Abstract
In scientific papers, citations refer to relevant previous work in order to underline the current line of argumentation, compare to other work and/or avoid repetition in writing. Self-citations, e.g. authors citing own previous work might have the same motivation but have also gained negative attention w.r.t. unjustified improvement of scientific performance indicators. Previous studies on self-citations do not provide a detailed analysis in the domain of computer science. In this work, we analyse the prevalence of self-citations in the DBLP, a digital library for computer science. We find, that approx. 10% of all citations are self-citations, while the rates vary with year after publication and the position of the author in the list as well as with the gender of the lead author. Further, we find that C-ranked venues have the highest incoming self-citation rate, while the outgoing rate is stable across all ranks.
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Acknowledgment
We would like to thank Moritz Grünbauer for his preliminary analysis and help with constructing the queries for the graph database.
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Milz, T., Seifert, C. (2018). Analysing Author Self-citations in Computer Science Publications. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_24
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DOI: https://doi.org/10.1007/978-3-319-99133-7_24
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