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Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis

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Abstract

Interdisciplinary interaction and integration have become major features of current science and technology development. Hence, ways to measure the strength of the interdisciplinary interactions between two given disciplines has become a crucial issue. In this study, we propose a novel framework for measuring interdisciplinary interaction that is based on both citation analysis and semantic analysis. Within the framework, direct citations combined with bibliographic coupling reflect citation relationship of interdisciplinary knowledge, while an LDA model combined with a word embedding model are used to explore the integration and diffusion of knowledge via semantic similarity. The strength of the interdisciplinary interactions is then assessed with an entropy weighting method. A case study on the interactions between Information & Library Science and six other disciplines demonstrates the efficacy and reliability of the framework.

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Notes

  1. ITGInsight is a text mining and visualization software for bibliometric data, such as scientific papers, patents, reports and newspapers. Please visit the website for details: http://cn.itginsight.com.

  2. https://gephi.org/.

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Acknowledgements

This work was supported by the National Science Foundation of China [Grant No. 71673086; 71774013]. Our heartfelt appreciation goes to Changtian Wang for his contributions to this paper.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, People’s Republic of China

    Lu Huang, Yijie Cai, Erdong Zhao, Shengting Zhang, Yue Shu & Jiao Fan

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  1. Lu Huang
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  2. Yijie Cai
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  3. Erdong Zhao
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Correspondence to Erdong Zhao.

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Huang, L., Cai, Y., Zhao, E. et al. Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis. Scientometrics 127, 6733–6761 (2022). https://doi.org/10.1007/s11192-022-04401-x

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