A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

被引:5
|
作者
Park, Susan [1 ,2 ]
Suh, Young-Kyoon [3 ,4 ,5 ]
机构
[1] Seoul Natl Univ, Inst Hlth & Environm, Seoul, South Korea
[2] Chung Ang Univ, Inst Community Care & Hlth Equ, Seoul, South Korea
[3] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea
[4] Kyungpook Natl Univ, Dept Data Convergence Comp, Daegu, South Korea
[5] Kyungpook Natl Univ, Sch Comp Sci & Engn, 80 Daehak ro, Daegu 41566, South Korea
基金
新加坡国家研究基金会;
关键词
COVID-19; vaccine; vaccination; Pfizer; Moderna; AstraZeneca; Janssen; Novavax; OPINION; NUMBER;
D O I
10.2196/42623
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare. Objective: This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs. Methods: We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis. Results: The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment. Conclusions: Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination.
引用
收藏
页数:14
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