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Detecting latent referential articles based on their vitality performance in the latest 2 years

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  • Mingyang Wang

    (Northeast Forestry University)

  • Shi Li

    (Northeast Forestry University)

  • Guangsheng Chen

    (Northeast Forestry University)

Abstract

In this paper, we propose a methodology to detect latent referential articles through a universal, citation-based investigation. We discuss articles’ dynamic vitality performance, concealed in their citation distributions, in order to understand the mechanisms that govern which articles are likely to be referenced in the future. Articles have diverse vitality performances expressed in the amount of citations obtained in different time periods. Through an examination of the correlation between articles’ future citation count and their past citations, we establish the optimal time period during which it is best to forecast articles’ future referential possibilities. The results show that the latest 2 years is the optimal time period. In other words, the correlation between the articles’ future citation count and their past citation count reaches a maximum value in the most recent 2-year period. The articles with a higher vitality performance in the most recent 2 years have a higher ratio of being cited as references in the future. These results help, not only, in understanding mechanisms of generating references, but also provide an additional indicator for decision makers to evaluate the academic performance of individuals according to their citation performance in the latest 2 years.

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  • Mingyang Wang & Shi Li & Guangsheng Chen, 2017. "Detecting latent referential articles based on their vitality performance in the latest 2 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1557-1571, September.
    • Handle: RePEc:spr:scient:v:112:y:2017:i:3:d:10.1007_s11192-017-2433-8
    DOI: 10.1007/s11192-017-2433-8
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    2. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Tianyu Zhang, 2019. "Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
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    4. Binglu Wang & Yi Bu & Yang Xu, 2018. "A quantitative exploration on reasons for citing articles from the perspective of cited authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 675-687, August.

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