Madanian, SamanehBakhtiari, VahidFeng, Vincent2025-09-012025-09-012025-08Studies in Health Technology and Informatics, ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, Volume 329: MEDINFO 2025 — Healthcare Smart × Medicine Deep, 1505-1509. doi: 10.3233/SHTI2510900926-96300926-9630http://hdl.handle.net/10292/19746This study advances our understanding of public health crisis communication by conducting a longitudinal analysis. As COVID-19 has been the largest public health crisis to date, we performed sentiment analysis on it. While previous research focused on discrete time periods, our study examines the arc of pandemic-related discourse from 2020 to 2022, revealing long-term patterns in public sentiment evolution. Using advanced natural language processing techniques and temporal pattern analysis, we identify key transition points in public health discourse and sentiment, offering insights for future crisis communication strategies.© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).https://creativecommons.org/licenses/by/4.0/Digital HealthDisaster ResponseNLPPublic HealthText Mining46 Information and Computing Sciences4608 Human-Centred ComputingInfectious DiseasesEmerging Infectious Diseases0807 Library and Information Studies1117 Public Health and Health ServicesMedical Informatics4203 Health services and systems4601 Applied computingCOVID-19HumansLongitudinal StudiesNatural Language ProcessingPandemicsPublic HealthSARS-CoV-2Social MediaCOVID-19Longitudinal StudiesHumansNatural Language ProcessingPublic HealthSARS-CoV-2PandemicsSocial MediaHumansLongitudinal StudiesPublic HealthNatural Language ProcessingPandemicsSocial MediaCOVID-19SARS-CoV-2Journal ArticleOpenAccess10.3233/SHTI251090