Classification method for detecting coercive self-citation in journals

Journal self-citations strongly affect journal evaluation indicators (such as impact factors) at the meso- and micro-levels, and therefore they are often increased artificially to inflate the evaluation indicators in journal evaluation systems. This coercive self-citation is a form of scientific misconduct that severely undermines the objective authenticity of these indicators. In this study, we developed the feature space for describing journal citation behavior and conducted feature selection by combining GA-Wrapper with RelifF. We also constructed a journal classification model using the logistic regression method to identify normal and abnormal journals. We evaluated the performance of the classification model using journals in three subject areas (BIOLOGY, MATHEMATICS and CHEMISTRY, APPLIED) during 2002–2011 as the test samples and good results were achieved in our experiments. Thus, we developed an effective method for the accurate identification of coercive self-citations.