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

Quality/Credibility of eHealth Information and Trust Issues (685) Artificial Intelligence (2460) Misinformation and Disinformation Outbreaks and Information Prevalence Studies (110) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (106)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73601, first published .
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Identifying Disinformation on the Extended Impacts of COVID-19: Methodological Investigation Using a Fuzzy Ranking Ensemble of Natural Language Processing Models

Identifying Disinformation on the Extended Impacts of COVID-19: Methodological Investigation Using a Fuzzy Ranking Ensemble of Natural Language Processing Models

Authors of this article:

Jian-An Chen1 Author Orcid Image ;   Wu-Chun Chung2 Author Orcid Image ;   Che-Lun Hung1, 3 Author Orcid Image ;   Chun-Ying Wu1, 3, 4, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Gong Y, Goyal A, Ravikumar S, Wang D. Prebunking false information in the wild: Do inoculation-based interventions work against real-world false information?. International Journal of Human-Computer Studies 2026;214:103837 View

Conference Proceedings

  1. Patel J, Bhatt C, Trivedi H, Nguyen T. 2025 8th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). Misinformation Detection using Large Language Models with Explainability View