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

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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.

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Fig. 1

Data sourced from Clarivate Analytics Web of Science Core Collection

Fig. 2

Data sourced from Clarivate Analytics Web of Science Core Collection

Fig. 3

Data sourced from Clarivate Analytics Web of Science Core Collection

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Authors and Affiliations

  1. ECOOM, KU Leuven, Louvain, Belgium

    Bart Thijs

  2. ECOOM and Department of MSI, KU Leuven, Louvain, Belgium

    Wolfgang Glänzel

  3. Department of Science Policy and Scientometrics, Library of the Hungarian Academy of Sciences, Budapest, Hungary

    Wolfgang Glänzel

Authors
  1. Bart Thijs
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  2. Wolfgang Glänzel
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Correspondence to Bart Thijs.

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Thijs, B., Glänzel, W. The contribution of the lexical component in hybrid clustering, the case of four decades of “Scientometrics”. Scientometrics 115, 21–33 (2018). https://doi.org/10.1007/s11192-018-2659-0

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  • DOI: https://doi.org/10.1007/s11192-018-2659-0

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