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A decade of research in statistics: a topic model approach

Author

Listed:
  • Francesca De Battisti

    (Università degli Studi di Milano)

  • Alfio Ferrara

    (Università degli Studi di Milano)

  • Silvia Salini

    (Università degli Studi di Milano)

Abstract

Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution.

Suggested Citation

  • Francesca De Battisti & Alfio Ferrara & Silvia Salini, 2015. "A decade of research in statistics: a topic model approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 413-433, May.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:2:d:10.1007_s11192-015-1554-1
    DOI: 10.1007/s11192-015-1554-1
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    References listed on IDEAS

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    1. Schell, Michael J., 2010. "Identifying Key Statistical Papers From 1985 to 2002 Using Citation Data for Applied Biostatisticians," The American Statistician, American Statistical Association, vol. 64(4), pages 310-317.
    2. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    3. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    4. Thomas Ryan & William Woodall, 2005. "The most-cited statistical papers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(5), pages 461-474.
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    Cited by:

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