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- Title
Citation impact prediction for scientific papers using stepwise regression analysis.
- Authors
Yu, Tian; Yu, Guang; Li, Peng-Yu; Wang, Liang
- 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.
- Subjects
CITATION analysis; SCIENCE publishing; RESEARCH personnel; MULTIPLE regression analysis; IMPACT factor (Citation analysis); INFORMATION science; LIBRARY science
- Publication
Scientometrics, 2014, Vol 101, Issue 2, p1233
- ISSN
0138-9130
- Publication type
Article
- DOI
10.1007/s11192-014-1279-6