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Topic diversity: A discipline scheme‐free diversity measurement for journals

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  • Yi Bu
  • Mengyang Li
  • Weiye Gu
  • Win‐bin Huang

Abstract

Scientometrics has many citation‐based measurements for characterizing diversity, but most of these measurements depend on human‐designed categories and the granularity of discipline classifications sometimes does not allow in‐depth analysis. As such, the current paper proposes a new measurement for quantifying journals' diversity by utilizing the abstracts of scientific publications in journals, namely topic diversity (TD). Specifically, we apply a topic detection method to extract fine‐grained topics, rather than disciplines, in journals and adapt certain diversity indicators to calculate TD. Since TD only needs as inputs abstracts of publications rather than citing relationships between publications, this measurement has the potential to be widely used in scientometrics.

Suggested Citation

  • Yi Bu & Mengyang Li & Weiye Gu & Win‐bin Huang, 2021. "Topic diversity: A discipline scheme‐free diversity measurement for journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 523-539, May.
    • Handle: RePEc:bla:jinfst:v:72:y:2021:i:5:p:523-539
    DOI: 10.1002/asi.24433
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    References listed on IDEAS

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    Cited by:

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    2. Liang Hu & Win-bin Huang & Yi Bu, 2024. "Interdisciplinary research attracts greater attention from policy documents: evidence from COVID-19," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    3. Urdiales, Cristina & Guzmán, Eduardo, 2024. "An automatic and association-based procedure for hierarchical publication subject categorization," Journal of Informetrics, Elsevier, vol. 18(1).
    4. Zhao, Yi & Liu, Lifan & Zhang, Chengzhi, 2022. "Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. Dong, Ke & Wu, Jiang & Wang, Kaili, 2021. "On the inequality of citation counts of all publications of individual authors," Journal of Informetrics, Elsevier, vol. 15(4).

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