THEORETICAL STUDY

Identifying the Discipline Structure with Topic Coupling Analysis of Journal Article

  • Xu Yuanjie ,
  • Li Jianping ,
  • Wu Dengsheng
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  • 1 Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190;
    2 School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100190;
    3 School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    4 College of Management, Shenzhen University, Shenzhen 518060

Received date: 2023-10-11

  Revised date: 2024-01-16

  Online published: 2024-11-09

Supported by

This work is supported by the National Natural Science Foundation of China project titled “Data-driven Technology Management and Decision-making” (Grant No. 72022021).

Abstract

[Purpose/Significance] Academic journals are widely used as a processing unit for identifying discipline structure. By identifying discipline subfields through journal clustering, it is possible to effectively reveal the intrinsic structural features of a discipline. From journal contents on the semantic correlation of articles, it proposes a new method of constructing correlation between journals, called journal article topic coupling, and a discipline structure identification model. [Method/Process] It employed the LDA topic model to identify the topic and its topic distribution. Based on this, it proposed a threshold-based method to construct journal article topic distribution, and adopted the t-SNE algorithm to obtain low-dimensional embedding of the high-dimensional distribution to address the difficulty of clustering journal by high-dimensional journal topic distribution. [Result/Conclusion] It conducts empirical analysis on 68,078 articles in 107 economics and management journals included in CSSCI from 2018 to 2022, and describes the model’s parameter selection and setting methods. The results demonstrate that the new method effectively utilizes article topic correlation on the semantic dimension for journal clustering and discipline subfields identification, divides domestic economic and management disciplines into 16 subfields, including 9 in economics, 5 in management, and 2 in the cross-field, along with an interpretation of the research scope of each subfield.

Cite this article

Xu Yuanjie , Li Jianping , Wu Dengsheng . Identifying the Discipline Structure with Topic Coupling Analysis of Journal Article[J]. Library and Information Service, 2024 , 68(21) : 3 -15 . DOI: 10.13266/j.issn.0252-3116.2024.21.001

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