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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

For example, Xue et al [7] examined Twitter posts related to the COVID-19 pandemic. Obadimu and colleagues [18] applied LDA to recognize the toxicity of comments on You Tube. In this study, we treated the collected You Tube comments as a document and applied LDA topic modeling with the gensim Python library. For each identified topic, we used Word Net Lemmatizer to extract popular unigrams and bigrams. The py LDV library was used to visualize the findings.

Chengda Zheng, Jia Xue, Yumin Sun, Tingshao Zhu

J Med Internet Res 2021;23(2):e23957

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Article has an altmetric score of 8

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets

Xue et al [38] analyzed 322,863 tweets about domestic violence and found that high-profile cases such as Greg Hardy's domestic violence case are prominent. These studies consistently show that Twitter continues to be a source of news coverage on current events for domestic violence, even during the COVID-19 pandemic. There are a number of limitations to this study that must be acknowledged. First, Twitter data reveal insights from Twitter users and thus does not represent the entire population's opinions.

Jia Xue, Junxiang Chen, Chen Chen, Ran Hu, Tingshao Zhu

J Med Internet Res 2020;22(11):e24361

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Article has an altmetric score of 29