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

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

[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。

[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.