Evidence map›Paper›PMID 39602782›Full record

Observational studyJournal of medical Internet research2024

Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study.

Gabrielle O'Brien, Ronith Ganjigunta, Paramveer S Dhillon

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

3 authors.

Gabrielle O'BrienSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID 0009-0001-3198-3586
Ronith GanjiguntaDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States.ORCID 0009-0003-7292-8254
Paramveer S DhillonSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-0994-9488

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOnline wellness influencers (individuals dispensing unregulated health and wellness advice over social media) may have incentives to oppose traditional medical authorities. Their messaging may decrease the overall effectiveness of public health campaigns during global health crises like the COVID-19 pandemic.

objectiveThis study aimed to probe how wellness influencers respond to a public health campaign; we examined how a sample of wellness influencers on Twitter (rebranded as X in 2023) identified before the COVID-19 pandemic on Twitter took stances on the COVID-19 vaccine during 2020-2022. We evaluated the prevalence of provaccination messaging among wellness influencers compared with a control group, as well as the rhetorical strategies these influencers used when supporting or opposing vaccination.

methodsFollowing a longitudinal design, wellness influencer accounts were identified on Twitter from a random sample of tweets posted in 2019. Accounts were identified using a combination of topic modeling and hand-annotation for adherence to influencer criteria. Their tweets from 2020-2022 containing vaccine keywords were collected and labeled as pro- or antivaccination stances using a language model. We compared their stances to a control group of noninfluencer accounts that discussed similar health topics before the pandemic using a generalized linear model with mixed effects and a nearest-neighbors classifier. We also used topic modeling to locate key themes in influencer's pro- and antivaccine messages.

resultsWellness influencers (n=161) had lower rates of provaccination stances in their on-topic tweets (20%, 614/3045) compared with controls (n=242 accounts, with 42% or 3201/7584 provaccination tweets). Using a generalized linear model of tweet stance with mixed effects to model tweets from the same account, the main effect of the group was significant (β

conclusionsWellness influencers showed higher rates of vaccine opposition compared with other accounts that participated in health discourse before the pandemic. This pattern supports the theory that unregulated wellness influencers have incentives to resist messaging from establishment authorities such as public health agencies.

Indexed as

COVID-19COVID-19 VaccinesSocial MediaHealth PromotionHumansLongitudinal StudiesPandemicsSARS-CoV-2VaccinationCOVID-19 Vaccinesanti-vaccinationglobal healthhand-annotationinfodemiologylongitudinal designpandemicpersonal brandspublic healthsocial media, COVID-19, vaccinationtweetsTwittervaccinewellnesswellness influencers

Identifiers

PMID39602782
PMCPMC11635329

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.