图书情报工作

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2026 , Vol. 70 >Issue 4: 84 - 99

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.04.007

研究论文

个性化内容推荐环境下社交媒体用户算法素养研究

  • 王捷 ,
  • 闫蒲 ,
  • 马万腾
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  • 1 北京大学信息管理系 北京 100871;
    2 北京大学教育学院 北京 100871
王捷，硕士研究生;闫蒲，助理教授，研究员，博士，通信作者，E-mail:puyan@pku.edu.cn;马万腾，硕士研究生。

收稿日期: 2024-12-27

  修回日期: 2025-04-07

  网络出版日期: 2026-02-12

基金资助

本文系北京市社会科学基金青年项目“北京公民算法素养研究”（项目编号:22XCC013）和国家自然科学基金青年项目“算法鸿沟影响因素与作用机制研究”（项目编号:72304017）研究成果之一。

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Research on the Algorithmic Literacy among Social Media Users in the Personalization Recommendation Environment

  • Wang Jie ,
  • Yan Pu ,
  • Ma Wanteng
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  • 1 Department of Information Management, Peking University, Beijing 100871;
    2 Graduate School of Education, Peking University, Beijing 100871

Received date: 2024-12-27

  Revised date: 2025-04-07

  Online published: 2026-02-12

Supported by

This work is supported by the youth project of Social Science Foundation of Beijing titled “Research on Beijing Citizens' Algorithm Literacy” (Grant No. 22XCC013), and the youth project of National Natural Science Foundation of China titled “Research on the Factors and Mechanisms of Algorithm Divide” (Grant No. 72304017).

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摘要

[目的/意义]个性化内容推荐算法已深度融入互联网用户的日常生活，但同时也引发舆论极化、价格歧视和虚假信息传播等问题。对社交媒体用户的算法素养水平进行测量和评估具有重要的学术和实践意义。[方法/过程]采用定量问卷和定性访谈相结合的方法，收集853份在线问卷和15名社交媒体用户的深度焦点小组访谈数据。通过因子分析与回归分析，识别算法素养的影响因素，并结合深度访谈揭示用户应对算法环境的挑战和策略。[结果/结论]用户算法素养与教育程度、数字技能和社交媒体使用经验密切相关。多数用户对个性化内容推荐算法持较为消极的态度，不同素养水平会影响其应对策略的选择。研究还进一步探讨用户在算法环境中面临的主体性降低及参与平台规则塑造困难等问题。研究为算法素养的测量提供了参考指标，为在新技术环境下弥合社会不平等、确保个体用户保持在个性化算法环境中的主体性提出建议。

关键词： 算法素养; 信息分化; 内容推荐算法; 多元方法

本文引用格式

王捷 , 闫蒲 , 马万腾 . 个性化内容推荐环境下社交媒体用户算法素养研究[J]. 图书情报工作, 2026 , 70(4) : 84 -99 . DOI: 10.13266/j.issn.0252-3116.2026.04.007

Abstract

[Purpose/Significance] Personalized content recommendation algorithms have become deeply integrated into the daily lives of Internet users in China. However, they have also led to issues such as polarization, price discrimination, and the spread of false information. Therefore, it is important to measure and evaluate the levels of algorithm literacy among social media users. [Method/Process] Using a mixed-methods approach combining quantitative questionnaires and qualitative interviews, we collected data from 853 online surveys and conducted in-depth focus group interviews with 15 social media users. Through factor analysis and regression analysis, we identified the influencing factors of algorithmic literacy. Additionally, the in-depth interviews were used to reveal the challenges users face and the strategies they employ to navigate algorithmic environments. [Result/Conclusion] The study showed that users’ algorithmic literacy was closely related to education levels, digital skills and social media usage experiences. Most users hold relatively negative attitudes towards personalized content recommendation. However, users adopted different strategies to cope with algorithms depending on their levels of algorithm literacy. It also discussed the challenges in the algorithmic environment, such as a decline in agency and difficulty in participating in shaping platform rules. This study can contribute to constructing effective measurements of digital literacy and offer suggestions on how to bridge social inequalities in the new technological environment and ensure individual users maintain their agency in algorithmic environments.

Key words： algorithmic literacy; information divide; content recommendation algorithm; mixed methods

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