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Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria

Author

Listed:
  • Julia Poncela-Casasnovas

    (Northwestern University)

  • Martin Gerlach

    (Northwestern University)

  • Nathan Aguirre

    (Northwestern University)

  • Luís A. N. Amaral

    (Northwestern University
    Northwestern University
    Northwestern University)

Abstract

The analysis of citations to scientific publications has become a tool that is used in the evaluation of a researcher’s work; especially in the face of an ever-increasing production volume1–6. Despite the acknowledged shortcomings of citation analysis and the ongoing debate on the meaning of citations7,8, citations are still primarily viewed as endorsements and as indicators of the influence of the cited reference, regardless of the context of the citation. However, only recently has attention9,10 been given to the connection between contextual information and the success of citing and cited papers, primarily because of the lack of extensive databases that cover both types of metadata. Here we address this issue by studying the usage of citations throughout the full text of 156,558 articles published by the Public Library of Science (PLoS), and by tracing their bibliometric history from among 60 million records obtained from the Web of Science. We find universal patterns of variation in the usage of citations across paper sections11. Notably, we find differences in microlevel citation patterns that were dependent on the ultimate impact of the citing paper itself; publications from high-impact groups tend to cite younger references, as well as more very young and better-cited references. Our study provides a quantitative approach to addressing the long-standing issue that not all citations count the same.

Suggested Citation

  • Julia Poncela-Casasnovas & Martin Gerlach & Nathan Aguirre & Luís A. N. Amaral, 2019. "Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria," Nature Human Behaviour, Nature, vol. 3(6), pages 568-575, June.
    • Handle: RePEc:nat:nathum:v:3:y:2019:i:6:d:10.1038_s41562-019-0585-7
    DOI: 10.1038/s41562-019-0585-7
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    Cited by:

    1. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    2. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.

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