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A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection

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  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon)

  • Abdelghani Dahou

    (Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria)

  • Dina Ahmed Orabi

    (Faculty of Media Production, Galala University, Suez 435611, Egypt)

  • Samah Alshathri

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Eman M. Soliman

    (Faculty of Media Production, Galala University, Suez 435611, Egypt)

  • Ahmed A. Ewees

    (Department of Computer, Damietta University, Damietta 34517, Egypt)

Abstract

The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures.

Suggested Citation

  • Mohamed Abd Elaziz & Abdelghani Dahou & Dina Ahmed Orabi & Samah Alshathri & Eman M. Soliman & Ahmed A. Ewees, 2023. "A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:258-:d:1024382
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    References listed on IDEAS

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    1. Hadeer Adel & Abdelghani Dahou & Alhassan Mabrouk & Mohamed Abd Elaziz & Mohammed Kayed & Ibrahim Mahmoud El-Henawy & Samah Alshathri & Abdelmgeid Amin Ali, 2022. "Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm," Mathematics, MDPI, vol. 10(3), pages 1-22, January.
    2. Umaru A. Pate & Danjuma Gambo & Adamkolo Mohammed Ibrahim, 2019. "The Impact of Fake News and the Emerging Post-Truth Political Era on Nigerian Polity: A Review of Literature," Studies in Media and Communication, Redfame publishing, vol. 7(1), pages 21-29, June.
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