Evidence map›Paper›PMID 36368060›Full record

ArticleSocial science & medicine (1982)2022

When the influencer says jump! How influencer signaling affects engagement with COVID-19 misinformation.

Ben Wasike

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

1 author.

Ben WasikeDepartment of Communication, University of Texas Rio Grande Valley, Ph.D. Granting Institution: Louisiana State University (2005), One West University Boulevard, Brownsville, TX, 78520, USA. Electronic address: ben.wasike@utrgv.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With signaling theory, credibility, and social media engagement (SME) as guiding frameworks, this study used an experiment to examine how social media influencers (SMIs) affect how people engage with COVID-19 misinformation. SMI-promoted information elicited more SME, credibility, and purchase likelihood than non-SMI promoted information. The most effective message was a post promoted by an SMI that contained detailed information about an authentic product. However, data indicated nuance regarding the effect of SMIs. The authenticity of the information as well as the amount of detail in the post played a role. Additionally, mediated effects analysis showed that the impact of SME on purchase likelihood was higher among non-SMI followers. Data suggests that using a multi-signal messaging approach is suitable regardless of promotion by an SMI. This has important implications to public health messaging and the author discusses how health agencies may effectively signal information to the public.

Indexed as

COVID-19Social MediaCommunicationHumansProbabilityPublic HealthCOVID-19CredibilityMisinformationSignalingSocial media influencersStructural equation modeling

Identifiers

PMID36368060
PMCPMC9643098

What OpenQuestion holds

Textmetadata
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.