The study aims to understand Twitter users' discussions and reactions about the COVID-19. We use machine learning techniques to analyze about 1.8 million Tweets messages related to coronavirus collected from January 20th to March 7th , 2020. A total of salient 11 topics are identified and then categorized into 10 themes, such as "  cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive /Protective measures," "authorities," and "supply chain ". Results do not reveal treatment and /or symptoms related messages as a prevalent topic on Twitter. We also run sentiment analysis and the results show that trust for the authorities remained a prevalent emotion, but mixed feelings of trust for authorities, fear for the outbreak, and anticipation for the potential preventive measures will be taken are identified . Implications and limitations of the study are also discussed.
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7 , 2020. A total of salient 11 topics are identified and then categorized into ten themes, including " updates about confirmed cases," "COVID-19 related death," " cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain ." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics . Implications and limitations of the study are also discussed.
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discussions and reactions about the
discourse and psychological reactions to
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1.8 million Tweets messages
1.9 million Tweets (written in English)
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20th to March 7th
23 to March 7
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10 themes, such as
ten themes, including
A
updates about confirmed cases," "COVID-19 related death," "
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/Protective
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".
."
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treatment and /or
treatments and
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a prevalent topic
prevalent topics
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We also run sentiment analysis and the results show that trust for the authorities remained a prevalent emotion, but mixed feelings of trust for authorities, fear for the outbreak, and anticipation for the potential preventive measures will be taken are identified
Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics