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;9:205520762311714 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 2023:1 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

Books/Policy Documents

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