Citation Farming on ResearchGate: Blatant and Effective

TL;DR

This study uncovers coordinated citation boosting on ResearchGate by analyzing nearly 3000 papers, revealing patterns of suspicious citation groups and their impact on author metrics.

Contribution

It introduces an interpretable structural signal for detecting citation boosting and demonstrates its effectiveness in identifying suspicious citation groups.

Findings

Many papers exhibit equal references groups indicating boosting.

A significant share of some authors' citations originate from suspicious groups.

Legitimate scientific work rarely shows the detected citation motifs.

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, 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.