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Identifying interdisciplinary topics and their evolution based on BERTopic

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Abstract

Interdisciplinary topic reflects the knowledge exchange and integration between different disciplines. Analyzing its evolutionary path is beneficial for interdisciplinary research in identifying potential cooperative research direction and promoting the cross-integration of different disciplines. However, current studies on the evolution of interdisciplinary topics mainly focus on identifying interdisciplinary topics at the macro level. More analysis of the evolution process of interdisciplinary topics at the micro level is still needed. This paper proposes a framework for interdisciplinary topic identification and evolutionary analysis based on BERTopic to bridge the gap. The framework consists of four steps: (1) Extract the topics from the dataset using the BERTopic model. (2) Filter out the invalid global topics and stage topics based on lexical distribution and further filter out the invalid stage topics based on topic correlation. (3) Identify interdisciplinary topics based on disciplinary diversity and disciplinary cohesion. (4) Analyze the interdisciplinary topic evolution by inspecting the intensity and content in the evolution, and visualize the evolution using Sankey diagrams. Finally, We conduct an empirical study on a dataset collected from the Web of Science (WoS) in Library & Information Science (LIS) to evaluate the validity of the framework. From the dataset, we have identified two distinct types of interdisciplinary topics in LIS. Our findings suggest that the growth points of LIS mainly exist in the interdisciplinary research topics. Additionally, our analysis reveals that more and more interdisciplinary knowledge needs to be integrated to solve more complex problems. Mature interdisciplinary topics mainly formed from the internal core knowledge in LIS stimulated by external disciplinary knowledge, while promising interdisciplinary topics are still at the stage of internalizing and absorbing the knowledge of other disciplines. The dataset, the code for implementing the algorithms, and the complete experiment results will be released on GitHub at: https://github.com/haihua0913/IITE-BERT.

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Acknowledgements

The paper is a substantially extended version of the article “Interdisciplinary Topics Extraction and Evolution Analysis” presented in the 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents 2022 (EEKE 2022) at JCDL 2022. The authors are grateful to all the anonymous reviewers for their precious comments and suggestions.

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Authors and Affiliations

  1. School of Information Management, Central China Normal University, Wuhan, 430079, China

    Zhongyi Wang & Jing Chen

  2. Department of Information Science, University of North Texas, Denton, TX, 76203, USA

    Jiangping Chen & Haihua Chen

Authors
  1. Zhongyi Wang
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  2. Jing Chen
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  3. Jiangping Chen
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  4. Haihua Chen
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhongyi Wang, Jing Chen, Jiangping Chen, and Haihua Chen. The first draft of the manuscript was written by Zhongyi Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Haihua Chen.

Additional information

This study is supported by National Social Science Foundation of China (Grant Number 22BTQ102).

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Wang, Z., Chen, J., Chen, J. et al. Identifying interdisciplinary topics and their evolution based on BERTopic. Scientometrics (2023). https://doi.org/10.1007/s11192-023-04776-5

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