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Citation impact prediction for scientific papers using stepwise regression analysis

Author

Listed:
  • Tian Yu

    (Harbin Institute of Technology)

  • Guang Yu

    (Harbin Institute of Technology)

  • Peng-Yu Li

    (Harbin Institute of Technology)

  • Liang Wang

    (Harbin Institute of Technology)

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.

Suggested Citation

  • Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1279-6
    DOI: 10.1007/s11192-014-1279-6
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    References listed on IDEAS

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