Enhancing COVID‐19 misinformation detection through novel attention mechanisms in NLP

Author:

Hussain Anbar1,Ali Wajid1,Ahmad Awais2ORCID,Iqbal Muhammad Shahid3,Moqurrab Syed Atif45,Paul Anand6,Jabbar Sohail2ORCID,Akram Sheeraz2

Affiliation:

1. School of Computer Science and Engineering Central South University Changsha Hunan China

2. College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

3. Department of Computer Science and Information Technology Women University of Azad Jammu and Kashmir Bagh Pakistan

4. School of Computing Gachon University Seongnam‐si Korea

5. Department of Cybersecurity, Faculty of Computer Technologies and Cyber Security International IT University Almaty Kazakhstan

6. School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea

Abstract

AbstractThe 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.

Funder

Imam Mohammed Ibn Saud Islamic University

Publisher

Wiley

Reference42 articles.

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