Detecting impact factor manipulation with data mining techniques

Dong-Hui Yang (), Xin Li, Xiaoxia Sun and Jie Wan
Additional contact information
Dong-Hui Yang: Southeast University
Xin Li: Southeast University
Xiaoxia Sun: Southeast University
Jie Wan: Harbin Institute of Technology

Scientometrics, 2016, vol. 109, issue 3, No 29, 1989-2005

Abstract: Abstract Disingenuously manipulating impact factor is the significant way to harm the fairness of impact factor. That behavior should be banned with effective means. In this paper, data mining techniques are used to solve this problem. Firstly, ten features are collected into feature set for nine normal journals and nine abnormal journals from 2005 to 2014. Then, three types of strong classification methods, k-nearest neighbor, decision tree and support vector machine are adopted to learn the well classification models. Moreover, eight algorithms are run on the data set to find out suitable methods for detecting impact factor manipulation in our experiment. Finally, two excellent algorithms in performance with precisions higher than 85 % are picked out and used to predict new journal samples. According to the results, random forest and one type of support vector machine are relatively more suitable than k-nearest neighbor in this case of detecting abnormal journals. When using those two methods to recognize other 90 journals in the field of nine disciplines from 2007 to 2014, they are verified to be broadly applicable. Unfortunately, four journals are recognized to be manipulated in some years. Therefore, in this paper, two data mining methods are discovered to be intelligent and automatic ways to detect and ban impact factor manipulation for journal managers.

Keywords: Impact factor; Manipulation; Data mining; Classification; Prediction (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-016-2144-6 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:109:y:2016:i:3:d:10.1007_s11192-016-2144-6

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

DOI: 10.1007/s11192-016-2144-6

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 ().