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Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

Author

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  • Wenjie Wei

    (Tongji University
    Tongji University)

  • Hongxu Liu

    (Tongji University)

  • Zhuanlan Sun

    (Nanjing University of Posts and Telecommunications)

Abstract

The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of “emerging topics” share the similar characters with the “cover papers”, this study present a novel approach to translate the “emerging topics” detection to “cover paper” prediction. By using “AdaBoost model” and topic model, we construct a machine learning framework to imitate the top journal (chief) editor’s judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.

Suggested Citation

  • Wenjie Wei & Hongxu Liu & Zhuanlan Sun, 2022. "Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4315-4333, August.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:8:d:10.1007_s11192-022-04462-y
    DOI: 10.1007/s11192-022-04462-y
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    Cited by:

    1. Sun, Zhuanlan, 2024. "Textual features of peer review predict top-cited papers: An interpretable machine learning perspective," Journal of Informetrics, Elsevier, vol. 18(2).

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