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COVID-19 Fake News Detection using Deep Learning Model

Author

Listed:
  • Mahabuba Akhter

    (Premier University)

  • Syed Md. Minhaz Hossain

    (Premier University
    Chittagong University of Engineering and Technology)

  • Rizma Sijana Nigar

    (Premier University)

  • Srabanti Paul

    (Premier University)

  • Khaleque Md. Aashiq Kamal

    (Premier University)

  • Anik Sen

    (Premier University
    Chittagong University of Engineering and Technology)

  • Iqbal H. Sarker

    (Chittagong University of Engineering and Technology)

Abstract

People may now receive and share information more quickly and easily than ever due to the widespread use of mobile networked devices. However, this can occasionally lead to the spread of false information. Such information is being disseminated widely, which may cause people to make incorrect decisions about potentially crucial topics. This occurred in 2020, the year of the fatal and extremely contagious Coronavirus Disease (COVID-19) outbreak. The spread of false information about COVID-19 on social media has already been labeled as an “infodemic” by the World Health Organization (WHO), causing serious difficulties for governments attempting to control the pandemic. Consequently, it is crucial to have a model for detecting fake news related to COVID-19. In this paper, we present an effective Convolutional Neural Network (CNN)-based deep learning model using word embedding. For selecting the best CNN architecture, we take into account the optimal values of model hyper-parameters using grid search. Further, for measuring the effectiveness of our proposed CNN model, various state-of-the-art machine learning algorithms are conducted for COVID-19 fake news detection. Among them, CNN outperforms with 96.19% mean accuracy, 95% mean F1-score, and 0.985 area under ROC curve (AUC).

Suggested Citation

  • Mahabuba Akhter & Syed Md. Minhaz Hossain & Rizma Sijana Nigar & Srabanti Paul & Khaleque Md. Aashiq Kamal & Anik Sen & Iqbal H. Sarker, 2024. "COVID-19 Fake News Detection using Deep Learning Model," Annals of Data Science, Springer, vol. 11(6), pages 2167-2198, December.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:6:d:10.1007_s40745-023-00507-y
    DOI: 10.1007/s40745-023-00507-y
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    References listed on IDEAS

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    1. Leila Sh. Krupenikova (Крупеникова Л.Ш.) & Vladimir I. Kurbatov (Курбатов В.И.), 2022. "Big Data: New Organizational Opportunities And Social Risks [Big Data: Новые Организационные Возможности И Социальные Риски]," State and Municipal Management Scholar Notes, Russian Presidential Academy of National Economy and Public Administration, vol. 2, pages 247-251.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. Yoon Mi Hong & Sheng Yu Wang, 2022. "How is Big Data Changing Economic Research Paradigms?," Journal of Management World, Academia Publishing Group, vol. 2022(3), pages 40-55.
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