Evidence map›Paper›PMID 40888149›Full record

ArticleHealth expectations : an international journal of public participation in health care and health policy2025

Impact of YouTube User-Generated Content on News Dissemination and Youth Information Reception.

Wu Chunqiong, Jiang Shan, Sun Jianhong, Liu Yingqi

Abstract read
In one paragraph

Article in Health expectations : an international journal of public participation in health care and health policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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. 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

4 authors.

Wu ChunqiongSchool of Economics and Management, Yango University, Fuzhou, Fujian Province, China.ORCID 0009-0003-9775-9696
Jiang ShanSchool of Economics and Management, Yango University, Fuzhou, Fujian Province, China.
Sun JianhongSchool of Business, Ningbo University, Ningbo, Zhejiang Province, China.
Liu YingqiSchool of Economics and Management, Yango University, Fuzhou, Fujian Province, China.

Funding

This work was supported by the 2023 Ministry of Education Humanities and Social Sciences General Project, 'From Behavioural Laws to Decision-making Models: The Impact of Information Search Modes on the Communication Effect of Data News in Liquid Scenarios' (23YJA840020). 2022 'I Contribute Good Suggestions for Building a New Fujian' (United Front Special Project), 'New Data News and Group Cognition Research' (JAT22043); the 2022 Fujian Province Young and Middle-aged Teachers Education and Research Project (Social Sciences), 'Data News and Smart Media KOL Generation' (JAS22208); the 2023 Fuzhou City Social Science Planning General Project, 'Yongtai Rural Talent Capacity Supported by Digital Technology' (2023FZC22); the 2024 Fujian Provincial Social Science Fund project "Research on the Paths to Enhancing the International Communication Capacity of Traditional Fujian Culture from the Perspective of Integrated Development" (FJ2024C054).
6 · The paper itself

Abstract

backgroundUser-generated content (UGC) on YouTube has reshaped news dissemination, fostered engagement, raised concerns about credibility, algorithmic influence and the spread of misinformation. This study addresses the gap in understanding how UGC engagement, trust and algorithmic awareness influence digital news consumption.

methodsA convergent parallel mixed-methods design was employed, integrating survey data (n = 100), qualitative interviews and content analysis of 200 YouTube news videos. Data were collected over 6 weeks. Quantitative analyses included ANOVA, multivariate regression and structural equation modelling (SEM), while qualitative data were thematically analysed to contextualise statistical findings.

resultsUGC news consumption (M = 3.21, SD = 1.14) exceeded traditional news (M = 2.95, SD = 1.20), with trust in UGC (M = 3.48, SD = 1.05) surpassing traditional sources (M = 3.12, SD = 1.17). SEM analysis confirmed that UGC engagement significantly increased trust (β = 0.42, p < 0.001), while algorithmic influence negatively affected trust (β = -0.33, p = 0.015). Sensationalist content attracted higher engagement (30.0%) but had lower credibility, with misinformation prevalent in 38.0% of analysed videos.

conclusionFindings highlight the need for platform transparency, stronger content verification and policy interventions to balance engagement-driven algorithms and news credibility. Media literacy initiatives are crucial for equipping users with the critical evaluation skills they need.

Indexed as

Information DisseminationSocial MediaAdolescentAdultCommunicationFemaleHumansInterviews as TopicMaleQualitative ResearchSurveys and QuestionnairesTrustYoung Adultalgorithmic influencedigital engagementmedia literacymisinformationtrust in mediauser‐generated contentYouTube news

Identifiers

PMID40888149
PMCPMC12399985

What OpenQuestion holds

Textmetadata
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.