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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.13784 |
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| _version_ | 1866914478861320192 |
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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 |