Machine learning on Big Data from Twitter to understand public reactions to COVID-19

Xue, Jia; Chen, Junxiang; Chen, Chen; Zheng, ChengDa; Zhu, Tingshao

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.