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

Artificial Intelligence (2162) Infodemiology and Infoveillance (1565) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions 2023 (409) Outbreak and Pandemic Preparedness and Management (1829) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1142) Natural Language Processing (923) Generative Language Models Including ChatGPT (725) Theme Issue 2022: Social Media, Ethics, and COVID-19 Misinformation (45) Social Media in Public Health informatics (67)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63824, first published .
Understanding Citizens’ Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI

Understanding Citizens’ Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI

Understanding Citizens’ Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI

Authors of this article:

Ryuichi Saito1 Author Orcid Image ;   Sho Tsugawa1 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics Author Video

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  2. Jiang H, Wu M, Yu J, Xiao Y, Yin C. AI-powered epidemic control: Deepseek’s role in global health resilience. Journal of Translational Medicine 2025;23(1) View
  3. Qiu Z, Li X, Liu Q, Yang S. Real-time monitoring of public affective risk perception during extreme rainstorm events: A deep learning framework with disaster knowledge-enhanced attention mechanism. Urban Climate 2026;65:102737 View