Detecting anomalous citation groups in journal networks

TL;DR

This paper introduces CIDRE, an algorithm to identify anomalous citation groups in journal networks, revealing potential citation cartels that artificially inflate impact factors and impact the academic publishing landscape.

Contribution

The paper presents CIDRE, a novel method for detecting anomalous citation groups, improving early identification of citation cartels in scholarly publishing.

Findings

CIDRE detects over half of the journals suspended for citation anomalies.

It identifies new anomalous groups that inflate impact factors.

The method provides insights into citation practices affecting journal metrics.

Abstract

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals. "Citation cartel" behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm -- named CIDRE -- to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate.