Evidence map›Paper›PMID 41550301›Full record

ArticleJournal of the Southern Association for Information Systems2024

The Celebrity Factor: Exploring the Impact of Influencers on COVID-19 Vaccine Sentiment through Bayesian Modeling of Time Series.

Abhishek Shah, Shweta Shah, Bill Rand, Xiaoxia Champon

Abstract read
In one paragraph

Article in Journal of the Southern Association for Information Systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Abhishek ShahGreen Level High School.
Shweta ShahGreen Level High School.
Bill RandNorth Carolina State University.
Xiaoxia ChamponNorth Carolina State University.

Funding

Integrated Biostatistical Training for CVD ResearchT32HL079896 · NHLBI · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI Sean M O'Brien, Ana-Maria Staicu · 2006 to 2026
$4.1M
NHLBI NIH HHS T32 HL079896
6 · The paper itself

Abstract

Online social networks allow for information to rapidly propagate throughout the world, and opinions expressed on such platforms can influence people's decisions. During the COVID-19 pandemic, many influential public figures used these social networks to share their opinions about the vaccines developed to combat the virus. Many influencers encouraged vaccination, and a considerable number also expressed doubt and skepticism over the efficacy of the vaccines. This study modeled the impact that eleven influencers' statements had on the overall sentiment towards COVID-19 vaccines, as expressed on Twitter. Sentiment is measured by collecting a series of publicly-available tweets made regarding the vaccine during the pandemic, and assigning each a sentiment score based on the VADER lexicon. Several models were used to analyze the impact of the influencers' statements, including linear, sequential and tree-based models. The results were obtained by constructing a Bayesian structural time series model based on each model's counterfactual estimate. The results found that influencers who share messages encouraging vaccination generally tend to increase the number of "pro-vaccination" tweets over the next 20 days. Influencers sharing "anti-vaccination" messages sometimes resulted in a decrease in anti-vaccine tweets, and other times in an increase over the next 20 days. The results from this study provide an introductory look into the complex issue of vaccine hesitancy and the effect of influencers on vaccine messaging, and inform public health strategy regarding this issue.

Indexed as

BSTSCelebritycelebrity influenceCOVIDCOVID-19EndorsementInfluenceInfluencermachine learningsentiment analysistime-series forecastingTwitterVaccineVaccine hesitancyvaccinesVADER lexicon

Identifiers

PMID41550301
PMCPMC12805432

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.