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A comparison of citation-based clustering and topic modeling for science mapping

Author

Listed:
  • Qianqian Xie

    (Leiden University)

  • Ludo Waltman

    (Leiden University)

Abstract

Understanding the different ways in which different science mapping approaches capture the structure of scientific fields is critical. This paper presents a comparative analysis of two commonly used approaches, topic modeling (TM) and citation-based clustering (CC), to assess their respective strengths, weaknesses, and the characteristics of their results. We compare the two approaches using cluster-to-topic and topic-to-cluster mappings based on science maps of cardiovascular research generated by TM and CC. Our findings reveal that relations between topics and clusters are generally weak, with limited overlap between topics and clusters. Only in a few exceptional cases do more than one-third of the documents in a topic belong to the same cluster, or vice versa. For TM the presence of highly similar topics is a considerable challenge. A strength of TM is its ability to represent societal needs related to cardiovascular disease, potentially offering valuable insights for policymakers. In contrast, CC excels in depicting the intellectual structure of cardiovascular diseases, with a strong capability to reflect scientific micro-communities. This study deepens the understanding of the use of TM and CC for science mapping, providing insights for users on how to apply these approaches based on their needs.

Suggested Citation

  • Qianqian Xie & Ludo Waltman, 2025. "A comparison of citation-based clustering and topic modeling for science mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(5), pages 2497-2522, May.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:5:d:10.1007_s11192-025-05324-z
    DOI: 10.1007/s11192-025-05324-z
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