ArticleJournal of medical Internet research2021
Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Machine learning prediction models for the popularization and dissemination of medical science popularization videos.Frontiers in public health · 2026Article
- A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.Open forum infectious diseases · 2024Article
- 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 · 2024Article
- Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects.Computers in human behavior · 2023Article
- Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government's responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel.
objectiveThe aim of this study was to examine comments on Canadian Prime Minister Trudeau's COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time.
methodsWe used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau's COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes.
resultsWe found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau's policies, essential work and frontline workers, individuals' financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China's relationship, vaccines, and reopening.
conclusionsThis study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau's daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.