Citation impact prediction for scientific papers using stepwise regression analysis

• Published in: Scientometrics Vol. 101; no. 2; pp. 1233 - 1252
• Main Authors: Yu, Tian, Yu, Guang, Li, Peng-Yu, Wang, Liang
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2014; Springer
• Subjects: Bibliometrics. Scientometrics; Bibliometrics. Scientometrics. Evaluation; Citations; Computer Science; Exact sciences and technology; Impact factors; Information and communication sciences; Information science. Documentation; Information Storage and Retrieval; Library and information science. General aspects; Library Science; Predictions; Science; Sciences and techniques of general use; Scientific paper; Citation impact prediction; Feature space; Multiple regression; Correlation analysis; Impact factor; Regression model; Periodical; Scientific research; Regression analysis; Citation
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-014-1279-6

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.