Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter
文献类型：期刊论文
| 作者 | Xue, Jia5,6; Chen, Junxiang4; Chen, Chen3 ; Zheng, Chengda5; Li, Sijia1,2; Zhu, Tingshao2
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| 刊名 | PLOS ONE
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| 出版日期 | 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 |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000576266600002 |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| 资助机构 | National Natural Science Foundation of China ; Artificial Intelligence Lab for Justice at University of Toronto, Canada |
| 源URL | [http://ir.psych.ac.cn/handle/311026/32968]  |
| 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 Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 3.Univ Toronto, Middleware Syst Res Grp, Toronto, ON, Canada 4.Univ Pittsburgh, Sch Med, Pittsburgh, PA USA 5.Univ Toronto, Fac Informat, Toronto, ON, Canada 6.Univ Toronto, Factor Inwentash Fac Social Work, 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. |
入库方式： OAI收割
来源：心理研究所
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