摘要 [目的/意义]在现有社交网络用户自我披露领域的研究中,出现了大量混合性的实验结论。同时,当前学界针对用户披露内容的挖掘尚浅,存在角度单一、划分离散且不全面、社交网络媒介特性缺失等问题。[方法/过程]通过引入自我差异理论,挖掘社交网络用户自我披露的内容特征。以社交网络用户群体为研究对象,通过问卷调查法,基于筛选获得的344份问卷,采用多元逻辑回归等方法进行分析。[结果/结论]隐私、信任、社会资本因素对用户社交网络披露不同自我维度的频率及偏好均存在不同程度的影响。新角度的引入加深了对社交网络用户自我披露的理解,也为后续研究的展开提供新的思路。 [Purpose/significance]Existing research in the field of self-disclosure of social network users has produced a large number of mixed experimental conclusions.Meanwhile,the existing research lacks the mining of the content of user disclosure.In addition,the existing research dimensions are relatively single,discrete and incomplete,and the characteristics of social network media are still lacking.[Method/Process]By introducing the theory of self-difference,the content characteristics of self-disclosure content of social network users are explored.Taking the social network users as the research object,based on the questionnaire survey method and the screening of 344 questionnaires,multiple logistic regression is employed for further analysis.[Result/conclusions]Privacy,trust,and social capital factors all have varying degrees of impacts on the frequency and preferences of users’disclosure of different self-dimensions on social networks.The introduction of new perspectives has deepened the understanding of selfdisclosure of social network users,and also provided new insights for future research.
机构地区 武汉大学信息资源研究中心 南京农业大学信息管理学院
出处 《情报理论与实践》 北大核心 2021年第3期122-130,共9页 Information Studies:Theory & Application
基金 武汉大学“人工智能问题”融通研究专项课题“人工智能应用与个人信息保护研究”的成果之一,项目编号:2020AI021。
关键词 自我差异理论 自我披露 社交网络 隐私计算 自我形象 self-discrepancy theory self-disclosure SNS privacy calculus self-image
