期刊
PLOS ONE
卷 15, 期 9, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0239441
关键词
-
资金
- National Natural Science Foundation of China [31700984]
- Artificial Intelligence Lab for Justice at University of Toronto, Canada
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据