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Lexical analysis of scientific publications for nano-level scientometrics

Author

Listed:
  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

  • Sarah Heeffer

    (KU Leuven)

  • Bart Thijs

    (KU Leuven)

Abstract

In earlier studies (e.g. Glänzel and Thijs in Scientometrics, 2017) we have used components of text analysis in combination with link-based techniques to cluster documents spaces and to detect emerging research topics on the large scale. Taking up now the objectives of evaluative scientometrics, we attempt to link the textual analysis of small sets of individual scientific papers to evaluative bibliometrics. The objective is, however, quite similar. We focus on the detection of similarities and on monitoring structural changes but this time on the small scale. We proceed from earlier approaches used in quantitative linguistics applied to bibliometrics (Telcs et al. in Math Soc Sci; 10(2):169–178, 1985). In the present pilot study we have selected 18 papers by András Schubert and published in three different periods with 6 papers each: 1983–1985, 1993–1998 and 2010–2013. The objective is twofold: We first try only to detect linguistic regularities in the scientometric text by applying a Waring model to the analysis of Schubert’s vocabulary on the basis of all words and nouns. The second goal refers to the identification of changes in the used vocabulary over a period of three decades. The main findings are discussed along with future research tasks, which arise from these result in the context of the analysis of dynamics and emergence of research topics at the micro and nano level.

Suggested Citation

  • Wolfgang Glänzel & Sarah Heeffer & Bart Thijs, 2017. "Lexical analysis of scientific publications for nano-level scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1897-1906, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2336-8
    DOI: 10.1007/s11192-017-2336-8
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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. Telcs, A. & Glanzel, W. & Schubert, A., 1985. "Characterization and statistical test using truncated expectations for a class of skew distributions," Mathematical Social Sciences, Elsevier, vol. 10(2), pages 169-178, October.
    3. Wolfgang Glänzel & Bart Thijs & Koenraad Debackere, 2014. "The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 939-952, November.
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    Cited by:

    1. Jiaying Liu & Tao Tang & Xiangjie Kong & Amr Tolba & Zafer AL-Makhadmeh & Feng Xia, 2018. "Understanding the advisor–advisee relationship via scholarly data analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 161-180, July.
    2. Valeria Aman, 2018. "A new bibliometric approach to measure knowledge transfer of internationally mobile scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 227-247, October.
    3. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.

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