An automatic recognition method of journal impact factor manipulation

Journal impact factor (IF) manipulation has unhealthy effects on the academic community and is attracting more attention from scholars. In this paper, an intelligent method is proposed to identify manipulative self-citation behaviour in journals using pattern recognition. Data on IFs, age distributions of total citations, and numbers of self-citations were collected for 18 journals from 1998 to 2007 in Journal Citation Reports (JCR); these journals include known manipulated journals. The feature variables of the citation distribution functions of the known manipulated journals were extracted using the k-nearest neighbour classifier, and a feature attribute space was established for pattern recognition. The MATLAB software was used to process, train, and test the data and to develop a suitable matrix model which can provide an original model for identifying other manipulated journals. To verify the validity and reliability of this method, the authors randomly collected citation distribution data from several journals in JCR, analysed the results of the verification, and proved the effectiveness of pattern recognition in this context.