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COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling | |
Huangfu, Luwen1,2; Mo, Yiwen1; Zhang, Peijie3; Zeng, Daniel Dajun3,4; He, Saike3,4 | |
发表期刊 | JOURNAL OF MEDICAL INTERNET RESEARCH |
ISSN | 1438-8871 |
2022-02-08 | |
卷号 | 24期号:2页码:14 |
通讯作者 | He, Saike(saike.he@ia.ac.cn) |
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. |
关键词 | COVID-19 COVID-19 vaccine sentiment evolution topic modeling social media text mining |
DOI | 10.2196/31726 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | San Diego State University Master Research Scholarship ; Fowler College of Business |
项目资助者 | San Diego State University Master Research Scholarship ; Fowler College of Business |
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:000766780400002 |
出版者 | JMIR PUBLICATIONS, INC |
七大方向——子方向分类 | 社会计算 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47976 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | He, Saike |
作者单位 | 1.San Diego State Univ, Fowler Coll Business, San Diego, CA 92182 USA 2.San Diego State Univ, Ctr Human Dynam Mobile Age, San Diego, CA 92182 USA 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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