Institutional Repository of Key Laboratory of Behavioral Science, CAS
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach | |
Xue, Jia1,2; Chen, Junxiang3; Hu, Ran1; Chen, Chen4; Zheng, Chengda2; Su, Yue5,6; Zhu, Tingshao5 | |
第一作者 | Xue, Jia |
通讯作者邮箱 | tszhu@psych.ac.cn |
心理所单位排序 | 5 |
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. |
关键词 | machine learning Twitter data COVID-19 infodemic infodemiology infoveillance public discussion public sentiment Twitter social media virus |
2020-11-25 | |
DOI | 10.2196/20550 |
发表期刊 | JOURNAL OF MEDICAL INTERNET RESEARCH |
ISSN | 1438-8871 |
卷号 | 22期号:11页码:14 |
期刊论文类型 | 实证研究 |
收录类别 | SCI |
出版者 | JMIR PUBLICATIONS, INC |
WOS关键词 | SOCIAL MEDIA ; SENTIMENT |
WOS研究方向 | Health Care Sciences & Services ; Medical Informatics |
WOS类目 | Health Care Sciences & Services ; Medical Informatics |
WOS记录号 | WOS:000602371500004 |
WOS分区 | Q1 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/38283 |
专题 | 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 |
通讯作者单位 | 中国科学院行为科学重点实验室 |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Twitter Discussions (1181KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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