{"title":"对 COVID-19 疫苗的情感:从推特分析中了解疫苗接种的犹豫和意愿","authors":"","doi":"10.1016/j.jpolmod.2024.05.005","DOIUrl":null,"url":null,"abstract":"<div><p>The declaration by the World Health Organization and government-initiated actions by different countries for the COVID-19 vaccine have led to the rapid evolution of sentiments on various social media platforms. Real-time data related to vaccination<span> has grown the need to anticipate the changes in vaccine uptake. Using Twitter dataset, the study models different emotions and their associated word. The emotions are majorly classified into hesitancy and willingness for vaccination. The study categorizes the tweets into pre-launch, post-launch, and booster doses of the COVID-19 vaccine. Based on comparative analysis, most sentiments were related to hesitancy for vaccination during pre-launch. In post-launch, the majority of sentiments were oriented towards willingness for vaccination. However, during the booster dose, the sentiments were oriented toward happy, adequate, and free emotions. Over the time period, the willingness of the COVID-19 vaccine has improved. The practitioners and policymakers can obtain real-time sentiments based on this approach and strategize the long-term vaccination policy for COVID-19 and other vaccination programs.</span></p></div>","PeriodicalId":48015,"journal":{"name":"Journal of Policy Modeling","volume":"46 5","pages":"Pages 964-984"},"PeriodicalIF":3.5000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The emotions for COVID-19 vaccine: Insights from Twitter analytics about hesitancy and willingness for vaccination\",\"authors\":\"\",\"doi\":\"10.1016/j.jpolmod.2024.05.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The declaration by the World Health Organization and government-initiated actions by different countries for the COVID-19 vaccine have led to the rapid evolution of sentiments on various social media platforms. Real-time data related to vaccination<span> has grown the need to anticipate the changes in vaccine uptake. Using Twitter dataset, the study models different emotions and their associated word. The emotions are majorly classified into hesitancy and willingness for vaccination. The study categorizes the tweets into pre-launch, post-launch, and booster doses of the COVID-19 vaccine. Based on comparative analysis, most sentiments were related to hesitancy for vaccination during pre-launch. In post-launch, the majority of sentiments were oriented towards willingness for vaccination. However, during the booster dose, the sentiments were oriented toward happy, adequate, and free emotions. Over the time period, the willingness of the COVID-19 vaccine has improved. The practitioners and policymakers can obtain real-time sentiments based on this approach and strategize the long-term vaccination policy for COVID-19 and other vaccination programs.</span></p></div>\",\"PeriodicalId\":48015,\"journal\":{\"name\":\"Journal of Policy Modeling\",\"volume\":\"46 5\",\"pages\":\"Pages 964-984\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Policy Modeling\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0161893824000462\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Policy Modeling","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0161893824000462","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
The emotions for COVID-19 vaccine: Insights from Twitter analytics about hesitancy and willingness for vaccination
The declaration by the World Health Organization and government-initiated actions by different countries for the COVID-19 vaccine have led to the rapid evolution of sentiments on various social media platforms. Real-time data related to vaccination has grown the need to anticipate the changes in vaccine uptake. Using Twitter dataset, the study models different emotions and their associated word. The emotions are majorly classified into hesitancy and willingness for vaccination. The study categorizes the tweets into pre-launch, post-launch, and booster doses of the COVID-19 vaccine. Based on comparative analysis, most sentiments were related to hesitancy for vaccination during pre-launch. In post-launch, the majority of sentiments were oriented towards willingness for vaccination. However, during the booster dose, the sentiments were oriented toward happy, adequate, and free emotions. Over the time period, the willingness of the COVID-19 vaccine has improved. The practitioners and policymakers can obtain real-time sentiments based on this approach and strategize the long-term vaccination policy for COVID-19 and other vaccination programs.
期刊介绍:
The Journal of Policy Modeling is published by Elsevier for the Society for Policy Modeling to provide a forum for analysis and debate concerning international policy issues. The journal addresses questions of critical import to the world community as a whole, and it focuses upon the economic, social, and political interdependencies between national and regional systems. This implies concern with international policies for the promotion of a better life for all human beings and, therefore, concentrates on improved methodological underpinnings for dealing with these problems.