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

Infodemiology and Infoveillance (1349) JMIR Theme Issue: COVID-19 Special Issue (2452) COVID-19 Vaccination (146) Vaccination and Immunization in the Digital Age (374) Parent and Caregiver Education and Behavior Change for Vaccination (74) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Mass Media/Social Media Communication and Campaigns (461) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30715, first published .
Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach

Authors of this article:

Chen Luo1, 2 Author Orcid Image ;   Kaiyuan Ji1 Author Orcid Image ;   Yulong Tang3 Author Orcid Image ;   Zhiyuan Du1 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Ferawati K, Liew K, Aramaki E, Wakamiya S. Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study. JMIR Infodemiology 2022;2(2):e39504 View
  2. Simeoni R, Maccioni G, Giansanti D. The Vaccination Process against the COVID-19: Opportunities, Problems and mHealth Support. Healthcare 2021;9(9):1165 View
  3. Luo C, Chen A, Cui B, Liao W. Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China. Telematics and Informatics 2021;65:101712 View
  4. Yin J. Media Data and Vaccine Hesitancy: Scoping Review. JMIR Infodemiology 2022;2(2):e37300 View
  5. Kim M, Noh Y, Yamada A, Hong S. Comparison of the Erectile Dysfunction Drugs Sildenafil and Tadalafil Using Patient Medication Reviews: Topic Modeling Study. JMIR Medical Informatics 2022;10(2):e32689 View
  6. Luo C, Dai R, Deng Y, Chen A. How did Chinese public health authorities promote COVID-19 vaccination on social media? A content analysis of the vaccination promotion posts. DIGITAL HEALTH 2023;9 View
  7. Thampy P, Sharma S, Joshi P, Raj M, Rupani A, Tyagi S, Joshi A. COVID-19 Vaccine Hesitancy Among Healthcare Workers: A Phenomenological Study of Skepticism. Cureus 2024 View
  8. Kim N, Lee N. Structural Topic Modeling Analysis of Patient Safety Interest among Health Consumers in Social Media. Journal of Korean Academy of Nursing 2024;54(2):266 View
  9. Pak J, Kim H. The Role of Content Labeling and Discrete Emotions in User Participation in a COVID-19 U.S. Reddit Community. Journalism & Mass Communication Quarterly 2024 View
  10. Rouhani S, Mozaffari F. Comprehensive analytics of COVID-19 vaccine research: From topic modeling to topic classification. Artificial Intelligence in Medicine 2024;157:102980 View
  11. Li J, Shi W. Debates over the role of Traditional Chinese Medicine on COVID-19: A computational comparison between professionals and laypersons in Chinese online knowledge community. Social Science & Medicine 2024;361:117366 View
  12. Guo Y, Xiang H, Hou Y. Coping with Unknown Health Crisis via Social Media: A Content Analysis of Online Mutual Aid Group in the Beginning of the COVID-19 Pandemic. Disaster Medicine and Public Health Preparedness 2024;18 View