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The contribution of the lexical component in hybrid clustering, the case of four decades of “Scientometrics”

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  • Bart Thijs

    (KU Leuven)

  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

Abstract

The introduction of textual analysis and the use of lexical similarities already proved an important asset in science mapping. Earlier research showed the added value of hybrid document networks over link-based ones through the reduction of the extreme sparseness. However, it was only after the application of Natural Language Processing and phrase extraction that networks purely based on lexical similarities could be used as input for topic detection in quantitative science studies. This study investigates the contribution of the lexical component in hybrid cluster on a set of articles published in the journal Scientometrics since its foundation during four decades. Shifting the weight of the lexical components generates changes in the structure of the underlying hybrid network, which can be detected through clustering techniques. We show that these changes are not moving documents randomly, but in fact identify small groups of papers either at the borderline between different topics or combining those. In addition, the analysis substantiates that the lexical component adopts the structure of the network rather than amplifies hidden structures of the link-based network.

Suggested Citation

  • Bart Thijs & Wolfgang Glänzel, 2018. "The contribution of the lexical component in hybrid clustering, the case of four decades of “Scientometrics”," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 21-33, April.
    • Handle: RePEc:spr:scient:v:115:y:2018:i:1:d:10.1007_s11192-018-2659-0
    DOI: 10.1007/s11192-018-2659-0
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    References listed on IDEAS

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    1. Wolfgang Glänzel & Bart Thijs, 2017. "Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1071-1087, May.
    2. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    3. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    4. Frizo Janssens & Wolfgang Glänzel & Bart Moor, 2008. "A hybrid mapping of information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 607-631, June.
    5. Wolfgang Glänzel & Bart Thijs, 2011. "Using ‘core documents’ for the representation of clusters and topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 297-309, July.
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    Cited by:

    1. Matthias Held & Grit Laudel & Jochen Gläser, 2021. "Challenges to the validity of topic reconstruction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4511-4536, May.
    2. Saeed-Ul Hassan & Naif R. Aljohani & Mudassir Shabbir & Umair Ali & Sehrish Iqbal & Raheem Sarwar & Eugenio Martínez-Cámara & Sebastián Ventura & Francisco Herrera, 2020. "Tweet Coupling: a social media methodology for clustering scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 973-991, August.
    3. Bart Thijs, 2020. "Using neural-network based paragraph embeddings for the calculation of within and between document similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 835-849, November.

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