图书情报知识 ›› 2019, Vol. 0 ›› Issue (4): 82-93.doi: 10.13366/j.dik.2019.04.082

• 知识、学习与管理 • 上一篇    下一篇

中文网络学术社区用户参与行为的实证分析

张敏, 田松瑞, 张可   

  • 出版日期:2019-07-10 发布日期:2019-09-10

Emperical Study of Users’ Participating Behavior in Chinese Online Academic Community

  • Online:2019-07-10 Published:2019-09-10

摘要: [目的/意义]系统分析了小木虫论坛的用户参与行为模式,希望对中文网络学术社区的系统构建和服务改善提供参考,以期推动中文网络学术社区的健康发展。[研究设计/方法]以小木虫论坛为样本对象,将定性内容编码与统计分析相结合,对收集到的5个学科版块(高分子、微米和纳米、仿真模拟、药学、投资理财)的431位用户、513个发帖及38423个回帖进行文本分析。[结论/发现] 不同等级用户具有不同的参与方式,多种类型资源存在于中文网络学术社区用户的交互内容中,支持类情感信息在各学科中都普遍存在,提供个人观点和深入联系的比例较低。[创新/价值]弥补了中文网络学术社区中用户交互内容的文本语义分析不足,系统探究了不同学科用户的参与模式差异、共享资源的需求偏好等问题,充实丰富了网络学术社区用户参与行为的研究视角。

关键词: 网络学术社区, 用户参与行为, 内容分析法, 用户类型, 社会情感

Abstract: [Purpose/Significance]This paper systematically analyzes users’ participating behavior patterns on the Emuch Forum, which intends to provide reference to improve system construction and service, as well as to promote healthy development for Chinese online academic community. [Design/Methodology]Taking the Emuch Forum as an example, this study combines qualitative content coding with statistical analysis, and conducts text analysis for the collected data, which involves five disciplines including Polymer, Micro and Nano, Simulation, Pharmacy, Finance and Investment, and consists of the information of 431 users, 513 posts and 38423 reviews. [Findings/Conclusion]Users at different levels have different participating modes. There are various types of resource in users’ interaction in Chinese online academic communities. Supportive emotional information exists in all disciplines, while personal viewpoints and indepth connections account for a relatively low proportion. [Originality/Value]This paper makes up for the paucity of text semantic analysis on users’  interaction in Chinese online academic community, systematically explores users’ participating modes of different disciplines, users’ demand preference of sharing resources and so on. This paper enriches the research perspectives of users’ participating behaviors in online academic community.

Key words: Online academic community; Users’ participating behavior, Content analysis, User type, Social emotion