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

Abstract

Background During 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. Objective The 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. Methods We 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. Results We 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. Conclusions This 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.

Classificação automática de vídeos educacionais por meio de comentários apoiada por técnicas de Aprendizado de Máquina: uma análise experimental utilizando o Youtube

“…O estudo sugeriu que os comentários dos usuários do Youtube apresentam potencial para serem utilizados para categorizar os vídeos da plataforma. Zheng et al (2021) analisaram comentários postados em vídeos diários do primeiro-ministro canadense durante a pandemia de COVID-19. Foram analisados 46.732 comentários, em inglês, obtidos em de 57 vídeos postados entre 13 de Março a 22 de Maio de 2020.…”

Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments

“…With the increasing spread and availability of DTC genetic testing [ 2 ] and a general tendency in society to retrieve as well as discuss health information and health-related topics on the internet [ 19 ], it is by no means surprising that DTC genetic testing is a frequent and recent topic on many social media platforms [ 18 , 20 , 21 ]. In particular, YouTube, one of the largest social media platforms and the most comprehensive web-based video platform [ 22 ], serves as the first port of call for many internet users to discuss health information and DTC genetic testing in particular [ 23 ]. While YouTube can serve to share health information and experiences with a big audience for content creators (eg, consumers, service providers, health care professionals, or journalists), it also enables user discourse through textual comments below individual videos [ 24 ].…”

“…Understanding the topics, opinions, and attitudes discussed by the users can prove crucial for many stakeholders, as comments are the main form of user reaction and feedback on social media [ 23 ]. Service providers may gain, for instance, insights into consumer demands, whereas content creators may improve their videos by adjusting their content to meet user preferences.…”

“…Consequently, information dissemination platforms (see Multimedia Appendix 1 for a detailed description of social media platform types), such as YouTube, have garnered interest from researchers to study various health care–related topics. For example, studies have investigated users' attitudes toward the effect of sleep-aiding music [ 24 ], users' preferences regarding treatment and symptoms of diabetes as well as the social culture pertaining to diabetes-related video clips [ 39 ], or public opinions and concerns about daily coverage of the COVID-19 crisis in Canada [ 23 ].…”

Users' Guides to the Medical Literature series on social media (part 2): how to appraise studies using data from platforms

“…For instance, a particular topic might only exist on one platform, such as public educational videos describing advanced orthopaedic surgical techniques 15. Another example is when social media content is related to a particular type of discourse at a specific time, such as an announcement from the prime minister on new COVID-19 briefings16 or clinical presentation after a mass casualty event 17. In these scenarios, a study that includes a small sample size or analyses only one platform may be justified.…”