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J Med Internet Res ; 23(8): e29150, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1365496

ABSTRACT

BACKGROUND: The COVID-19 outbreak has induced negative emotions among people. These emotions are expressed by the public on social media and are rapidly spread across the internet, which could cause high levels of panic among the public. Understanding the changes in public sentiment on social media during the pandemic can provide valuable information for developing appropriate policies to reduce the negative impact of the pandemic on the public. Previous studies have consistently shown that the COVID-19 outbreak has had a devastating negative impact on public sentiment. However, it remains unclear whether there has been a variation in the public sentiment during the recovery phase of the pandemic. OBJECTIVE: In this study, we aim to determine the impact of the COVID-19 pandemic in mainland China by continuously tracking public sentiment on social media throughout 2020. METHODS: We collected 64,723,242 posts from Sina Weibo, China's largest social media platform, and conducted a sentiment analysis based on natural language processing to analyze the emotions reflected in these posts. RESULTS: We found that the COVID-19 pandemic not only affected public sentiment on social media during the initial outbreak but also induced long-term negative effects even in the recovery period. These long-term negative effects were no longer correlated with the number of new confirmed COVID-19 cases both locally and nationwide during the recovery period, and they were not attributed to the postpandemic economic recession. CONCLUSIONS: The COVID-19 pandemic induced long-term negative effects on public sentiment in mainland China even as the country recovered from the pandemic. Our study findings remind public health and government administrators of the need to pay attention to public mental health even once the pandemic has concluded.


Subject(s)
COVID-19/epidemiology , Emotions , Mental Health/statistics & numerical data , Pandemics , Public Opinion , Social Media/statistics & numerical data , China/epidemiology , Humans , SARS-CoV-2 , Time Factors
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