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The use of citation context to detect the evolution of research topics: a large-scale analysis

Author

Listed:
  • Chaker Jebari

    (University of Technology and Applied Sciences)

  • Enrique Herrera-Viedma

    (University of Granada)

  • Manuel Jesus Cobo

    (University of Cádiz)

Abstract

With the exponential increase in the number of published papers, discovering how topics evolve becomes increasingly important for anybody involved in research, including researchers, institutes, research funding bodies, and decision-makers. This study proposes a large-scale analysis of the evolution of biomedical and life sciences using the citation contexts of the collected papers, or more precisely their citing sentences. Using 64,350 papers published in PubMed Central between 2008 and 2018, we determined the research trends for ten research topics. Moreover, we studied how these topics evolve across countries and across the most common journals in biomedical and life sciences.

Suggested Citation

  • Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-020-03858-y
    DOI: 10.1007/s11192-020-03858-y
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    References listed on IDEAS

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    Cited by:

    1. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2023. "Context-aware citation recommendation of scientific papers: comparative study, gaps and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4243-4268, August.
    2. Ruhao Zhang & Junpeng Yuan, 2022. "Enhanced author bibliographic coupling analysis using semantic and syntactic citation information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7681-7706, December.
    3. Carmen Díaz-López & Alessandra Bonoli & María Martín-Morales & Montserrat Zamorano, 2021. "Analysis of the Scientific Evolution of the Circular Economy Applied to Construction and Demolition Waste," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    4. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
    5. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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