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A decade of research in statistics: a topic model approach

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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.

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Notes

  1. In our approach, the relevance \(\rho (p,T)\) of a topic T for a paper p is the log-likelihood returned for each paper by the VEM algorithm implementation as provided in the R topicmodels package (Grün and Hornik 2011).

  2. We note that the total number of papers is lower than the total number of papers in the corpus, because we excluded from the topic analysis those papers with incomplete metadata as well as those containing editorial material but not a proper scientific contribution.

  3. T960 with 124 papers was not considered here because it groups Discussion papers. Similarly T906 with 20 papers was not considered here because it groups all the Erratum papers.

  4. In the subsequent formula and in all the other formulae in the rest of the paper, the \(\log \) symbol refers to the base-10 logarithm.

  5. We recall that topic with an index of h includes h papers each of which has been cited in other papers at least h times.

  6. Corrado Gini’s concentration index; the value 0 indicates equality or uniform distribution, the value 1 indicates maximum concentration.

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Authors and Affiliations

  1. DEMM, Università degli Studi di Milano, Milan, Italy

    Francesca De Battisti & Silvia Salini

  2. DI, Università degli Studi di Milano, Milan, Italy

    Alfio Ferrara

Authors
  1. Francesca De Battisti
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  2. Alfio Ferrara
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  3. Silvia Salini
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Correspondence to Silvia Salini.

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De Battisti, F., Ferrara, A. & Salini, S. A decade of research in statistics: a topic model approach. Scientometrics 103, 413–433 (2015). https://doi.org/10.1007/s11192-015-1554-1

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  • DOI: https://doi.org/10.1007/s11192-015-1554-1

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