Evidence map›Paper›PMID 37420218›Full record

ArticleBMC proceedings2023

Social network analysis of COVID-19 vaccine YouTube videos in Odisha, India: mapping the channel network and analyzing comment sentiment.

Neil Alperstein, Paola Pascual-Ferrá, Rohini Ganjoo, Ananya Bhaktaram, Julia Burleson, Daniel J Barnett, Amelia M Jamison, Eleanor Kluegel, Satyanarayan Mohanty, Peter Z Orton and 3 more

Erratum issuedOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 17% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Neil AlpersteinDepartment of Communication, Loyola University, Baltimore, Maryland, USA. nalperstein@loyola.edu.ORCID http://orcid.org/0000-0003-3906-3474
Paola Pascual-FerráDepartment of Communication, Loyola University, Baltimore, Maryland, USA.
Rohini GanjooSchool of Medical and Health Sciences, George Washington University, WashingtonD.C., USA.
Ananya BhaktaramBloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Julia BurlesonBloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Daniel J BarnettBloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Amelia M JamisonBloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Eleanor KluegelDepartment of Communication, Loyola University, Baltimore, Maryland, USA.
Satyanarayan MohantyDevelopment Corner (D-COR), Bhubaneswar, Odisha, India.
Peter Z OrtonWellflix Inc., Hillsborough, USA.
Manoj ParidaDevelopment Corner (D-COR), Bhubaneswar, Odisha, India.
Sidharth RathSwasthya Plus Network, Bhubaneswar, Odisha, India.
Rajiv RimalBloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. rimal@jhu.edu.
Johns Hopkins University · USLoyola University Maryland · USAnthem (United States) · USGeorge Washington University · US

Funding

Vaccine Confidence Fund VCF-019
6 · The paper itself

Abstract

India has reported more than 35 million confirmed cases of COVID-19 and nearly half a million cumulative deaths. Although vaccination rates for the first vaccine dose are quite high, one-third of the population has not received a second shot. Due to its widespread use and popularity, social media can play a vital role in enhancing vaccine acceptance. This study in a real-world setting utilizes YouTube videos in Odisha, India where the platform has deep penetration among the 18-35 target population, and secondarily their family and peers. Two contrasting videos were launched on the YouTube platform to examine how those videos operate within the broader recommender and subscription systems that determine the audience reach. Video analytics, algorithms for recommended videos, visual representation of connections created, centrality between the networks, and comment analysis was conducted. The results indicate that the video with a non-humorous tone and collectivistic appeal delivered by a female protagonist performed best with regard to views and time spent watching the videos. The results are of significance to health communicators who seek to better understand the platform mechanisms that determine the spread of videos and measures of viewer reactions based on viewer sentiment.

Indexed as

COVID-19 vaccine acceptanceMessage designSentiment analysisSocial network analysisYouTube recommender system

Identifiers

PMID37420218
PMCPMC10329293
OpenAlexW4383555700

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.