COVID-19 Fake News Detection using Deep Learning Model

Mahabuba Akhter (), Syed Md. Minhaz Hossain (), Rizma Sijana Nigar (), Srabanti Paul (), Khaleque Md. Aashiq Kamal (), Anik Sen () and Iqbal H. Sarker ()
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Mahabuba Akhter: Premier University
Syed Md. Minhaz Hossain: Premier University
Rizma Sijana Nigar: Premier University
Srabanti Paul: Premier University
Khaleque Md. Aashiq Kamal: Premier University
Anik Sen: Premier University
Iqbal H. Sarker: Chittagong University of Engineering and Technology

Annals of Data Science, 2024, vol. 11, issue 6, No 13, 2167-2198

Abstract: 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).

Keywords: COVID-19; Fake news; Machine learning; CNN (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00507-y

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