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

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
  4. Tivey M. What can equine surveillance learn from surveillance of other species?. Veterinary Record 2021;189(12) View
  5. Yu H, Yang C, Yu P, Liu K, Patel S. Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers. PLOS ONE 2022;17(3):e0264794 View
  6. Pokharel M, Lillie H, Nagatsuka K, Barbour J, Ratcliff C, Jensen J. Social media narratives can influence vaccine intentions: The impact of depicting regret and character death. Computers in Human Behavior 2023;141:107612 View
  7. Mao Z, Wang D, Zheng S. Health belief model and social media engagement: A cross-national study of health promotion strategies against COVID-19 in 2020. Frontiers in Public Health 2023;11 View
  8. Singhal A, Baxi M, Mago V. Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models. JMIR Medical Informatics 2022;10(8):e37829 View
  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 2022:1 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