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Survival analysis of author keywords: An application to the library and information sciences area

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  • F Peset
  • F Garzón‐Farinós
  • LM González
  • X García‐Massó
  • A Ferrer‐Sapena
  • JL Toca‐Herrera
  • EA Sánchez‐Pérez

Abstract

Our purpose is to adapt a statistical method for the analysis of discrete numerical series to the keywords appearing in scientific articles of a given area. As an example, we apply our methodological approach to the study of the keywords in the Library and Information Sciences (LIS) area. Our objective is to detect the new author keywords that appear in a fixed knowledge area in the period of 1 year in order to quantify the probabilities of survival for 10 years as a function of the impact of the journals where they appeared. Many of the new keywords appearing in the LIS field are ephemeral. Actually, more than half are never used again. In general, the terms most commonly used in the LIS area come from other areas. The average survival time of these keywords is approximately 3 years, being slightly higher in the case of words that were published in journals classified in the second quartile of the area. We believe that measuring the appearance and disappearance of terms will allow understanding some relevant aspects of the evolution of a discipline, providing in this way a new bibliometric approach.

Suggested Citation

  • F Peset & F Garzón‐Farinós & LM González & X García‐Massó & A Ferrer‐Sapena & JL Toca‐Herrera & EA Sánchez‐Pérez, 2020. "Survival analysis of author keywords: An application to the library and information sciences area," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(4), pages 462-473, April.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:4:p:462-473
    DOI: 10.1002/asi.24248
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    Cited by:

    1. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    3. Jinqing Yang & Zhifeng Liu & Xiufeng Cheng & Guanghui Ye, 2024. "Understanding the keyword adoption behavior patterns of researchers from a functional structure perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3359-3384, June.

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