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

doi:10.1371/journal.pone.0239441

	Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter
	Xue, Jia 1,6; Chen, Junxiang 5; Chen, Chen 4; Zheng, Chengda 6; Li, Sijia 2,3; Zhu, Tingshao 3
刊名	PLOS ONE
	2020-09-25
卷号	15 期号:9 页码:12
ISSN号	1932-6203
DOI	10.1371/journal.pone.0239441
产权排序	5
文献子类	实证研究
英文摘要	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.
资助项目	National Natural Science Foundation of China[31700984] ; Artificial Intelligence Lab for Justice at University of Toronto, Canada
WOS研究方向	Science & Technology - Other Topics
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000576266600002
资助机构	National Natural Science Foundation of China ; Artificial Intelligence Lab for Justice at University of Toronto, Canada
内容类型	期刊论文
源URL	[http://ir.psych.ac.cn/handle/311026/32967]
专题	心理研究所_社会与工程心理学研究室
通讯作者	Zhu, Tingshao
作者单位	1.Univ Toronto, Factor Inwentash Fac Social Work, Toronto, ON, Canada 2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 4.Univ Toronto, Middleware Syst Res Grp, Toronto, ON, Canada 5.Univ Pittsburgh, Sch Med, Pittsburgh, PA USA 6.Univ Toronto, Fac Informat, Toronto, ON, Canada
推荐引用方式 GB/T 7714	Xue, Jia,Chen, Junxiang,Chen, Chen,et al. Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter[J]. PLOS ONE,2020,15(9):12.
APA	Xue, Jia,Chen, Junxiang,Chen, Chen,Zheng, Chengda,Li, Sijia,&Zhu, Tingshao.(2020).Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.PLOS ONE,15(9),12.
MLA	Xue, Jia,et al."Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter".PLOS ONE 15.9(2020):12.