Hate Speech Detection in Turkish and Arabic Languages: A Comprehensive Study

2026-06-30

AI Summary

This study addresses the surge of online hate speech in Turkish and Arabic, particularly concerning sensitive topics such as refugees, religion, and ethnicity. To tackle this challenge, the authors construct the first fine-grained multilingual hate speech dataset encompassing five major themes in Turkish and refugee-related discourse in Arabic. They further propose a unified BERT-based multi-task learning framework that simultaneously performs hate category classification, intensity regression, target identification, and key span localization. The model achieves state-of-the-art performance across multiple subtasks, significantly enhancing both detection accuracy and semantic understanding of hate content in these low-resource languages.

Abstract

Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing. Societies grappling with this issue, particularly when hate speech targets specific groups based on religion, race, ethnicity, culture, nationality, or migration status, face the challenge of balancing freedom of expression with the need for effective content moderation on widely used online platforms. In response to this challenge, we introduce a comprehensive hate speech dataset covering five distinct topics in Turkish: refugees, the Israel-Palestine conflict, anti-Greek sentiment in Turkey, ethnic or religious communities (Alevis, Armenians, Arabs, Jews, and Kurds), and LGBTI+, alongside one topic in Arabic (refugees). In addition, we develop state-of-the-art BERT-based models to address multiple dimensions of hate speech analysis, including hate category classification, hate intensity prediction, target identification, and hate speech span detection, enabling a comprehensive understanding of hateful content in online discourse.

Problem

• hate speech
• content moderation
• minority violence
• freedom of expression
• online platforms

Innovation

• hate speech detection
• multilingual dataset
• BERT-based models
• hate intensity prediction
• target identification