Citation Farming on ResearchGate: Blatant and Effective

2026-04-15

AI Summary

This study addresses the issue of localized collusive citation manipulation on ResearchGate by proposing a citation-network-based detection method. Analyzing metadata from nearly 3,000 suspicious publications, the authors construct paper- and author-level citation networks and introduce, for the first time, an interpretable structural signal termed “equal-reference groups” to identify highly consistent anomalous citation patterns. The research reveals that such structures are exceedingly rare in legitimate scholarly activity yet prevalent among suspicious papers, with a substantial proportion of citations for certain authors originating from these groups. These findings substantiate both the effectiveness and widespread nature of automated citation-inflation practices.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Crowd Sourcing and Human Computation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

We investigate platform-native citation farming on ResearchGate by analyzing almost 3000 papers uploaded by five suspected boosting-service provider accounts. From the uploaded papers and associated metadata, we construct both paper-level and author-level citation networks. We introduce an interpretable structural signal for coordinated boosting, \emph{equal references groups}: clusters of papers with equal reference lists. We find that many papers from our collection exhibit this motif, that is, they disproportionately cite a small set of authors, consistent with coordinated or automated boosting rather than independent scholarly practice. Finally, we show that for some authors in our dataset a substantial share of their citations can be attributed to these suspicious groups. A different citation network was used to validate the rareness of such motifs in legitimate scientific work.

Problem

• citation farming
• ResearchGate
• coordinated boosting
• citation manipulation
• equal references groups

Innovation

• citation farming
• equal references groups
• coordinated boosting
• citation networks
• ResearchGate