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As smartphones and social media usage grow among young people, particularly in Arab communities, the risk of encountering cyberbullying and harmful content increases. However, existing cyberbullying detection solutions are primarily tailored for English, leaving Arabic users underserved. This study addresses this gap by developing improved detection models for Arabic content. Through training various classifiers with annotated Arabic datasets, including traditional machine learning and deep learning techniques, this research aims to enhance the effectiveness of cyberbullying detection in Arabic online spaces, collected from three different platforms (Facebook, Twitter, and YouTube). Additionally, we conducted domain-specific data extraction from our existing datasets, focusing solely on political discourse. This was followed by testing the extracted data with deep learning algorithms. The results indicate that the proposed model outperforms other classifiers examined in the study. The overall enhancement achieved by the proposed model reaches an accuracy of 89% compared to the best-performing classifier.

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LLMs for Cyberbullying Detection in Political Social Media

  • 会议论文

  • 发表日期：

    2025年

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  • Sahar Ben Bechir,
  • Asma Mekki,
  • Mariem Ellouze,
  • Lamia Hadrich Belguith

摘要

As smartphones and social media usage grow among young people, particularly in Arab communities, the risk of encountering cyberbullying and harmful content increases. However, existing cyberbullying detection solutions are primarily tailored for English, leaving Arabic users underserved. This study addresses this gap by developing improved detection models for Arabic content. Through training various classifiers with annotated Arabic datasets, including traditional machine learning and deep learning techniques, this research aims to enhance the effectiveness of cyberbullying detection in Arabic online spaces, collected from three different platforms (Facebook, Twitter, and YouTube). Additionally, we conducted domain-specific data extraction from our existing datasets, focusing solely on political discourse. This was followed by testing the extracted data with deep learning algorithms. The results indicate that the proposed model outperforms other classifiers examined in the study. The overall enhancement achieved by the proposed model reaches an accuracy of 89% compared to the best-performing classifier.