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Main Authors: Erdogan, Cenk, Daniel, Bennett, Wotka, Benedikt, Sai, Ashish, Iamnitchi, Adriana
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.13784
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author Erdogan, Cenk
Daniel, Bennett
Wotka, Benedikt
Sai, Ashish
Iamnitchi, Adriana
author_facet Erdogan, Cenk
Daniel, Bennett
Wotka, Benedikt
Sai, Ashish
Iamnitchi, Adriana
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2604_13784
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Citation Farming on ResearchGate: Blatant and Effective
Erdogan, Cenk
Daniel, Bennett
Wotka, Benedikt
Sai, Ashish
Iamnitchi, Adriana
Social and Information Networks
G.2; H.3.3; K.4
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.
title Citation Farming on ResearchGate: Blatant and Effective
topic Social and Information Networks
G.2; H.3.3; K.4
url https://arxiv.org/abs/2604.13784