On the identification and analysis of citation pattern irregularities among journals

Recent studies report that few journals are adopting unethical citation practices to inflate Impact Factor (IF) artificially. Clarivate Analytics has started to blacklist such journals since 2006. As reported in the literature, evaluation of journals individually, to detect anomalies from vast and dynamically changing citation network is not efficient. The primary purpose of this work is to define a diverse feature set that can identify such cases of extreme outliers and reason them. The sample size is narrowed down using an unsupervised clustering algorithm in the absence of a labeled training dataset. Next, time-series IF data is analyzed to detect point outliers. Furthermore, microscopic features are identified to reason them. Results reflected from the F-value after ANOVA analysis reveals that geometrical patterns (self-loop, pairwise and group mutual-citation) among journals, an abrupt increase in the paper count of donor and corresponding IF inflation of recipient are some of the essential features. Microscopic features include social factor (calculation of revised IF after removing directed self or mutual-citation), impact of the field of study, impact of publication house and author factor that includes author self and mutual-citation. The significance of this work is to ensure that the quality of a journal is withheld without compromising research integrity by controlling or auditing individual features periodically.

Automated summary

Recent studies have shown that some journals are adopting unethical citation practices to inflate Impact Factor artificially. This research aims to identify extreme cases by defining a diverse feature set, narrowing sample size using clustering algorithm, and analyzing time-series data to detect outliers.