图书情报工作

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2024 , Vol. 68 >Issue 9: 98 - 109

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.09.010

情报研究

重大突发公共卫生事件下社交媒体信息传播的算法抵抗行为研究

  • 刘宇桐 ,
  • 王晰巍 ,
  • 王楠阿雪 ,
  • 乌吉斯古楞
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  • 1 吉林大学商学与管理学院 长春 130022;
    2 吉林大学国家发展与安全研究院 长春 130022
刘宇桐,博士研究生,E-mail:13262796197@163.com;王晰巍,教授,博士生导师;王楠阿雪,博士研究生;乌吉斯古楞,博士研究生。

收稿日期: 2023-07-27

  修回日期: 2023-11-07

  网络出版日期: 2024-05-16

基金资助

本文系国家社会科学基金重大项目“大数据驱动的社交网络舆情主题图谱构建及调控策略研究”(项目编号:18ZDA310)和2023年吉林大学研究生创新研究计划项目“重大突发事件社交媒体信息传播中的算法鸿沟形成和影响研究”(项目编号:2023CX030)研究成果之一。

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A Study of Algorithmic Resistance Behavior in Social Media Information Dissemination under Major Public Health Emergencies

  • Liu Yutong ,
  • Wang Xiwei ,
  • Wang Nanaxue ,
  • WUJI Siguleng
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  • 1 School of Business and Management, Jilin University, Changchun 130022;
    2 National Institute for Development and security, Jilin University, Changchun 130022

Received date: 2023-07-27

  Revised date: 2023-11-07

  Online published: 2024-05-16

Supported by

This work is supported by National Social Science Fund of China project titled“Big Data-Driven Thematic Mapping of Social Network Public Opinion and Research on Moderation”(Grant No.18ZDA310) and by Jilin University Postgraduate Innovative Research Programme Project titled“A Study of Algorithmic Divide Formation and Impact in Social Media Information Dissemination for Major Emergenciesject”(Grant No.2023CX030).

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摘要

[目的/意义] 鉴于社交媒体推荐算法所带来的众多算法负面问题,研究社交媒体用户在重大突发公共卫生事件下的算法抵抗行为,对我国的应急管理和舆情治理具有应用价值。[方法/过程] 基于福格行为模型(FBM 模型)和风险信息寻求与处理模型(RISP 模型)构建重大突发公共卫生事件下社交媒体信息传播中的算法抵抗行为影响机理模型,并利用问卷调查和结构方程方法进行实证检验。[结果/结论] 研究发现,触发维的感知风险变量正向影响负面情感反应;动机维的信息不充分变量正向影响算法抵抗行为。能力维的算法功能感知会通过信息不充分影响算法抵抗行为,算法 FEAT 感知直接影响算法抵抗行为。为重大突发公共卫生事件下社交媒体信息传播的算法抵抗行为研究提供新的理论视角和分析框架,对推动重大突发公共卫生事件的社交媒体算法治理和应急舆情管理提供参考和借鉴。

关键词： 重大突发公共卫生事件; 社交媒体; 信息传播; 算法抵抗行为

本文引用格式

刘宇桐 , 王晰巍 , 王楠阿雪 , 乌吉斯古楞 . 重大突发公共卫生事件下社交媒体信息传播的算法抵抗行为研究[J]. 图书情报工作, 2024 , 68(9) : 98 -109 . DOI: 10.13266/j.issn.0252-3116.2024.09.010

Abstract

[Purpose/Significance] In view of the numerous algorithmic negative problems caused by social media recommendation, it is necessary to study the algorithmic resistance behaviors of social media users under major public health emergencies for the emergency management and public opinion governance in China.[Method/Process] This paper constructed an influence mechanism model of algorithmic resistance behavior in social media information dissemination under major public health emergencies based on the Fogg behavioral model (FBM model) and the risk information seeking and processing model (RISP model), and empirically examined it by questionnaires and structural equation methods.[Result/Conclusion] The perceived risk variable in the trigger dimension positively affects negative emotional responses. The information insufficiency variable in the motivation dimension positively affects algorithmic resistance behavior. Algorithmic function perception in the capability dimension affects algorithmic resistance behavior through information insufficiency, and algorithmic FEAT perception directly affects algorithmic resistance behavior. This paper provides a new theoretical perspective and analytical framework for the study of algorithmic resistance behavior in social media information dissemination under major public health emergencies, and provides references for promoting social media algorithmic governance and emergency public opinion management of major public health emergencies.

Key words： major public health emergencies; social media; information dissemination; algorithmic resistance

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