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

JMIR Theme Issue: COVID-19 Special Issue (2554) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1139) Infodemiology and Infoveillance (1558) Vaccination and Immunization in the Digital Age (442) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911) Discourse (40)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42623, first published .
A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

Authors of this article:

Susan Park1, 2 Author Orcid Image ;   Young-Kyoon Suh3, 4 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Lindelöf G, Aledavood T, Keller B. Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts. Journal of Medical Internet Research 2023;25:e41319 View
  2. Faizah , Lin B. Visualizing Change and Correlation of Topics With LDA and Agglomerative Clustering on COVID-19 Vaccine Tweets. IEEE Access 2023;11:51647 View
  3. Albitar L, Aboualchamat G. Assessment of attitudes and practices towards COVID-19 pandemic: a survey on a cohort of educated Syrian population. Journal of the Egyptian Public Health Association 2023;98(1) View
  4. Cheng T, Han B, Liu Y. Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data. Frontiers in Public Health 2023;11 View
  5. Doğan B, Balcioglu Y, Elçi M. Multidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic. Kybernetes 2025;54(4):2414 View
  6. Raman R, Singhania M, Nedungadi P. Advancing the United Nations Sustainable Development Goals Through Digital Health Research: 25 Years of Contributions From the Journal of Medical Internet Research. Journal of Medical Internet Research 2024;26:e60025 View
  7. Alshanik F, Khasawneh R, Dalky A, Qawasmeh E. Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach. JMIR Infodemiology 2025;5:e53434 View
  8. Kim T, Yang J, Park E. MSDLF-K: A Multimodal Feature Learning Approach for Sentiment Analysis in Korean Incorporating Text and Speech. IEEE Transactions on Multimedia 2025;27:1266 View
  9. Oh J, Seo J, Park H. Cause of death statistics in 2022 in the Republic of Korea. Ewha Medical Journal 2025;48(3):e46 View
  10. Jabbari Tofighi N, Alhajj R, Elbassuoni S. Investigating the impact of social media images on users’ sentiments towards sociopolitical events based on deep artificial intelligence. PLOS One 2025;20(7):e0326936 View
  11. Hora R, Ray A, Kumari A, Mehra R, Kaur A, F Quadri S, Ray B, Singh Koshal S, Kumar Singh S, Sultana A, Deb Roy A. Digital Media Coverage of Respiratory Syncytial Virus-Related News in India: Mixed Methods Content Analysis of Disease Burden and Intervention. JMIR Formative Research 2025;9:e70322 View

Books/Policy Documents

  1. Reis J. Emerging Trends in Information Systems and Technologies. View

Conference Proceedings

  1. Seo H, Joo H, Tak B, Suh Y. Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing. HARIN: A Novel Metric for Hierarchical Topic Model Assessment View