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Portraying the life cycle of ideas in social psychology through functional (textual) data analysis: a toolkit for digital history

Author

Listed:
  • Valentina Rizzoli

    (Sapienza University of Rome)

  • Matilde Trevisani

    (University of Trieste)

  • Arjuna Tuzzi

    (University of Padua)

Abstract

This paper presents a method for the digital history of a discipline (social psychology in this application) through the analysis of scientific publications. The titles of a comprehensive set of papers published in the Journal of Personality and Social Psychology (1965–2021) were collected, yielding a total of 10,222 items. The corpus thus constructed underwent several stages of preprocessing until the final conversion into a terms x time-points matrix, where terms are stemmed words and multi-words. After normalizing frequencies via a chi square-like transformation, clusters of words portraying similar temporal patterns were identified by functional (textual) data analysis and distance-based curve clustering. Among the best candidates in terms of the number of clusters, the solutions with six, nine and thirteen clusters (from lower to higher resolution) have been chosen and the nesting relationship demonstrated. They reveal—at different levels of granularity—increasing, decreasing, and stable keywords trends, highlighting methods, theories, and application domains that have become more popular in recent years, lost popularity, or have remained in common use. Moreover, this method allows to highlight historical issues (such as crises in the discipline or debates over the use of terms). The results highlight the core topics of social psychology in the past and today, underlying the crucial contribution of this method for the digital history of a discipline.

Suggested Citation

  • Valentina Rizzoli & Matilde Trevisani & Arjuna Tuzzi, 2023. "Portraying the life cycle of ideas in social psychology through functional (textual) data analysis: a toolkit for digital history," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5197-5226, September.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:9:d:10.1007_s11192-023-04722-5
    DOI: 10.1007/s11192-023-04722-5
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    References listed on IDEAS

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    1. Genolini, Christophe & Alacoque, Xavier & Sentenac, Mariane & Arnaud, Catherine, 2015. "kml and kml3d: R Packages to Cluster Longitudinal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i04).
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    3. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
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