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

JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Outbreak and Pandemic Preparedness and Management (1727) Quality/Credibility of eHealth Information and Trust Issues (531) Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infectious Diseases (non-STD/STI) (1575) Information Quality in Digital Media (340) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Mass Media/Social Media Communication and Campaigns (461) Machine Learning (1465)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23957, first published .
Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

Authors of this article:

Chengda Zheng1 Author Orcid Image ;   Jia Xue1, 2 Author Orcid Image ;   Yumin Sun1 Author Orcid Image ;   Tingshao Zhu3, 4 Author Orcid Image
Article Authors Cited by (9) Tweetations (9) Metrics

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  1. D'Souza R, Daraz L, Hooten W, Guyatt G, Murad M. Users' Guides to the Medical Literature series on social media (part 2): how to appraise studies using data from platforms. BMJ Evidence-Based Medicine 2022;27(1):15 View
  2. Melissa M, Jillian J, Jennifer E, Daniel G, Andrew P. Review and thematic analysis of guiding principles for effective crisis communication using social media. Journal of Public Health and Epidemiology 2022;14(2):72 View
  3. Toussaint P, Renner M, Lins S, Thiebes S, Sunyaev A. Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments. JMIR Infodemiology 2022;2(2):e38749 View
  4. Kwon S, Park A. Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects. Computers in Human Behavior 2023;144:107734 View
  5. Alafwan B, Siallagan M, Putro U. Comments Analysis on Social Media: A Review. ICST Transactions on Scalable Information Systems 2023 View
  6. Sharma R, Bharathy G, Karimi F, Mishra A, Prasad M. Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review. Information 2023;14(10):577 View
  7. Enilama O, MacDonald C, Thompson P, Khan U, Allu S, Beaucage M, Yau K, Oliver M, Hladunewich M, Levin A. Perceptions and Information-Seeking Behavior Regarding COVID-19 Vaccination Among Patients With Chronic Kidney Disease in 2023: A Cross-Sectional Survey. Canadian Journal of Kidney Health and Disease 2024;11 View
  8. Chandrabhatla A, Narahari A, Horgan T, Patel P, Sturek J, Davis C, Jackson P, Bell T. Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response. Open Forum Infectious Diseases 2024;11(4) View
  9. Xue J, Shier M, Chen J, Wang Y, Zheng C, Chen C. A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e51698 View