研究论文

移动短视频用户算法抵抗行为影响因素与拓扑路径研究

  • 毛太田 ,
  • 丁天曲 ,
  • 马家伟
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  • 1 湘潭大学公共管理学院 湘潭 411105;
    2 吉林大学商学与管理学院 长春 130012
毛太田,教授,博士,博士生导师;丁天曲,博士研究生,通信作者,E-mail:dingtianqu@163.com;马家伟,博士研究生。

收稿日期: 2025-03-11

  修回日期: 2025-06-09

  网络出版日期: 2026-05-22

基金资助

国家社会科学基金一般项目“人智交互情境下用户隐私披露行为生成机理与引导策略研究”(项目编号:24BTQ052)研究成果之一。

A Study on the Influencing Factors and Topological Pathways of Algorithmic Resistance Behavior of Mobile Short-Video Users

  • Mao Taitian ,
  • Ding Tianqu ,
  • Ma Jiawei
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  • 1 School of Public Administration, Xiangtan University, Xiangtan 411105;
    2 School of Business and Management, Jilin University, Changchun 130012
Chen Yifan, Doctoral Candidate; Zhang Zhiqiang, Research Fellow, PhD, Doctoral Supervisor, Corresponding Author, E-mail: zhangzq@clas.ac.cn; Xie Ruixia, Doctoral Candidate; Ding Jingda, Professor, PhD, Doctoral Supervisor.

Received date: 2025-03-11

  Revised date: 2025-06-09

  Online published: 2026-05-22

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on the Generation Mechanism and Guidance Strategies of User Privacy Disclosure Behavior in Human-Intelligence Interaction Contexts” (Grant No. 24BTQ052).

摘要

[目的/意义] 揭示移动短视频用户算法抵抗行为的影响因素及作用路径,剖析认知和情感的动态演化机制,为构建人机协同的算法治理框架提供理论参考与对策建议。[方法/过程] 基于元人种志初步凝练影响因素,采用三角模糊数DEMATEL分析影响因素间的因果关系,而后通过DANP方法量化影响因素权重确定其重要程度,进而利用AISM方法构建对抗层级拓扑网络图,最终结合双系统理论解构算法抵抗行为的作用机制。[结果/结论] 研究识别出信息质量等22个影响因素,并基于双系统理论揭示启发式加工(系统Ⅰ)主导的非理性冲动路径、分析式加工(系统Ⅱ)主导的理性反思路径以及二者的动态博弈机制,阐明移动短视频用户算法抵抗行为中情境、认知与情感的复杂交互关系,最终从监管体系、算法设计和素养教育3个方面提出算法抵抗行为管理策略。

本文引用格式

毛太田 , 丁天曲 , 马家伟 . 移动短视频用户算法抵抗行为影响因素与拓扑路径研究[J]. 图书情报工作, 2026 , 70(11) : 73 -85 . DOI: 10.13266/j.issn.0252-3116.2026.11.006

Abstract

[Purpose/Significance] This study aims to reveal the influencing factors and pathways of algorithmic resistance behavior of mobile short-video users, analyze the dynamic evolution mechanism of cognition and emotion, and provide theoretical basis and countermeasures for the construction of a human-machine collaborative algorithmic governance framework. [Method/Process] The article preliminarily synthesized influencing factors using meta-ethnography and employed the triangular fuzzy number DEMATEL to analyze the causal relationships among these factors. Following this, it utilized the DANP method to quantify the weights of the influencing factors to determine their significance. Subsequently, it employed the AISM method to construct a hierarchical adversarial topological network and ultimately integrated the dual system theory to deconstruct the mechanisms of resistance behavior. [Result/Conclusion] The study identifies 22 influencing factors related to information quality. Based on the dual-system theory, it reveals the impulsive path dominated by System I, the reflective path governed by System II, and the dynamic game mechanism between the two. It elucidates the complex interactions among context, cognition, and emotion in the algorithmic resistance behavior of mobile short-video users. Finally, it proposes management strategies for this behavior from three perspectives: regulatory systems, algorithm design, and literacy cultivation.

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