Temporal Evolution of Public Health Sentiment: A Longitudinal Analysis - Madanian, Samaneh; Bakhtiari, Vahid; Feng, Vincent (IOS Press, 2025)
This 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.
Keywords: Digital Health; Disaster Response; NLP; Public Health; Text Mining; 46 Information and Computing Sciences; 4608 Human-Centred Computing; Infectious Diseases; Emerging Infectious Diseases; 0807 Library and Information Studies; 1117 Public Health and Health Services; Medical Informatics; 4203 Health services and systems; 4601 Applied computing; COVID-19; Humans; Longitudinal Studies; Natural Language Processing; Pandemics; Public Health; SARS-CoV-2; Social Media; COVID-19; Longitudinal Studies; Humans; Natural Language Processing; Public Health; SARS-CoV-2; Pandemics; Social Media; Humans; Longitudinal Studies; Public Health; Natural Language Processing; Pandemics; Social Media; COVID-19; SARS-CoV-2