Trends of fear and anger on YouTube during the initial stage of the COVID-19 outbreak in South Korea.

BACKGROUND: The COVID-19 pandemic has been the most widespread and threatening health crisis experienced by the Korean society. Faced with an unprecedented threat to survival, society has been gripped by social fear and anger, questioning the culpability of this pandemic. This study explored the correlation between social cognitions and negative emotions and their changes in response to the severe events stemming from the COVID-19 pandemic in South Korea. METHODS: The analysis was based on a cognitive-emotional model that links fear and anger to the social causes that trigger them and used discursive content from comments posted on YouTube's COVID-19-related videos. A total of 182,915 comments from 1,200 videos were collected between January and December 2020. We performed data analyses and visualizations using R, Netminer 4.0, and Gephi software and calculated Pearson's correlation coefficients between emotions. RESULTS: YouTube videos were analyzed for keywords indicating cognitive assessments of major events related to COVID-19 and keywords indicating negative emotions. Eight topics were identified through topic modeling: causes and risks, perceptions of China, media and information, infection prevention rules, economic activity, school and infection, political leaders, and religion, politics, and infection. The correlation coefficient between fear and anger was 0.462 (p < .001), indicating a moderate linear relationship between the two emotions. Fear was the highest from January to March in the first year of the COVID-19 outbreak, while anger occurred before and after the outbreak, with fluctuations in both emotions during this period. CONCLUSIONS: This study confirmed that social cognitions and negative emotions are intertwined in response to major events related to the COVID-19 pandemic, with each emotion varying individually rather than being ambiguously mixed. These findings could aid in developing social cognition-emotion-based public health strategies through education and communication during future pandemic outbreaks.

Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis.

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.