Divide in Vaccine Belief in COVID-19 Conversations: Implications for Immunization Plans
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Abstract
The 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.
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SciScore for 10.1101/2020.07.23.20160887: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Nevertheless, our analysis has several limitations. Our COVID-19 data stream is biased towards English speaking users. This could especially bias our results related to religious inclination and state media. Also, in our analysis for state media, religious accounts, and healthcare, we collected the most followed accounts in each group by …
SciScore for 10.1101/2020.07.23.20160887: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Nevertheless, our analysis has several limitations. Our COVID-19 data stream is biased towards English speaking users. This could especially bias our results related to religious inclination and state media. Also, in our analysis for state media, religious accounts, and healthcare, we collected the most followed accounts in each group by Google and Twitter user search. However, these may not represent all the accounts in a particular group that are followed by vaxxers or anti-vaxxers.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.07.23.20160887: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Also, in our analysis for state media, religious accounts, and healthcare, we collected the most followed accounts in each group by Google and Twitter user search. Googlesuggested: (Google, SCR_017097)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for …
SciScore for 10.1101/2020.07.23.20160887: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Also, in our analysis for state media, religious accounts, and healthcare, we collected the most followed accounts in each group by Google and Twitter user search. Googlesuggested: (Google, SCR_017097)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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