Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

doi:10.2196/20550

CORC > 心理研究所 > 中国科学院心理研究所 > 中国科学院行为科学重点实验室

	Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
	Xue, Jia 1,2; Chen, Junxiang 3; Hu, Ran 1; Chen, Chen 4; Zheng, Chengda 2; Su, Yue 5,6; Zhu, Tingshao 5
刊名	JOURNAL OF MEDICAL INTERNET RESEARCH
	2020-11-25
卷号	22 期号:11 页码:14
关键词	machine learning Twitter data COVID-19 infodemic infodemiology infoveillance public discussion public sentiment Twitter social media virus
ISSN号	1438-8871
DOI	10.2196/20550
通讯作者	Zhu, Tingshao(tszhu@psych.ac.cn)
英文摘要	Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. Objective: The objective of this study is to examine COVID-19-related discussions, concerns, and sentiments using tweets posted by Twitter users. Methods: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, "coronavirus," "COVID-19," "quarantine") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Results: Popular unigrams included "virus," "lockdown," and "quarantine." Popular bigrams included "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Conclusions: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.
WOS关键词	SOCIAL MEDIA ; SENTIMENT
WOS研究方向	Health Care Sciences & Services ; Medical Informatics
语种	英语
出版者	JMIR PUBLICATIONS, INC
WOS记录号	WOS:000602371500004
内容类型	期刊论文
源URL	[http://ir.psych.ac.cn/handle/311026/38282]
专题	心理研究所_中国科学院行为科学重点实验室
通讯作者	Zhu, Tingshao
作者单位	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, CAS Key Lab Behav Sci, 16 Lincui Rd, Beijing 100101, Peoples R China 6.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xue, Jia,Chen, Junxiang,Hu, Ran,et al. Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2020,22(11):14.
APA	Xue, Jia.,Chen, Junxiang.,Hu, Ran.,Chen, Chen.,Zheng, Chengda.,...&Zhu, Tingshao.(2020).Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.JOURNAL OF MEDICAL INTERNET RESEARCH,22(11),14.
MLA	Xue, Jia,et al."Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach".JOURNAL OF MEDICAL INTERNET RESEARCH 22.11(2020):14.