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

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484) JMIR Theme Issue: COVID-19 Special Issue (2394) Natural Language Processing (650) Outbreak and Pandemic Preparedness and Management (1679) Mass Media/Social Media Communication and Campaigns (450) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Infodemiology and Infoveillance (1278) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1573) Infectious Diseases (non-STD/STI) (1490) Knowledge Translation and Implementation Science (375)

Published on in Vol 7, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26720, first published .
Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach

Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach

Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach

Authors of this article:

Lu Tang1 Author Orcid Image ;   Wenlin Liu2 Author Orcid Image ;   Benjamin Thomas3 Author Orcid Image ;   Hong Thoai Nga Tran4 Author Orcid Image ;   Wenxue Zou1 Author Orcid Image ;   Xueying Zhang5 Author Orcid Image ;   Degui Zhi4 Author Orcid Image
Article Authors Cited by (20) Tweetations (8) Metrics

Journals

  1. Alhassan F, AlDossary S. The Saudi Ministry of Health’s Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study. JMIR Public Health and Surveillance 2021;7(7):e27942 View
  2. Xie J, Liu L. Identifying features of source and message that influence the retweeting of health information on social media during the COVID-19 pandemic. BMC Public Health 2022;22(1) View
  3. Hoque M, Lee K, Beyer J, Curran S, Gonser K, Lam N, Mihunov V, Wang K. Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States. IEEE Access 2022;10:72879 View
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  9. Ding Q, Massey D, Huang C, Grady C, Lu Y, Cohen A, Matzner P, Mahajan S, Caraballo C, Kumar N, Xue Y, Dreyer R, Roy B, Krumholz H. Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study. JMIR Public Health and Surveillance 2021;7(9):e29413 View
  10. Ceretti E, Covolo L, Cappellini F, Nanni A, Sorosina S, Beatini A, Taranto M, Gasparini A, De Castro P, Brusaferro S, Gelatti U. Evaluating the Effectiveness of Internet-Based Communication for Public Health: Systematic Review. Journal of Medical Internet Research 2022;24(9):e38541 View
  11. Chen Y, Zhang Z. An easy numeric data augmentation method for early-stage COVID-19 tweets exploration of participatory dynamics of public attention and news coverage. Information Processing & Management 2022;59(6):103073 View
  12. Zhou A, Liu W, Kim H, Lee E, Shin J, Zhang Y, Huang-Isherwood K, Dong C, Yang A. Moral Foundations, Ideological Divide, and Public Engagement with U.S. Government Agencies’ COVID-19 Vaccine Communication on Social Media. Mass Communication and Society 2024;27(4):739 View
  13. Liu W, Huang Y. Does Relationship Matter during a Health Crisis: Examining the Role of Local Government- Public Relationship in the Public Acceptance of COVID-19 Vaccines. Health Communication 2023;38(6):1146 View
  14. Tsai J, Shih T, Tsai T, Lee S, Liang C. Individualism, economic development, and democracy as determinants of COVID-19 risk information on 132 government websites. Preventive Medicine Reports 2023;34:102242 View
  15. James L, McPhail H, Foisey L, Donelle L, Bauer M, Kothari A. Exploring communication by public health leaders and organizations during the pandemic: a content analysis of COVID-related tweets. Canadian Journal of Public Health 2023;114(4):563 View
  16. 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
  17. Lösch L, Zuiderent-Jerak T, Kunneman F, Syurina E, Bongers M, Stein M, Chan M, Willems W, Timen A. Capturing Emerging Experiential Knowledge for Vaccination Guidelines Through Natural Language Processing: Proof-of-Concept Study. Journal of Medical Internet Research 2023;25:e44461 View
  18. Balogun B, Hogden A, Kemp N, Yang L, Agaliotis M. Public health agencies’ use of social media for communication during pandemics: a scoping review of the literature. Osong Public Health and Research Perspectives 2023;14(4):235 View
  19. Terry K, Yang F, Yao Q, Liu C. The role of social media in public health crises caused by infectious disease: a scoping review. BMJ Global Health 2023;8(12):e013515 View
  20. Shady S, Shoda V, Kamihigashi T. Governors in the Digital Era: Analyzing and Predicting Social Media Engagement Using Machine Learning during the COVID-19 Pandemic in Japan. Informatics 2024;11(2):17 View