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From Machine Learning to Deep Learning for Detecting Abusive Messages in Arabic Social Media: Survey and Challenges

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

Abstract

The pervasiveness of social networks in recent years has revolutionized the way we communicate. The chance is now opened up for every person to freely and anonymously share his thoughts, opinions and ideas in a real-time manner. However, social media platforms are not always considered as a safe environment due to the increasing propagation of abusive messages that severely impact the community as a whole. The rapid detection of abusive messages remains a challenge for social platforms not only because of the harm it may cause to the users but also because of its impact on the quality of service they provide. Furthermore, the detection task proves to be more difficult when contents are generated in a specific language known by its complexity, richness and specificities like the Arabic language. The aim of this paper is to provide a comprehensive review of the existing approaches for detecting abusive messages from social media in the Arabic language. These approaches extend from the use of traditional machine learning to the incorporation of the latest deep learning architectures. Additionally, a background on abusive messages and Arabic language specificities will be presented. Finally, challenges are described for better analysis and identification of the future directions.

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Author information

Authors and Affiliations

  1. National School of Computer Science, Manouba University, Manouba, Tunisia

    Salma Abid Azzi & Chiraz Ben Othmane Zribi

Authors
  1. Salma Abid Azzi
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  2. Chiraz Ben Othmane Zribi
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Corresponding author

Correspondence to Salma Abid Azzi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Azzi, S.A., Zribi, C.B.O. (2021). From Machine Learning to Deep Learning for Detecting Abusive Messages in Arabic Social Media: Survey and Challenges. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_38

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