重大公共卫生事件下的社交媒体用户算法抵抗行为过程研究被引量：1

Research on the Algorithmic Resistance Behavior Processes of Social Media Users under Major Public Health Events

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摘要 [目的/意义]社交媒体已经成为重大公共卫生事件信息传播的重要途径,社交媒体推荐算法对重大公共卫生事件信息传播产生重要影响。社交媒体用户通过算法抵抗行为参与社交媒体算法规则,主动调节重大公共卫生事件的信息获取。算法抵抗行为是重大公共卫生事件社交媒体信息传播中用户的重要信息行为。[方法/过程]利用扎根研究方法对25位受访者的访谈资料进行分析,划分重大公共卫生事件下的社交媒体用户算法抵抗行为过程阶段,识别不同过程阶段的影响因素,总结算法抵抗行为过程理论模型。[结果/结论]重大公共卫生事件下的社交媒体用户算法抵抗行为过程包括情景需求诱发阶段、推荐评价与体验阶段、抵抗评估与决策阶段。算法抵抗行为受到抵抗策略评估、用户素养、情感体验、推荐评价要素的直接影响以及情境压力和用户需求的间接影响。研究结果可以深化重大公共卫生事件下的社交媒体用户信息行为理论研究,为重大公共卫生事件的应急管理和舆情治理实践提供参考。 [Purpose/significance]Social media has become an important way to disseminate information about major public health events,and social media recommendation algorithms have an important impact on the dissemination of information about major public health events.Social media users participate in social media algorithmic rules through algorithmic resistance behaviors to actively regulate access to information about major public health events.Algorithm resistance behavior is important information behavior of users in social media information dissemination of major public health events.[Method/process]This paper analyzes the interview data of 25 interviewees using the grounded research method,delineates the process stages of social media users’algorithmic resistance behaviors under major public health events,identifies the influencing factors of different process stages,and summarizes the theoretical model of the process of algorithmic resistance behaviors.[Result/conclusion]The process of algorithmic resistance behavior of social media users under major public health events includes the stage of situational demand triggering,the stage of recommendation evaluation and experience,and the stage of resistance assessment and decision-making.Algorithmic resistance behavior is directly influenced by the elements of resistance strategy evaluation,user literacy,emotional experience,recommendation evaluation,and indirectly influenced by situational pressure and user needs.This paper helps to deepen the theoretical study of social media users’information behavior under major public health events,and provides a reference for emergency management and public opinion governance practices of major public health events.

作者刘宇桐王晰巍邱程程乌吉斯古楞 Liu Yutong;Wang Liwei;Qiu Chengcheng;Wuji Siguleng(School of Business and Management,Jilin University,Jilin Changchun 130022;Research Centre for Big Data Management,Jilin University,Jilin Changchun 130022;Research Centre for Cyberspace Governance,National Institute of Development and Security Studies,Jilin University,Jilin Changchun 130022)

机构地区吉林大学商学与管理学院吉林大学大数据管理研究中心吉林大学国家发展与安全研究院网络空间治理研究中心吉林大学国家发展与安全研究院

出处《情报理论与实践》 CSSCI 北大核心 2024年第10期128-139,共12页 Information Studies:Theory & Application

基金吉林省自然科学基金面上项目“重大突发事件下智能推荐算法对网络舆情演化影响及风险预警研究”(项目编号:20240101372JC) 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)的成果之一。

关键词重大公共卫生事件算法抵抗行为社交媒体算法素养扎根理论 major emergencies algorithmic resistance behavior social media algorithmic literacy grounded theory

分类号 R-05 [医药卫生] G252 [文化科学—图书馆学]

作者简介刘宇桐,博士生。研究方向:突发事件与信息行为;王晰巍,教授,博士生导师。研究方向:信息行为与舆情;邱程程,博士生。研究方向:群体极化;乌吉斯古楞,博士生。研究方向:突发事件与信息行为。