了解用户如何在短视频中识别健康错误信息:使用PLS-SEM和fsQCA进行综合分析
Ruojia Wang1, Huiwen Yang2,3, Yue Wang1
1School of Management, Beijing University of Chinese Medicine, Beijing, China.
Frontiers in public health
|December 24, 2025
概括
用户通过信息质量,用户特征和外部因素来识别短视频中的健康错误信息. 了解这些因素有助于打击在线虚假健康信息的传播.
科学领域:
- 数字健康数字健康
- 卫生沟通健康沟通
- 信息科学 信息科学 信息科学
背景情况:
- 短视频平台是健康信息的主要来源.
- 人工智能产生的内容和低创造障碍加剧了健康错误信息.
- 了解用户对健康错误信息的辨别是至关重要的.
研究的目的:
- 识别影响用户识别短视频中的健康错误信息的因素.
- 用定性和定量方法分析这些因素的因果结构.
- 提出减轻数字平台上的健康错误信息的策略.
主要方法:
- 基于47次深度采访的理论分析.
- 在279个有效样本中进行了问卷调查.
- 部分最小平方结构方程建模 (PLS-SEM) 和模糊集定性比较分析 (fsQCA).
主要成果:
- 影响辨别能力的三个关键类别:信息质量,用户特征和外部环境.
- PLS-SEM模型解释了47.8%的辨别差异.
- 内容逻辑,叙事表达,信息结构,认知水平和外部影响显著影响了辨别能力.
结论:
- 用户使用分析评估,内容暗示依赖或认知暗示依赖来辨别错误信息.
- 研究结果表明,应采取多层次的干预措施 (个人,平台,政策) 来打击健康错误信息.
- 有效的策略需要解决信息质量,用户特征和外部影响.
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