Citation impact prediction for scientific papers using stepwise regression analysis

Tian Yu (), Guang Yu (), Peng-Yu Li () and Liang Wang ()
Additional contact information
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

Scientometrics, 2014, vol. 101, issue 2, No 20, 1233-1252

Abstract: 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.

Keywords: Scientific paper; Citation impact prediction; Feature space; Multiple regression (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (55)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-014-1279-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1279-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-014-1279-6

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().