Chunqiong Wu, Shan Jiang, Jianhong Sun, Yingqi Liu
School of Economics and Management, Yango University, Fuzhou, Fujian Province, China.
School of Business, Ningbo University, Ningbo, Zhejiang Province, China.
Health Expect. 2025 Oct;28(5):e70408. doi: 10.1111/hex.70408.
User-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.
A 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.
UGC 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.
Findings 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.
YouTube 上的用户生成内容（UGC）重塑了新闻传播方式，促进了用户参与度，但也引发了对可信度、算法影响和错误信息传播的担忧。本研究旨在填补在理解 UGC 参与度、信任度和算法认知如何影响数字新闻消费方面的空白。
采用了收敛平行混合方法设计，整合了调查数据（n = 100）、定性访谈以及对 200 个 YouTube 新闻视频的内容分析。数据收集历时 6 周。定量分析包括方差分析、多元回归和结构方程模型（SEM），而定性数据则进行了主题分析以解释统计结果。
UGC 新闻消费量（M = 3.21，标准差 = 1.14）超过了传统新闻（M = 2.95，标准差 = 1.20），对 UGC 的信任度（M = 3.48，标准差 = 1.05）超过了传统新闻来源（M = 3.12，标准差 = 1.17）。SEM 分析证实，UGC 参与度显著提高了信任度（β = 0.42，p < 0.001），而算法影响对信任度产生了负面影响（β = -0.33，p = 0.015）。耸人听闻的内容吸引了更高的参与度（30.0%），但可信度较低，在 38.0%的分析视频中存在错误信息。
研究结果凸显了平台透明度、更强的内容核实和政策干预的必要性，以平衡由参与度驱动的算法和新闻可信度。媒体素养倡议对于培养用户所需的批判性评估技能至关重要。
