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Cyberbullying Classification Methods for Arabic: A Systematic Review

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

Abstract

Across the globe, bullying and cyberbullying are present and have significant implications for people and communities. Over the last years, the number of research studies for cyberbullying classification in texts has grown exponentially, but most studies have been examined on English datasets. Most of the new methods in text classification are based on linguistic features of the text since the Arabic language has different rules and structures. This research intended to review articles about cyberbullying classification methods for Arabic texts and analyzed them The review includes the used methods, quality measuring performance, and details of the dataset (dialect, annotation method, source). This will guide future research by giving researchers a more consistent and compatible view on the topic. Three major databases were systematically searched for articles on related topics (Science Direct, IEEE, Google Scholar). Through a systematic study, nine research papers related to this topic are obtained. Results show that this topic’s work is still recent, and many methods have not yet been tested to figure out the best approach for classifying Arabic texts as cyberbullying. Also, the number of available datasets for cyberbullying classification in Arabic text is still limited compared to the English language. Therefore, generalizing the best method and dataset for classifying cyberbullying is so difficult. Furthermore, the majority of studies used Twitter for collecting the datasets. SVM is the most used classifier, Whereas CNN, is the most commonly used neural network for the cyberbullying classification.

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Acknowledgment

This work is a part of a thesis submitted in fulfilment of Ph.D. in Project Management, Faculty of Engineering & Information Technology at the British University in Dubai.

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Authors and Affiliations

  1. Higher College of Technology, Abu Dhabi, UAE

    Reem ALBayari

  2. Faculty of Engineering and IT, The British University in Dubai, Dubai, UAE

    Reem ALBayari & Sharif Abdullah

  3. School of Science, Engineering, and Environment, University of Salford, Salford, UK

    Said A. Salloum

  4. Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, UAE

    Said A. Salloum

Authors
  1. Reem ALBayari
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  2. Sharif Abdullah
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  3. Said A. Salloum
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Corresponding author

Correspondence to Said A. Salloum .

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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ALBayari, R., Abdullah, S., Salloum, S.A. (2021). Cyberbullying Classification Methods for Arabic: A Systematic Review. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_35

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