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Measuring hotness transfer of individual papers based on citation relationship

Author

Listed:
  • Jianlin Zhou

    (Beijing Normal University)

  • Jinshan Wu

    (Beijing Normal University)

Abstract

It is a common phenomenon for scientists to follow hot topics in research and this phenomenon can generally be quantified by measuring the preference attachment of new papers. A similar phenomenon also exists when a paper chooses its references. However, the abovementioned method does not apply to measure the preference for hot papers. To solve this problem, in this paper, we propose to convert measuring a paper’s preference for hot papers into calculating the hotness obtained from a paper’s references. We propose a PageRank-like algorithm that considers the hotness propagation based on citation relationships between papers to measure the hotness transfer of individual papers. We apply this method to the American Physical Society journals and explore the hotness transfer performance of individual papers in physics. It is found that highly innovative papers, such as Nobel Prize-winning papers in physics, have a weaker hotness transfer degree than papers with the same number of citations. We explore the factors associated with the performance of hotness transfer indicators. We find that the larger the size or citation counts of the field are, the stronger the hotness transfer degree of the field is likely to be. The team size and the number of references can also affect the hotness transfer degree of individual papers. Finally, we find that the hotness transfer scores of papers show an increasing trend over time. Relevant empirical discoveries may be valuable for evaluating paper impact.

Suggested Citation

  • Jianlin Zhou & Jinshan Wu, 2024. "Measuring hotness transfer of individual papers based on citation relationship," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6659-6674, November.
  • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05140-x
    DOI: 10.1007/s11192-024-05140-x
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    References listed on IDEAS

    as
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    7. Li, Menghui & Yang, Liying & Zhang, Huina & Shen, Zhesi & Wu, Chensheng & Wu, Jinshan, 2017. "Do mathematicians, economists and biomedical scientists trace large topics more strongly than physicists?," Journal of Informetrics, Elsevier, vol. 11(2), pages 598-607.
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