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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28716, first published .
Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data

Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data

Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data

Journals

  1. Ezike N, Ames Boykin A, Dobbs P, Mai H, Primack B. Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series. JMIR Infodemiology 2022;2(2):e37412 View
  2. Minian N, Gayapersad A, Saiva A, Dragonetti R, Kidd S, Strudwick G, Selby P. An e–Mental Health Resource for COVID-19–Associated Stress Reduction: Mixed Methods Study of Reach, Usability, and User Perceptions. JMIR Mental Health 2022;9(8):e39885 View
  3. Zhang F, Tang Q, Chen J, Han N. China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo. Frontiers in Psychology 2023;13 View
  4. Steenbeek A, Gallant A, MacDonald N, Curran J, Graham J. Nova Scotia Strong: why communities joined to embrace COVID-19 public health measures. Canadian Journal of Public Health 2022;113(S1):4 View
  5. Newbold K, Vrabic K, Wayland S, Wahoush O, Weerakoon Y. ‘Strange eyes’: Immigrant perceptions of racism during the COVID‐19 pandemic. Population, Space and Place 2022;28(7) View
  6. Trevino J, Malik S, Schmidt M. Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study. JMIR Infodemiology 2022;2(1):e32386 View
  7. Constant A, Conserve D, Gallopel-Morvan K, Raude J. Cognitive Factors Associated With Public Acceptance of COVID-19 Nonpharmaceutical Prevention Measures: Cross-sectional Study. JMIRx Med 2022;3(2):e32859 View
  8. Tsao S, MacLean A, Chen H, Li L, Yang Y, Butt Z. Public Attitudes During the Second Lockdown: Sentiment and Topic Analyses Using Tweets From Ontario, Canada. International Journal of Public Health 2022;67 View
  9. Weerasinghe S, Oyebode O, Orji R, Matwin S. Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach. DIGITAL HEALTH 2023;9 View
  10. Thakur N. Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox. Big Data and Cognitive Computing 2023;7(2):116 View
  11. Laurent-Simpson A. COVID-19 and Masking Disparities: Qualitative Analysis of Trust on the CDC’s Facebook Page. International Journal of Environmental Research and Public Health 2023;20(12):6062 View
  12. Lu M, Ali M, Zhang W, Kumar N. Mediation analysis of public emotions in response to policy implementation performance during crises: the case of COVID-19 management policies in the UK. Public Management Review 2024;26(8):2499 View
  13. Saleem A, Davis M, Rafique S, Meer S, Qader A, Aslam M. A Critical Glance to Non-Pharmacological Management of Novel COVID-19 Infection. Pakistan Journal of Health Sciences 2023:02 View
  14. Ngo V. Does ChatGPT change artificial intelligence-enabled marketing capability? Social media investigation of public sentiment and usage. Global Media and China 2024;9(1):101 View
  15. Steele B, Shastri P, Moses C, Tremblay E, Arcenal M, O’Campo P, Mason R, Du Mont J, Hujbregts M, Sim A, Yakubovich A. The mental health of staff at violence against women organizations during the COVID-19 pandemic: Findings from a mixed-methods study of service providers in Canada’s largest city. Canadian Journal of Public Health 2024;115(5):756 View

Books/Policy Documents

  1. Rade C, Anazodo K, Ricciardelli R. Employing Our Returning Citizens. View