Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Mar 21, 2022
Open Peer Review Period: Mar 21, 2022 - May 16, 2022
Date Accepted: Oct 8, 2022
Date Submitted to PubMed: Oct 11, 2022
(closed for review but you can still tweet)
Network analysis of the #VaccinesWork hashtag on Twitter in the context of the COVID-19 pandemic
ABSTRACT
Background:
Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Pro-vaccination users generated content promoting the vaccine, criticizing antivaccine beliefs and the safety and effectiveness of vaccines. Towards the end of April 2021, UNICEF, alongside other organizations, were promoting the hashtag 'VaccinesWork'.
Objective:
The aim of this paper is to determine whether health-related campaigns are effective through social media networks such as Twitter, by analysing the main messages shared and the organizations involved.
Methods:
The data set used in this article consists of 11,085 tweets containing the 'VaccinesWork' hashtag or were replied to or mentioned in these tweets from the 29th to the 30th of April, 2021. The data were retrieved using NodeXL and the network graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Results:
The study found that organizations such as WHO, UNICEF and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belong to academic journals with a high impact factor. Pro-vaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario.
Conclusions:
Twitter has an impact on the dissemination of information. The generation of truthful and contrasted information by leading health organizations through this social network is an effective way to reach the public.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.