Evidence map›Paper›PMID 38579627›Full record

ArticleSocial science & medicine (1982)2024

Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance.

Victoria Nelson, Bidhan Bashyal, Pang-Ning Tan, Young Anna Argyris

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Victoria NelsonDepartment of Advertising and Public Relations, College of Communication Arts and Sciences, Michigan State University, 404 Wilson Road, East Lansing, MI, 48864, USA. Electronic address: Nelsonv5@msu.edu.
Bidhan BashyalDepartment of Computer Science and Engineering, College of Engineering, Michigan State University, 428 S Shaw Lane, East Lansingm, MI, 48864, USA. Electronic address: bashyalb@msu.edu.
Pang-Ning TanDepartment of Computer Science and Engineering, College of Engineering, Michigan State University, 428 S Shaw Lane, East Lansingm, MI, 48864, USA. Electronic address: ptan@msu.edu.
Young Anna ArgyrisDepartment of Media and Information, College of Communication Arts and Sciences, Michigan State University, 404 Wilson Road, East Lansing, MI, 48864, USA. Electronic address: argyris@msu.edu.

Funding

Development of a vaccine informatics system and its application to identifying the impact of vaccine debate on immunization rates during a global pandemicR21LM013638 · NLM · MICHIGAN STATE UNIVERSITY · PI ARGYRIS, YOUNG ANNA, TAN, PANG-NING · 2021 to 2022
$348k
NLM NIH HHS R21 LM013638
6 · The paper itself

Abstract

The primary goal of this study is to examine the association between vaccine rhetoric on Twitter and the public's uptake rates of COVID-19 vaccines in the United States, compared to the extent of an association between self-reported vaccine acceptance and the CDC's uptake rates. We downloaded vaccine-related posts on Twitter in real-time daily for 13 months, from October 2021 to September 2022, collecting over half a billion tweets. A previously validated deep-learning algorithm was then applied to (1) filter out irrelevant tweets and (2) group the remaining relevant tweets into pro-, anti-, and neutral vaccine sentiments. Our results indicate that the tweet counts (combining all three sentiments) were significantly correlated with the uptake rates of all stages of COVID-19 shots (p < 0.01). The self-reported level of vaccine acceptance was not correlated with any of the stages of COVID-19 shots (p > 0.05) but with the daily new infection counts. These results suggest that although social media posts on vaccines may not represent the public's opinions, they are aligned with the public's behaviors of accepting vaccines, which is an essential step for developing interventions to increase the uptake rates. In contrast, self-reported vaccine acceptance represents the public's opinions, but these were not correlated with the behaviors of accepting vaccines. These outcomes provide empirical support for the validity of social media analytics for gauging the public's vaccination behaviors and understanding a nuanced perspective of the public's vaccine sentiment for health emergencies.

Indexed as

COVID-19COVID-19 VaccinesSelf ReportSocial MediaHumansPatient Acceptance of Health CareSARS-CoV-2United StatesVaccination HesitancyCOVID-19 Vaccines

Identifiers

PMID38579627
PMCPMC11077824

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.