  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.
