ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Natural Language Processing
Volume 8 - 2025 | doi: 10.3389/frai.2025.1627522
This article is part of the Research TopicHealthcare Text Analytics: Unlocking the Evidence from Free Text, Volume IVView all 7 articles
Linguistic Patterns in Pandemic-Related Content: A Comparative Analysis of COVID-19, Constraint, and Monkeypox Datasets
Provisionally accepted
- Manchester Metropolitan University, Manchester, United Kingdom
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Abstract—This study conducts a computational linguistic analysis of pandemic-related online discourse to examine how language distinguishes health misinformation from factual communication. Drawing on three corpora—COVID-19 false narratives (n = 7,588), general COVID-19 content (n = 10,700), and Monkeypox-related posts (n = 5,787)—we identify significant differences in readability, rhetorical markers, and persuasive language use. COVID-19 misinformation exhibited markedly lower readability scores and contained over twice the frequency of fear-related or persuasive terms compared to the other datasets. It also showed minimal use of exclamation marks, contrasting with the more emotive style of Monkeypox content. These patterns suggest that misinformation employs a deliberately complex rhetorical style embedded with emotional cues, a combination that may enhance its perceived credibility. Our findings contribute to the growing body of work on digital health misinformation by highlighting linguistic indicators that may aid detection efforts. They also inform public health messaging strategies and theoretical models of crisis communication in networked media environments. At the same time, the study acknowledges certain limitations, including reliance on traditional readability indices, use of a deliberately narrow persuasive lexicon, and reliance on static aggregate analysis. Future research should therefore incorporate longitudinal designs, broader emotion lexicons, and platform-sensitive approaches to strengthen robustness. The data and code is available at: https://doi.org/10.5281/zenodo.17024569
Keywords: COVID-19, health misinformation detection, Fake news classification, machine learning in public health, Elaboration Likelihood Model
Received: 12 May 2025; Accepted: 02 Sep 2025.
Copyright: © 2025 SIKOSANA, MAUDSLEY-BARTON and AJAO. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mkululi SIKOSANA, Manchester Metropolitan University, Manchester, United Kingdom
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