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COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling

文献类型：期刊论文

作者Huangfu, Luwen3,4; Mo, Yiwen4; Zhang, Peijie2; Zeng, Daniel Dajun1,2; He, Saike1,2
刊名JOURNAL OF MEDICAL INTERNET RESEARCH
出版日期2022-02-08
卷号24期号:2页码:14
ISSN号1438-8871
关键词COVID-19 COVID-19 vaccine sentiment evolution topic modeling social media text mining
DOI10.2196/31726
通讯作者He, Saike(saike.he@ia.ac.cn)
英文摘要Background: COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment-based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines. Objective: In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved. Methods: We collected 1, 122, 139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857, 128 tweets. We then applied sentiment-based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns. Results: Overall, 398, 661 (46.51%) were positive, 204, 084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251, 979/405, 560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405, 560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115, 206/205, 592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17, 154/205, 592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign. Conclusions: To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment-based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.
资助项目San Diego State University Master Research Scholarship ; Fowler College of Business
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:000766780400002
资助机构San Diego State University Master Research Scholarship ; Fowler College of Business
源URL[http://ir.ia.ac.cn/handle/173211/47975]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者He, Saike
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.San Diego State Univ, Ctr Human Dynam Mobile Age, San Diego, CA 92182 USA
4.San Diego State Univ, Fowler Coll Business, San Diego, CA 92182 USA
推荐引用方式
GB/T 7714		 Huangfu, Luwen,Mo, Yiwen,Zhang, Peijie,et al. COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2022,24(2):14.
APA Huangfu, Luwen,Mo, Yiwen,Zhang, Peijie,Zeng, Daniel Dajun,&He, Saike.(2022).COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling.JOURNAL OF MEDICAL INTERNET RESEARCH,24(2),14.
MLA Huangfu, Luwen,et al."COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling".JOURNAL OF MEDICAL INTERNET RESEARCH 24.2(2022):14.

入库方式： OAI收割

来源：自动化研究所

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