Misinformation versus Facts:Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake

作     者：Hanjia Lyu Zihe Zheng Jiebo Luo 

作者机构：Department of Computer ScienceUniversity of RochesterRochesterUSA Goergen Institute for Data ScienceUniversity of RochesterRochesterUSA 

出 版 物：《Health Data Science》 (健康数据科学（英文）)

年 卷 期：2022年第2022卷第1期

页      面：136-143页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：a University of Rochester Research Award and NIH grant RF1AG063811-01S2 

主　　题：vaccination discussions education 

摘      要：Background. There is a lot of fact-based information and misinformation in the online discourses and discussions about theCOVID-19 vaccines. Method. Using a sample of nearly four million geotagged English tweets and the data from the CDCCOVID Data Tracker, we conducted the Fama-MacBeth regression with the Newey-West adjustment to understand theinfluence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the US from April 19when US adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education,and the pandemic severity. We identified the tweets related to either misinformation or fact-based news by analyzing theURLs. Results. One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (B = −0:87,SE = 0:25, and p :001) in the number of daily new vaccinated people per hundred. No significant relationship was foundbetween the percentage of fake-news-related users and the vaccination rate. Conclusion. The negative association between thepercentage of fact-related users and the vaccination rate might be due to a combination of a larger user-level influence and thenegative impact of online social endorsement on vaccination intent.