Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.

Autor:	Zang S; School of Public Health, Fudan University, Shanghai, China.; Global Health Institute, Fudan University, Shanghai, China., Zhang X; School of Public Health, Fudan University, Shanghai, China.; Global Health Institute, Fudan University, Shanghai, China., Xing Y; School of Public Health, Fudan University, Shanghai, China.; Global Health Institute, Fudan University, Shanghai, China., Chen J; School of Public Health, Fudan University, Shanghai, China., Lin L; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, SAR, China., Hou Z; School of Public Health, Fudan University, Shanghai, China.; Global Health Institute, Fudan University, Shanghai, China.
Jazyk:	angličtina
Zdroj:	Journal of medical Internet research [J Med Internet Res] 2023 Feb 10; Vol. 25, pp. e40057. Date of Electronic Publication: 2023 Feb 10.
DOI:	10.2196/40057
Abstrakt:	Background: Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. Objective: We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. Methods: We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Results: A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. Conclusions: The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies. (©Shujie Zang, Xu Zhang, Yuting Xing, Jiaxian Chen, Leesa Lin, Zhiyuan Hou. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.02.2023.)
Databáze:	MEDLINE
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