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Challenges to the validity of topic reconstruction

Author

Listed:
  • Matthias Held

    (TU Berlin)

  • Grit Laudel

    (TU Berlin)

  • Jochen Gläser

    (TU Berlin)

Abstract

In this paper we utilize an opportunity to construct ground truths for topics in the field of atomic, molecular and optical physics. Our research questions in this paper focus on (i) how to construct a ground truth for topics and (ii) the suitability of common algorithms applied to bibliometric networks to reconstruct these topics. We use the ground truths to test two data models (direct citation and bibliographic coupling) with two algorithms (the Leiden algorithm and the Infomap algorithm). Our results are discomforting: none of the four combinations leads to a consistent reconstruction of the ground truths. No combination of data model and algorithm simultaneously reconstructs all micro-level topics at any resolution level. Meso-level topics are not reconstructed at all. This suggests (a) that we are currently unable to predict which combination of data model, algorithm and parameter setting will adequately reconstruct which (types of) topics, and (b) that a combination of several data models, algorithms and parameter settings appears to be necessary to reconstruct all or most topics in a set of papers.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03920-3
    DOI: 10.1007/s11192-021-03920-3
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    References listed on IDEAS

    as
    1. Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
    2. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    3. Rob Koopman & Shenghui Wang, 2017. "Mutual information based labelling and comparing clusters," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1157-1167, May.
    4. Klavans, Richard & Boyack, Kevin W., 2017. "Research portfolio analysis and topic prominence," Journal of Informetrics, Elsevier, vol. 11(4), pages 1158-1174.
    5. 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.
    6. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    7. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    8. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    9. 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.
    10. Richard Klavans & Kevin W. Boyack, 2011. "Using global mapping to create more accurate document‐level maps of research fields," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 1-18, January.
    11. Henry Small, 2006. "Tracking and predicting growth areas in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 595-610, September.
    12. Richard Klavans & Kevin W. Boyack, 2011. "Using global mapping to create more accurate document-level maps of research fields," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 1-18, January.
    13. Michael T. SCHAUB & Jean-Charles DELVENNE, 2017. "The many facets of community detection in complex networks," LIDAM Reprints CORE 2890, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Naoki Shibata & Yuya Kajikawa & Yoshiyuki Takeda & Katsumori Matsushima, 2009. "Comparative study on methods of detecting research fronts using different types of citation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 571-580, March.
    15. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    16. 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.
    17. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    18. Michel Zitt & Suzy Ramanana-Rahary & Elise Bassecoulard, 2005. "Relativity of citation performance and excellence measures: From cross-field to cross-scale effects of field-normalisation," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 373-401, April.
    19. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    20. 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.
    21. A. V. Chumachenko & B. G. Kreminskyi & Iu. L. Mosenkis & A. I. Yakimenko, 2020. "Dynamics of topic formation and quantitative analysis of hot trends in physical science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 739-753, October.
    22. Haunschild, Robin & Schier, Hermann & Marx, Werner & Bornmann, Lutz, 2018. "Algorithmically generated subject categories based on citation relations: An empirical micro study using papers on overall water splitting," Journal of Informetrics, Elsevier, vol. 12(2), pages 436-447.
    23. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
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