Citation Biases: Detecting Communities from Patterns of Temporal Variation in Journal Citation Networks

Abstract

Recent studies confirm that several journals exchange intentionally biased citations to inflate their Journal Impact Factors (JIF) mutually. It includes excessive self-citations, stacking, cartels, cabals, and rings. Microscopically, the key entities are authors, editors, and publishers. Identifying coordinated citation manipulation is complex because multiple dynamics are involved. Also, such behavior varies largely across disciplines. Hence, there is still a lack of automated algorithms to detect them readily. Nevertheless, the real problem arises when authentic journals with identical citation patterns (natural biases) are associated similar to abnormal patterns. Thus, our prime objective in this paper is to understand all reasons behind naturally occurring citation biases. This paper proposes a novel generalized methodology to detect such journals with irregular JIF inflations using community-based analysis. First, we model large-scale time-series citation data of 1606 journals in a network structure. Next, we detect communities from the resultant temporal network using a multi-layered modularity maximization algorithm. Broadly, we obtain four communities—Self-Citation (SC), Pairwise Mutual-Citation (MCP), Group Mutual-Citation (MCG), and Uni-directed Citation (UC). Macroscopically, we define the underlying community dynamics using network parameters. The promiscuity of the SC class is the highest, 0.90, and cohesion strength of MCG class is highest at 0.71. Microscopically, we present a case-by-case analysis from real-world data. An abrupt change in a donor's publication rate and sudden inflation in the recipient journal's JIF is a characteristic feature. Other features leading to natural biases include narrow domain specialization, publisher's impact, citations from newly published or review journals, overlapping author sets, and author self-citations. Consequently, future studies must carefully consider all these factors before modeling any citation anomaly detection algorithm.