Enhancing COVID-19 misinformation detection through novel attention mechanisms in NLP
Hussain A. Ali W. Ahmad A. Iqbal M.S. Moqurrab S.A. Paul A. Jabbar S. Akram S.
January 2025John Wiley and Sons Inc
Expert Systems
2025#42Issue 1
The rapid evolution of electronic media in recent decades has exponentially amplified the propagation of fake news, resulting in widespread confusion and misunderstanding among the masses, especially concerning critical topics like the COVID-19 pandemic. Consequently, detecting fake news on social media has emerged as a prominent area of research, attracting significant attention. This article introduces a novel cascaded group multi-head attention (CGMHA) model for COVID-19 fake news detection. Our research collected Twitter datasets with accurate and fake tweets in Urdu. The novel CGMHA model and depth-wise convolution capture local and global contextual information by employing multiple attention heads in a cascaded fashion, enabling a comprehensive understanding of fake news. While achieving state-of-the-art performance, we also highlight challenges such as language variations and misinformation nuances in the detection process, contributing to a more comprehensive understanding of the complexities involved in combatting fake news. Our proposed model surpasses the performance of state-of-the-art models in classifying fake news and achieves accuracy, F1 score, precision, and recall of 0.98, 0.96, 0.95, and 0.95, respectively.
COVID-19 , fake news detection , multi-head attention , sentiment analysis , social media , Urdu language
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School of Computer Science and Engineering, Central South University, Hunan, Changsha, China
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Department of Computer Science and Information Technology, Women University of Azad Jammu and Kashmir, Bagh, Pakistan
School of Computing, Gachon University, Seongnam-si, South Korea
Department of Cybersecurity, Faculty of Computer Technologies and Cyber Security, International IT University, Almaty, Kazakhstan
School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea
School of Computer Science and Engineering
College of Computer and Information Sciences
Department of Computer Science and Information Technology
School of Computing
Department of Cybersecurity
School of Computer Science and Engineering
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