tackled privacy concerns in hate speech detection by applying federated machine learning, achieving up to a 6.81% improvement in F1-score.
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.