Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques
文献类型:期刊论文
作者 | Zheng, Chengda4; Xue, Jia1,4; Sun, Yumin4; Zhu, Tingshao2,3![]() |
刊名 | JOURNAL OF MEDICAL INTERNET RESEARCH
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出版日期 | 2021-02-23 |
卷号 | 23期号:2页码:12 |
关键词 | Canada PM Trudeau YouTube machine learning big data infodemiology infodemic public concerns communication concern social media video |
ISSN号 | 1438-8871 |
DOI | 10.2196/23957 |
产权排序 | 3 |
文献子类 | 实证研究 |
英文摘要 | 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. |
WOS关键词 | EBOLA |
WOS研究方向 | Health Care Sciences & Services ; Medical Informatics |
语种 | 英语 |
WOS记录号 | WOS:000620768300004 |
出版者 | JMIR PUBLICATIONS, INC |
源URL | [http://ir.psych.ac.cn/handle/311026/38714] ![]() |
专题 | 心理研究所_中国科学院行为科学重点实验室 |
通讯作者 | Zhu, Tingshao |
作者单位 | 1.Univ Toronto, Factor Inwentash Fac Social Work, Toronto, ON, Canada 2.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, 16 Lincui Rd, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 4.Univ Toronto, Fac Informat, Toronto, ON, Canada |
推荐引用方式 GB/T 7714 | Zheng, Chengda,Xue, Jia,Sun, Yumin,et al. Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2021,23(2):12. |
APA | Zheng, Chengda,Xue, Jia,Sun, Yumin,&Zhu, Tingshao.(2021).Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques.JOURNAL OF MEDICAL INTERNET RESEARCH,23(2),12. |
MLA | Zheng, Chengda,et al."Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques".JOURNAL OF MEDICAL INTERNET RESEARCH 23.2(2021):12. |
入库方式: OAI收割
来源:心理研究所
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