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Investigating the effect of global data on topic detection

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  • Kevin W. Boyack

    (SciTech Strategies, Inc.)

Abstract

A dataset containing 111,616 documents in astronomy and astrophysics (Astro-set) has been created and is being partitioned by several research groups using different algorithms. For this paper, rather than partitioning the dataset directly, we locate the data in a previously created model of the full Scopus database. This allows comparisons between using local and global data for community detection, which is done in an accompanying paper. We can begin to answer the question of the extent to which the rest of a large database (a global solution) affects the partitioning of a smaller journal-based set of documents (a local solution). We find that the Astro-set, while spread across hundreds of partitions in the Scopus map, is concentrated in only a few regions of the map. From this perspective there seems to be some correspondence between local information and the global cluster solution. However, we also show that the within-Astro-set links are only one-third of the total links that are available to these papers in the full Scopus database. The non-Astro-set links are significant in two ways: (1) in areas where the Astro-set papers are concentrated, related papers from non-astronomy journals are included in clusters with the Astro-set papers, and (2) Astro-set papers that have a very low fraction of within-set links tend to end up in clusters that are not astronomy-based. Overall, this work highlights limitations of the use of journal-based document sets to identify the structure of scientific fields.

Suggested Citation

  • Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
  • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2297-y
    DOI: 10.1007/s11192-017-2297-y
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    References listed on IDEAS

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    1. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    2. 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.
    3. 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.
    4. 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.
    5. Nees Jan Eck & Ludo Waltman, 2017. "Citation-based clustering of publications using CitNetExplorer and VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1053-1070, May.
    6. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    7. Boyack, Kevin W. & Klavans, Richard, 2014. "Including cited non-source items in a large-scale map of science: What difference does it make?," Journal of Informetrics, Elsevier, vol. 8(3), pages 569-580.
    8. Kevin W. Boyack & Richard Klavans, 2014. "Creation of a highly detailed, dynamic, global model and map of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 670-685, April.
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    Citations

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    Cited by:

    1. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    2. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    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.
    4. Diana Maynard & Benedetto Lepori & Johann Petrak & Xingyi Song & Philippe Laredo, 2020. "Using ontologies to map between research data and policymakers’ presumptions: the experience of the KNOWMAK project," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1275-1290, November.
    5. 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.
    6. 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.
    7. 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.
    8. Bart Thijs, 0. "Using neural-network based paragraph embeddings for the calculation of within and between document similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 0, pages 1-15.
    9. Sitaram Devarakonda & Dmitriy Korobskiy & Tandy Warnow & George Chacko, 2020. "Viewing computer science through citation analysis: Salton and Bergmark Redux," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 271-287, October.
    10. 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.
    11. Paul Donner, 2021. "Validation of the Astro dataset clustering solutions with external data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1619-1645, February.

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