图书情报知识 ›› 2025, Vol. 42 ›› Issue (6): 51-61.doi: 10.13366/j.dik.2025.06.051

• 专题(2)·以人为本的AI拟人化设计 • 上一篇    下一篇

拟人化图像的真实性感知:社交媒体中数字人形象的辨识与信任研究

金帆, 宋颖, 张鹏翼   

  1. 北京大学信息管理系,北京,100871
  • 出版日期:2025-11-10 发布日期:2026-01-17
  • 通讯作者: 张鹏翼(ORCID: 0000-0003-0624-6776),博士,长聘副教授,研究方向:信息与知识组织、信息行为与认知,Email: pengyi@pku.edu.cn。
  • 作者简介:金帆(ORCID: 0009-0003-6343-1529),博士研究生,研究方向:人智交互、智慧图书馆,Email: jinfan@pku.edu.cn;宋颖(ORCID: 0009-0004-6573-4619),硕士研究生,研究方向:信息服务与用户,Email: songying@stu.pku.edu.cn。
  • 基金资助:
    本文系国家社会科学基金重大项目“人本人工智能驱动的信息服务体系重构与应用研究”(22&ZD325)的研究成果之一。

The Authenticity Perception in Anthropomorphic Images: Identification and Trust of Digital Human Profiles in Social Media

JIN Fan, SONG Ying, ZHANG Pengyi   

  1. Department of Information Management, Peking University, Beijing, 100871
  • Online:2025-11-10 Published:2026-01-17
  • Contact: Correspondence should be addressed to ZHANG Pengyi, Email: pengyi@pku.edu.cn, ORCID: 0000-0003-0624-6776
  • Supported by:
    This is an outcome of the Major Project "Information Service System Restructuring and Application Driven by Human-Centered Artificial Intelligence"(22&ZD325)supported by National Social Science Foundation of China.

摘要: [目的/意义]拟人化数字人形象在社交媒体中的使用引发人们对内容真实性和可信度的关注。探索用户对数字人形象的辨识能力有利于社交媒体对人工智能生成内容的管理。[研究设计/方法]招募了60位参与者进行眼动跟踪实验,验证数字人形象辨识的影响因素以及感知真实性对可信度的影响。[结论/发现]用户对社交媒体中数字人形象的辨识能力有限;视觉特征(图片中的背景和人物吸引力、性别)显著影响用户对数字人形象的感知真实性。同时,感知真实性显著提升图文内容的可信度,用户兴趣在真实性-可信度路径中起显著调节作用。[创新/价值]构建数字人形象辨识的影响因素模型,完善对人工智能生成内容的辨识机制和人机信任的解释框架,为数字人形象使用和管理提供参考。

关键词: 真实性感知, 数字人, 社交媒体, 外貌吸引力, 人机信任

Abstract: [Purpose/Significance] The use of anthropomorphic digital human images in social media has raised concerns about the authenticity and trustworthiness of the content. Exploring users' ability to identify digital human images is crucial for the management of AI-generated content in social media. [Design/Methodology] 60 participants were recruited for an eye-tracking experiment to validate the influencing factors of digital human image identification and the role of perceived authenticity in content trustworthiness. [Findings/Conclusion] Users exhibit limited ability to identify digital human images on social media. Visual characteristics, such as background, attractiveness and gender of characters in images, significantly influence users' perception of the authenticity of digital human images. Meanwhile, perceived authenticity significantly improves the trustworthiness of graphic posts. Users' interest plays a significant moderating role in the relationship between authenticity perception and content trustworthiness. [Originality/Value] This study constructs a model of factors influencing digital human image identification and improves the identification mechanism for AI-generated content and the explanatory framework for human-machine trust, which provide a reference for the use and management of digital human images.

Keywords: Authenticity perception, Digital human, Social media, Physical attractiveness, Human-machine trust