| Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter | |
Xue, Jia1,2; Chen, Junxiang3; Chen, Chen4 ; Zheng, Chengda2; Li, Sijia5,6; Zhu, Tingshao5
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| 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 | 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. |
| 2020-09-25 | |
| DOI | 10.1371/journal.pone.0239441 |
| 发表期刊 | PLOS ONE
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| ISSN | 1932-6203 |
| 卷号 | 15期号:9页码:12 |
| 期刊论文类型 | 实证研究 |
| 收录类别 | SCI |
| 资助项目 | National Natural Science Foundation of China[31700984] ; Artificial Intelligence Lab for Justice at University of Toronto, Canada |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| WOS研究方向 | Science & Technology - Other Topics |
| WOS类目 | Multidisciplinary Sciences |
| WOS记录号 | WOS:000576266600002 |
| WOS分区 | Q2 |
| 资助机构 | National Natural Science Foundation of China ; Artificial Intelligence Lab for Justice at University of Toronto, Canada |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.psych.ac.cn/handle/311026/32968 |
| 专题 | 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 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 |
| 通讯作者单位 | 中国科学院心理研究所 |
| 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. |
| 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 | ||
| Public discourse and（1113KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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