A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms

Yahya Tashtoush, Balqis Alrababah, Omar Darwish, Majdi Maabreh and Nasser Alsaedi
Additional contact information
Yahya Tashtoush: Computer Science Department, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
Balqis Alrababah: Computer Science Department, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
Omar Darwish: Information Security and Applied Computing Department, Eastern Michigan University, Ypsilanti, MI 48197, USA
Majdi Maabreh: Department of Information Technology, Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Nasser Alsaedi: Computer Science Department, Taibah University, Medina 2003, Saudi Arabia

Data, 2022, vol. 7, issue 5, 1-17

Abstract: The fast growth of technology in online communication and social media platforms alleviated numerous difficulties during the COVID-19 epidemic. However, it was utilized to propagate falsehoods and misleading information about the disease and the vaccination. In this study, we investigate the ability of deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), and a hybrid of CNN and LSTM networks, to automatically classify and identify fake news content related to the COVID-19 pandemic posted on social media platforms. These deep neural networks have been trained and tested using the “COVID-19 Fake News” dataset, which contains 21,379 real and fake news instances for the COVID-19 pandemic and its vaccines. The real news data were collected from independent and internationally reliable institutions on the web, such as the World Health Organization (WHO), the International Committee of the Red Cross (ICRC), the United Nations (UN), the United Nations Children’s Fund (UNICEF), and their official accounts on Twitter. The fake news data were collected from different fact-checking websites (such as Snopes, PolitiFact, and FactCheck). The evaluation results showed that the CNN model outperforms the other deep neural networks with the best accuracy of 94.2%.

Keywords: text classification; fake news detection; neural networks; deep learning; COVID-19; coronavirus; text mining (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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