图书情报工作

图书情报工作 >

2020 , Vol. 64 >Issue 20: 117 - 128

DOI: https://doi.org/10.13266/j.issn.0252-3116.2020.20.013

情报研究

用户交互对社会标注行为的差异影响研究——以豆瓣网为例

  • 庄倩 ,
  • 骆慧颖 ,
  • 戴岽丞 ,
  • 刘丽霞 ,
  • 靳雪宁
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  • 1 南京农业大学信息科学技术学院 南京 210095;
    2 北京师范大学系统科学学院 北京 100875
庄倩(ORCID:0000-0002-0984-4723),讲师,博士,E-mail:zhuangqian@njau.edu.cn;骆慧颖(ORCID:0000-0003-4587-9509),本科生;戴岽丞(0000-0003-2923-9640),本科生;刘丽霞(0000-0002-6310-2157),本科生;靳雪宁(0000-0002-1331-7440):本科生。

收稿日期: 2020-04-13

  修回日期: 2020-06-17

  网络出版日期: 2020-10-20

基金资助

本文系南京农业大学中央高校基本科研业务费专项资金"多元化视角的用户标注行为及影响因素研究"（项目编号：KYZ201864）和南京农业大学2018年国家大学生创新训练计划项目"豆瓣用户标注行为差异性研究"（项目编号：20181037077）研究成果之一。

收起

Research on the Influence of User Interaction on the Difference of Social Tagging Behaviors——A Case Study of Douban.com

  • Zhuang Qian ,
  • Luo Huiying ,
  • Dai Dongcheng ,
  • Liu Lixia ,
  • Jin Xuening
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  • 1 College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095;
    2 School of Systems Science, Beijing Normal University, Beijing 100875

Received date: 2020-04-13

  Revised date: 2020-06-17

  Online published: 2020-10-20

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摘要

[目的/意义] 为提高标签质量，优化社会标注系统的信息服务提供依据，从用户在社会标注系统中与其他用户交互的视角，探讨不同交互特征用户的标注行为差异。[方法/过程] 以豆瓣读书作为社会标注系统研究样本，从标签数量、标签结构、标签语义、标注动机和活跃度五个角度研究豆瓣用户标注行为的分布特征；使用用户的关注人数、被关注人数和使用年限表征其在社会标注系统中与其他用户的关联和交互程度，通过差异性分析探讨不同交互特征用户标注行为的差异，并通过多元回归分析研究交互特征对这种差异影响的程度。[结果/结论] 实证研究表明，不同交互特征的用户间的社会标注行为存在显著差异：与其他用户交互比较强的用户标签数据集包含更多的标签，关注其他用户越多的用户和被越多用户关注的用户所使用的标签数量越多；使用豆瓣读书年限越长的用户，其平均标签长度和标签重用率越大，而其与其他用户的关注关系对其标签平均长度和标签重用率的影响不大；用户的特殊语种标签比受用户的使用年限影响很大，但一个用户被多少人关注不会显著的影响其特殊语种标签比；关注其他用户越多的用户在标注系统中越活跃。由此可见，社会标注系统可以采取措施加强系统中用户间的交互，通过用户间的相互作用规范用户的社会标注行为，从而提高标签质量。

关键词： 用户; 交互; 标注行为; 差异性

本文引用格式

庄倩 , 骆慧颖 , 戴岽丞 , 刘丽霞 , 靳雪宁 . 用户交互对社会标注行为的差异影响研究——以豆瓣网为例[J]. 图书情报工作, 2020 , 64(20) : 117 -128 . DOI: 10.13266/j.issn.0252-3116.2020.20.013

Abstract

[Purpose/significance] From the perspective of users interacting with other users in social tagging systems, the differences in tagging behavior of users with different interaction characteristics are explored. The study is helpful to improve the quality of labels, and optimize the quality of information service of the social tagging systems.[Method/process] Using the sample data from Douban Book website, the quantitative indicators were selected to study the distribution characteristics of the users' tagging behaviors from the perspectives of number of tags, tag structure, tag semantics, tagging motivation and user activity. Three indicators including the number of users one follows, the number of one's followers and one's registered age were used to represent a user's degree of association and interaction with other users, then the differences in tagging behavior of users with different interaction characteristics were discussed through difference analysis. Moreover, the influence of the interaction characteristics on these differences were investigated through multivariate regression analysis.[Result/conclusion] The results shows that there exists significant differences in social tagging behaviors among users with different interaction characteristics:users who have stronger interactions with other users have more tags; users who have followed more users and have more followers the greater the number of tags used; the longer the user uses Douban, the greater the average tag length and tag reuse, while the relationship with other users has little effect on the average tag length and tag reuse rate; the user's ratio of tags with special language is also greatly affected by the user's registered age, but the number of user's followers does not significantly affect the user's ratio of tags with special language; the more users who follow other users, the more active they are in the tagging system. It is suggested that the social tagging system can take measures to strengthen the interaction between users, and regulate the users' social tagging behavior through the interaction between users, thereby improve the quality of the social tags in the system.

Key words： user; interaction; tagging behavior; difference

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