Dataset Information

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.

SUBMITTER: Zheng C

PROVIDER: S-EPMC7903980 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Zheng Chengda C Xue Jia J Sun Yumin Y Zhu Tingshao T

Journal of medical Internet research 20210223 2

<h4>Background</h4>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.<h4>Objective</h4>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 cha ...[more]

PMID: 33544690

Similar Datasets

Project description:BackgroundPoor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities.ObjectiveThe objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.MethodsThis paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.ResultsWith a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.ConclusionsThis study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

Project description:Skin diseases constitute an essential health and aesthetic problem in the elderly. The aim of the study was to evaluate the knowledge of the elderly residents of public nursing homes and participants of the University of the Third Age in Bialystok, Poland surrounding the factors influencing skin ageing, the awareness of skin conditions in agening skin, and the impact of skin ageing on the volunteers. The study was performed from April to June 2015 in Bialystok, in two groups: among 100 public nursing home residents (PNH) and 100 members of University of the Third Age (U3A), (all over 60 years old). The study made use of a diagnostic survey conducted via a questionnaire prepared by the authors. Nearly half of those surveyed (42.5%; n = 85) sunbathed in the past, while 28.0% (n = 56) of those surveyed now take part in this type of leisure activity. More than half of respondents (53.0%; n = 106) protected their skin using special protective preparations. A majority of Bialystok inhabitants surveyed (80.5%; n = 161) noticed the features of skin ageing. They reported birthmarks, fungal infections and bedsores as the main skin problems of the old age. Nearly half (40%) of respondents assessed their knowledge as average and 26.0% as poor. The study showed some statistical differences in the knowledge and awareness between the residents of public nursing homes and the students of the University of the Third Age, e.g., the use of the Internet by the U3A group for finding out information. There is a desire to receive education in the field of the agening skin conditions/diseases among the elderly because their level of knowledge is relatively poor. Education of seniors in this area can increase their awareness of the basic principles of skin care and prevention marking of skin ageing. The benefits of greater knowledge of seniors about the conditions of agening skin can help reduce the medical burden and reduce the incidence on certain skin diseases. Furthermore, there is a need for educating of the younger population on the factors of skin ageing to prevent certain skin conditions as they become older. Seniors should be professionally educated by qualified specialists; for example, dermatologists or cosmeticians, so that the information they receive is in line with evidence-based medicine.

Project description:Background: There has been very little previous research in Ireland on the farmers' opinions regarding calf welfare issues. Calf welfare, particularly for male dairy calves, has assumed greater importance in Ireland in recent years due, in part, to an increase in the number of dairy cattle over the past decade. The objective of this study was to explore dairy farmers' views on a broad range of issues related to the expansion in the dairy herd. Methods: A survey was developed to capture the views of farmers regarding male dairy calves. The majority of questions were quantitative, and a final open-ended question collected qualitative data. The survey was distributed to ~2,900 dairy farmers via text message and 881 responses were received. Results: The sample was composed almost entirely of dairy farmers, although ~20% also had a beef enterprise on their farm. Fifty eight percent of the farmers were concerned with the increase in the number of male dairy calves in recent years. The EU's abolition of milk quotas, the profitability of dairy farming compared to other farm types, and guidance from farm advisors were the three highest ranked drivers behind the increase in the number of male dairy calves. The three highest ranked options for managing the number of male dairy calves were to increase exports, encourage greater use of sexed semen, and improve the beef merit of these calves. Eighty five percent of respondents stated that individual farmers had responsibility for making changes to the number of male dairy calves. The main themes arising from analysis of the responses to the open-ended question, seeking any additional comments, were breed, beef price, live exports, and sexed semen. Conclusions: Dairy farmers recognized the responsibility they have for making changes in respect of male dairy calves, and many demonstrated a willingness to make changes in this regard. The important role of other stakeholders, particularly suckler (system where reared from calf to beef) farmers, in rearing male dairy calves for beef production was also recognized. However, the issues of who bears the risks and costs associated with greater integration will have to be carefully considered.

Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure