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Arabic Hate Speech Detection Using Different Machine Learning Approach

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Advances on Intelligent Computing and Data Science (ICACIn 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 179))

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Abstract

Hate speech is defined as an expression that targets an individual or community on the aspects like religion, sexual orientation, race, political opinion, and origin. Recently, hate speech on social media especially in the Arabic language has been exponentially increased and led to severe causes. Various studies had been conducted on social media platforms adopted by people to broadcast their opinions. This work aims to develop a model that is able to handle detection and classification of Arabic hate speech and offensive language. The experiments are carried out in using various machine learning (ML) and deep learning (DL) models. In this work, Arabic Hate Speech Detection (AHSD) model is proposed which composed of pre-processing, feature extraction, detection, and classification to identify hate speech on the Arabic benchmark dataset. The proposed model shows improved results. The transfer learning approach model exhibits superior performance compared to all other ML models in terms of accuracy, precision, recall, and F1 scores, achieving improvements of 84%, 79%, 80%, and 79%, respectively.

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Notes

  1. 1.

    https://www.pewresearch.org/religion/2009/10/07/

  2. 2.

    https://sites.google.com/view/arabichate2022/home.

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Author information

Authors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore, 570006, India

    Abdullah Y. Muaad, J. (Jayappa Davanagere) Hanumanthappa & Channabasava Chola

  2. IT Department, Sana’a Community College, Sana’a, 5695, Yemen

    Abdullah Y. Muaad

  3. Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, 570006, Karnataka, India

    S. P. Shiva Prakash

  4. Department of Computer Science, Saba’a Region University, Mareb, Yemen

    Mohammed Al-Sarem

  5. College of Computer Science and Engineering, Taibah University, Medina, 42353, Saudi Arabia

    Mohammed Al-Sarem & Fahad Ghabban

  6. Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, Kerala, India

    J. V. Bibal Benifa & Channabasava Chola

Authors
  1. Abdullah Y. Muaad
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  2. J. (Jayappa Davanagere) Hanumanthappa
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  3. S. P. Shiva Prakash
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  4. Mohammed Al-Sarem
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  5. Fahad Ghabban
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  6. J. V. Bibal Benifa
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  7. Channabasava Chola
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Corresponding author

Correspondence to Abdullah Y. Muaad .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. Information Systems Department, School of Computing, Universiti Utara Malaysia (UUM), Sintok, Malaysia

    Fathey Mohammed

  3. Faculty of Sciences, University of Hassan II Casablanca, Casablanca, Morocco

    Errais Mohammed

  4. Department of Computing and Technology, Nottingham Trent University, Nottingham, UK

    Tawfik Al-Hadhrami

  5. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem

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Muaad, A.Y. et al. (2023). Arabic Hate Speech Detection Using Different Machine Learning Approach. In: Saeed, F., Mohammed, F., Mohammed, E., Al-Hadhrami, T., Al-Sarem, M. (eds) Advances on Intelligent Computing and Data Science. ICACIn 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-031-36258-3_38

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