Evidence map›Paper›PMID 37112729›Full record

ArticleVaccines2023

Social Media Sentiment about COVID-19 Vaccination Predicts Vaccine Acceptance among Peruvian Social Media Users the Next Day.

Ayse D Lokmanoglu, Erik C Nisbet, Matthew T Osborne, Joseph Tien, Sam Malloy, Lourdes Cueva Chacón, Esteban Villa Turek, Rod Abhari

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2023. 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
2.3field-weighted citation impact, top 11% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  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

8 authors at 4 institutions in 1 country.

Ayse D LokmanogluDepartment of Communication Studies, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-1699-2684
Erik C NisbetDepartment of Communication Studies, Northwestern University, Evanston, IL 60208, USA.
Matthew T OsborneDepartment of Mathematics, The Ohio State University, Columbus, OH 43210, USA.
Joseph TienDepartment of Mathematics, The Ohio State University, Columbus, OH 43210, USA.
Sam MalloyMITRE, McLean, VA 22102, USA.
Lourdes Cueva ChacónSchool of Journalism and Media Studies, San Diego State University, San Diego, CA 92182, USA.ORCID 0000-0002-8029-5720
Esteban Villa TurekDepartment of Communication Studies, Northwestern University, Evanston, IL 60208, USA.
Rod AbhariDepartment of Communication Studies, Northwestern University, Evanston, IL 60208, USA.
Northwestern University · USThe Ohio State University · USMitre (United States) · USSan Diego State University · US

Funding

Office of the Director of National Intelligence 2021-20102000001
6 · The paper itself

Abstract

Drawing upon theories of risk and decision making, we present a theoretical framework for how the emotional attributes of social media content influence risk behaviors. We apply our framework to understanding how COVID-19 vaccination Twitter posts influence acceptance of the vaccine in Peru, the country with the highest relative number of COVID-19 excess deaths. By employing computational methods, topic modeling, and vector autoregressive time series analysis, we show that the prominence of expressed emotions about COVID-19 vaccination in social media content is associated with the daily percentage of Peruvian social media survey respondents who are vaccine-accepting over 231 days. Our findings show that net (positive) sentiment and trust emotions expressed in tweets about COVID-19 are positively associated with vaccine acceptance among survey respondents one day after the post occurs. This study demonstrates that the emotional attributes of social media content, besides veracity or informational attributes, may influence vaccine acceptance for better or worse based on its valence.

Indexed as

COVID-19Perusentiment analysissocial amplification of risk frameworksocial mediavaccine acceptance

Identifiers

PMID37112729
PMCPMC10146388
OpenAlexW4363647907

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.