摘要
[目的/意义]探索虚拟学术社区用户交互行为的特征,为社区知识服务的建设和平台管理提出参考。[方法/过程]在会话分析理论的基础上,结合运用内容分析、LDA主题模型和社会网络分析方法对社区的信息交互类型、交互主题特征以及用户交互网络的拓扑结构特征进行分析,从会话交互内容和会话交互关系两个维度揭示虚拟学术社区的用户交互特征。[结果/结论]虚拟学术社区用户交互内容需求与供给存在不均衡现象,用户交互主题结构具有分散性;事实信息、意见和建议是虚拟学术社区中主要的交互信息类型;虚拟学术社区用户交互网络.具有小世界效应的特征,但网络结构整体较为分散,并且缺乏高影响力的用户;提出包括保证有效的信息供给、创新社区学术知识服务和改善社区交互功能设计等促进虚拟学术社区用户交互的策略。
[Purpose/significance]This study aims to explore the characteristics of community user interaction,and propose useful references for community knowledge service construction and platform management.[Method/process]Based on the theory of conversation analysis,using content analysis,LDA theme model and social network analysis methods,this paper analyzed the information interaction type,interaction theme and topology structure of in-formation interaction network,and revealed the user interaction characteristics of virtual academic community from two dimensions of conversation interactive content and conversation interaction relationship.[Result/conclusion] The demand and supply of user interaction content in virtual academic community is unbalanced,and the topic struc-ture of user interaction is decentralized;factual information,opinions and suggestions are the main types of interac-tion information in virtual academic community;the user interaction network in virtual academic community has the characteristics of small world effect,but the network structure is relatively decentralized,and lacks high influential users:this paper proposes strategies including ensuring effective information supply.innovation of community aca-demic knowledge service and improvement of community interaction function design to promote virtual academic com-munity user interaction.
作者
卢恒
张向先
张莉曼
陶兴
Lu Heng;ZhangXiangxian;Zhang Liman;Tao Xing(School of Management,Jilin University,Changchun 130022)
出处
《图书情报工作》
CSSCI
北大核心
2020年第13期80-89,共10页
Library and Information Service
基金
国家社会科学基金项目“大数据驱动下学术新媒体知识聚合及创新服务研究”(项目编号
18BT0085)研究成果之一。
关键词
虚拟学术社区
用户交互行为
会话分析
内容分析
社会网络分析
virtual academic community
user interaction behavior
conversation analysis
content analysis
social network analysis