摘要
[目的/意义]学术社交网络已经成为科研工作者们维护人际关系、进行科研合作和学术交流的重要途径,开展用户行为聚类研究对学术社交网络平台准确识别用户组成成分、理解用户行为、提升服务效率具有重要意义。[方法/过程]文章以ResearchGate(RG)为研究对象,在构建平台用户行为的描述模型基础上,利用K-means算法对学术社交网络的用户利用行为进行聚类分析,并立足于学科差异视角探索不同学科用户分布与行为特征。[结果/结论]研究表明RG用户可被划分为10类,不同用户群体在平台功能的利用方面存在较明显的行为偏好差异。同时,学术社交网络的用户利用行为存在学科差异,自然科学类用户类型分布较为均匀,较少呈现极端偏向某一用户群体的情况,利用行为也更加积极;而人文社科类用户主要由潜水用户组成,表现相对沉默。
[Purpose/significance]Academic social networking sites have become an important way for researchers to maintain interpersonal relationships and conduct scientific research cooperation and academic exchanges.Carrying out user behavior clustering research is significant for academic social network platforms to accurately identify user components,understand user behavior and improve service efficiency.[Method/process]Taking ResearchGate(RG)as the research object,this paper used k-means algo-rithm to cluster and analyze the behavior of users on academic social networking sites based on the construction of a descriptive mod-el of user behavior.Besides,this study explored the distribution and the behavioral characteristics of users from the perspective of disciplinary differences.[Result/conclusion]The results show that RG users can be divided into 10 categories,with different user groups having different preferences in the use of platform’s functions;meanwhile,there exists disciplinary differences in the usage behavior of users of academic social networking sites.To be specific,natural sciences’users are more evenly distributed,less ex-tremely biased towards one user's group and more active in their usage behavior,while humanities and social sciences’users are mainly composed of lurkers who are more reticent.
出处
《情报理论与实践》
CSSCI
北大核心
2022年第6期138-146,共9页
Information Studies:Theory & Application
基金
国家自然科学基金青年项目“群体差异视角下学术社交网络用户交互与合作机制研究”的成果之一,项目编号:71904148。
