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作 者:李君君[1,2] 文娟 徐乔丹 叶凤云[3] LI Junjun;WEN Juan;XU Qiaodan;YE Fengyun(School of Management,Hangzhou Dianzi University,Hangzhou 310018,China;Experimental Center of Data Science and Intelligent Decision-Making,Hangzhou Dianzi University,Hangzhou 310018,China;School of Management,Anhui University,Hefei 230601,China)
机构地区:[1]杭州电子科技大学管理学院,浙江杭州310018 [2]杭州电子科技大学数据科学与智能决策实验中心,浙江杭州310018 [3]安徽大学管理学院,安徽合肥230601
出 处:《情报科学》2024年第9期100-111,共12页Information Science
基 金:国家社会科学基金一般项目“矛盾态度视角下社交媒体用户使用行为的演变与机理研究”(22BTQ095)
摘 要:【目的/意义】探究用户社交媒体矛盾态度的形成,深入了解社交媒体用户使用行为的动态变化,挖掘社交媒体用户类别标签和行为特征,成为社交媒体持续运营亟待解决的问题。【方法/过程】构建了社交媒体用户矛盾态度的产生机理和框架模型,通过调查问卷收集586份样本数据,对个体认知、社会环境、积极体验、消极体验、正面情感、负面情感等因素进行聚类分析和矛盾态度程度的计算和判定,将用户按照矛盾态度程度的高低划分成高、较高、中和低4个类别,并比较不同矛盾态度程度用户在各个维度标签以及用户行为表现方面的差异。【结果/结论】通过分析发现矛盾态度的主要影响因素,会产生不同程度的社交媒体矛盾态度和行为表现,且影响程度存在显著差异,为进一步优化社交媒体平台提供理论支持。【创新/局限】将矛盾态度引入到社交媒体用户使用行为的研究中,并通过实证发现不同矛盾态度用户行为的动态变化。但对于动态监控用户行为数据需要进一步深入探索。【Purpose/significance】Exploring the formation of attitudinal ambivalence among users on social media,gaining a deeper understanding of the user dynamic behavior changes in social media,and exploring social media user category tags and behavioral characteristics have become urgent issues for the continuous operation of social media.【Method/process】This research constructs the generation mechanism and framework model of social media users′attitudinal ambivalence,collects 586 sample data through questionnaires,and conducts cluster analysis and calculation and determination of the degree of attitudinal ambivalence on individual cognition,social environment,positive experience,negative experience,positive emotion,negative emotion and other factors.Divide users into four categories based on their level of attitudinal ambivalence:the most high,high,medium,and low,and compare the differences in labels and behavioral performance of users with different levels of attitudinal ambivalence.【Result/conclusion】Through analysis,it was found that the influencing factors of attitudinal ambivalence can lead to varying degrees of attitudinal ambivalence and behavioral manifestations on social media,and there are significant differences in the degree of influence,providing theoretical support for further optimizing social media platforms.【Innovation/limitation】Introducing attitudinal ambivalence into the study of social media user behavior and empirically discover the dynamic changes in user behavior with different attitudinal ambivalence.However,further exploration is needed for dynamic monitoring of user behavior data.
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