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• W4220790350 endingPage "104054" @default.
• W4220790350 startingPage "104054" @default.
• W4220790350 abstract "Vaccination is the most effective way to provide long-lasting immunity against viral infection; thus, rapid assessment of vaccine acceptance is a pressing challenge for health authorities. Prior studies have applied survey techniques to investigate vaccine acceptance, but these may be slow and expensive. This study investigates 29 million vaccine-related tweets from August 8, 2020 to April 19, 2021 and proposes a social media-based approach that derives a vaccine acceptance index (VAI) to quantify Twitter users’ opinions on COVID-19 vaccination. This index is calculated based on opinion classifications identified with the aid of natural language processing techniques and provides a quantitative metric to indicate the level of vaccine acceptance across different geographic scales in the U.S. The VAI is easily calculated from the number of positive and negative Tweets posted by a specific users and groups of users, it can be compiled for regions such a counties or states to provide geospatial information, and it can be tracked over time to assess changes in vaccine acceptance as related to trends in the media and politics. At the national level, it showed that the VAI moved from negative to positive in 2020 and maintained steady after January 2021. Through exploratory analysis of state- and county-level data, reliable assessments of VAI against subsequent vaccination rates could be made for counties with at least 30 users. The paper discusses information characteristics that enable consistent estimation of VAI. The findings support the use of social media to understand opinions and to offer a timely and cost-effective way to assess vaccine acceptance." @default.
• W4220790350 created "2022-04-03" @default.
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• W4220790350 date "2022-05-01" @default.
• W4220790350 modified "2023-10-16" @default.
• W4220790350 title "Dynamic assessment of the COVID-19 vaccine acceptance leveraging social media data" @default.
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• W4220790350 doi "https://doi.org/10.1016/j.jbi.2022.104054" @default.
• W4220790350 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35331966" @default.
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