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An optimized Arabic cyberbullying detection approach based on genetic algorithms.

Aya M Eissa1, Shawkat K Guirguis2, Magda M Madbouly3

  • 1Department of IT, Institute of Graduate Studies and Research, Alexandria University, Alexandria, Egypt. ayamohammed@alexu.edu.eg.

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|November 4, 2025
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This study introduces a Genetic Algorithm (GA) to enhance Arabic cyberbullying detection. The GA feature selection significantly improves detection accuracy and reduces processing time for online harmful content.

Keywords:
Arabic cyberbullying detectionFeature selectionGenetic algorithm

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Social Computing

Background:

  • Cyberbullying poses a significant threat on digital platforms, causing psychological harm.
  • Detecting cyberbullying in Arabic is challenging due to dialectal variations, informal language, and contextual nuances.
  • Conventional tools struggle with the complexities of Arabic cyberbullying identification.

Purpose of the Study:

  • To enhance Arabic cyberbullying detection mechanisms.
  • To introduce a feature-selection strategy using a Genetic Algorithm (GA).
  • To improve the accuracy and efficiency of identifying harmful online content in Arabic.

Main Methods:

  • Utilized a Genetic Algorithm (GA) for feature selection on a corpus of 46,000 Arabic Instagram comments.
  • Applied GA to reduce feature space, preserving semantic structures and removing noise.
  • Evaluated four classifiers with GA-driven feature selection.

Main Results:

  • The GA feature selector reduced the feature space by approximately 50%.
  • GA-driven selection improved F1-scores by 3.45-14.96% across classifiers.
  • Classification time was reduced by a factor of 2.32-12, indicating significant efficiency gains.

Conclusions:

  • Genetic-feature optimization enhances precision and significantly improves runtime for cyberbullying detection models.
  • The proposed method enables scalable and context-sensitive detection of harmful language in Arabic.
  • This approach is beneficial for morphologically rich languages requiring nuanced content analysis.