Evidence map›Paper›PMID 40540499›Full record

ArticlePloS one2025

Attachment beyond the screen: The influences of demographic factors and parasocial relationships on social media use in Qatar.

Ruining Jin, Tam-Tri Le

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

2 authors.

Ruining JinInstitute of Higher Education, Beijing University of Technology, Beijing, China.ORCID 0000-0002-8542-7614
Tam-Tri LeIndependent researcher, Ho Chi Minh City, Vietnam.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost studies on social media usage and parasocial relationships (PSRs) have been conducted in WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies, potentially overlooking the unique cultural, social, and economic factors present in non-WEIRD contexts. Examining these phenomena in a non-WEIRD setting is essential for a comprehensive understanding of social media's global impact.

methodsSecondary data from 574 participants in Qatar who followed Instagram influencers were analyzed using Bayesian analyses aided by Markov Chain Monte Carlo (MCMC) algorithms to examine the relationships between social media usage time, PSRs, and demographic factors.

findingsThe analysis results show that, regarding linear effects, a stronger parasocial relationship with Instagram influencer(s) is associated with higher daily social media usage time. Meanwhile, being male, being older, and having higher incomes all have negative associations with daily social media usage time. When parasocial relationships and the three demographic factors are seen in their interactions, negative associations with social media usage were also found in a similar pattern. To elaborate, among those with high parasocial relationship degrees, females, young people, and poor people tend to use social media for more hours each day.

conclusionsThis study highlights that demographic factors such as gender, age, and income in their interactions with parasocial relationships are associated with social media usage time within the non-WEIRD social context of Qatar. The findings underscore the necessity of considering the specific local cultural settings when studying social media behaviors.

Indexed as

Interpersonal RelationsSocial MediaAdolescentAdultAge FactorsBayes TheoremDemographyFemaleHumansMaleMiddle AgedQatarYoung Adult

Identifiers

PMID40540499
PMCPMC12180621

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.