Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

被引：157
|
作者：
Xue, Jia [1 ,2 ]
Chen, Junxiang [3 ]
Chen, Chen [4 ]
Zheng, Chengda [2 ]
Li, Sijia [5 ,6 ]
Zhu, Tingshao [5 ]
机构：
[1] Univ Toronto, Factor Inwentash Fac Social Work, Toronto, ON, Canada
[2] Univ Toronto, Fac Informat, Toronto, ON, Canada
[3] Univ Pittsburgh, Sch Med, Pittsburgh, PA USA
[4] Univ Toronto, Middleware Syst Res Grp, Toronto, ON, Canada
[5] Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
[6] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
来源：
PLOS ONE | 2020年 / 15卷 / 09期
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1371/journal.pone.0239441
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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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收藏
页数：12
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