This paper is in the following e-collection/theme issue:

Ethical, Legal, and Social Issues in AI (89) Artificial Intelligence (1958) Applications of AI (229)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47240, first published .
An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

Authors of this article:

Jiahui Lu1, 2 Author Orcid Image ;   Huibin Zhang2 Author Orcid Image ;   Yi Xiao2 Author Orcid Image ;   Yingyu Wang2 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Hussna A, Alam M, Islam R, Alkhamees B, Hassan M, Uddin M. Dissecting the infodemic: An in-depth analysis of COVID-19 misinformation detection on X (formerly Twitter) utilizing machine learning and deep learning techniques. Heliyon 2024;10(18):e37760 View
  2. McKee M, Rosenbacke R, Stuckler D. The power of artificial intelligence for managing pandemics: A primer for public health professionals. The International Journal of Health Planning and Management 2025;40(1):257 View
  3. Panteli D, Adib K, Buttigieg S, Goiana-da-Silva F, Ladewig K, Azzopardi-Muscat N, Figueras J, Novillo-Ortiz D, McKee M. Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions. The Lancet Public Health 2025;10(5):e428 View
  4. Bhattacharya S, Singh A. Unravelling the infodemic: a systematic review of misinformation dynamics during the COVID-19 pandemic. Frontiers in Communication 2025;10 View