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Citation impact prediction for scientific papers using stepwise regression analysis

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Abstract

Researchers typically pay greater attention to scientific papers published within the last 2 years, and especially papers that may have great citation impact in the future. However, the accuracy of current citation impact prediction methods is still not satisfactory. This paper argues that objective features of scientific papers can make citation impact prediction relatively accurate. The external features of a paper, features of authors, features of the journal of publication, and features of citations are all considered in constructing a paper’s feature space. The stepwise multiple regression analysis is used to select appropriate features from the space and to build a regression model for explaining the relationship between citation impact and the chosen features. The validity of this model is also experimentally verified in the subject area of Information Science & Library Science. The results show that the regression model is effective within this subject.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 70973031).

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Authors and Affiliations

  1. School of Management, Harbin Institute of Technology, Harbin, People’s Republic of China

    Tian Yu, Guang Yu & Liang Wang

  2. School of Education, Harbin Institute of Technology, Harbin, People’s Republic of China

    Peng-Yu Li

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  1. Tian Yu
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  2. Guang Yu
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  3. Peng-Yu Li
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  4. Liang Wang
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Correspondence to Tian Yu.

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Yu, T., Yu, G., Li, PY. et al. Citation impact prediction for scientific papers using stepwise regression analysis. Scientometrics 101, 1233–1252 (2014). https://doi.org/10.1007/s11192-014-1279-6

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