Divide in Vaccine Belief in COVID-19 Conversations: Implications for Immunization Plans

Author:

Tyagi AmanORCID,Carley Kathleen M.ORCID

Abstract

AbstractThe development of a viable COVID-19 vaccine is a work in progress, but the success of the immunization campaign will depend upon public acceptance. In this paper, we classify Twitter users in COVID-19 discussion into vaccine refusers (anti-vaxxers) and vaccine adherers (vaxxers) communities. We study the divide between anti-vaxxers and vaxxers in the context of whom they follow. More specifically, we look at followership of 1) the U.S. Congress members, 2) four major religions (Christianity, Hinduism, Judaism and Islam), 3) accounts related to the healthcare community, and 4) news media accounts. Our results indicate that there is a partisan divide between vaxxers and anti-vaxxers. We find a religious community with a higher than expected fraction of anti-vaxxers. Further, we find that the variance of vaccine belief within the news media accounts operated by Russian and Iranian governments is higher compared to news media accounts operated by other governments. Finally, we provide messaging and policy implications to inform the COVID-19 vaccine and future vaccination plans.

Publisher

Cold Spring Harbor Laboratory

Reference28 articles.

1. Alam, F. , Dalvi, F. , Shaar, S. , Durrani, N. , Mubarak, H. , Nikolov, A. , Da San Martino, G. , Abdelali, A. , Sajjad, H. , Darwish, K. , et al.: Fighting the covid-19 infodemic in social media: A holistic perspective and a call to arms (2020)

2. Neural machine translation by jointly learning to align and translate;arXiv preprint,2014

3. Beskow, D. , Carley, K.M. , Bisgin, H. , Hyder, A. , Dancy, C. , Thomson, R. : Introducing bothunter: A tiered approach to detection and characterizing automated activity on twitter. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Springer (2018)

4. Beskow, D.M. , Carley, K.M. : You are known by your friends: Leveraging network metrics for bot detection in twitter (2020-forthcoming)

5. Bessi, A. , Ferrara, E. : Social bots distort the 2016 us presidential election online discussion. First Monday 21(11-7) (2016)

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