Citation accuracy, citation noise, and citation bias: a foundation of citation analysis

Abstract Citation analysis is widely used in research evaluation to assess the impact of scientific papers. These analyses rest on the assumption that citation decisions by authors are accurate, representing the flow of knowledge from cited to citing papers. However, in practice, researchers often cite for reasons that are not related to the fact that there has been (intellectual) input from previous papers. Citations made for rhetorical reasons or without reading the cited work compromise the value of citations as instrument for research evaluation. Past research on threats to the accuracy of citations has mainly focused on citation bias as the primary concern. In this paper, we argue that citation noise—the undesirable variance in citation decisions—represents an equally critical but underexplored challenge in citation analysis. We define and differentiate two types of citation noise: citation level noise and citation pattern noise. Each type of noise is described in terms of how it arises and the specific ways it can undermine the validity of citation-based research assessments. By conceptually differing citation noise from citation accuracy and citation bias, we propose a framework for the foundation of citation analysis. We discuss strategies and interventions to minimize citation noise, aiming to improve the reliability and validity of citation analysis in research evaluation. We recommend that the current professional reform movement in research evaluation such as the Coalition for Advancing Research Assessment (CoARA) pick up these strategies and interventions as an additional building block for careful, responsible use of bibliometric indicators in research evaluation.

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Publication Details

Journal
Scientometrics
Published
2026-10-15
DOI
https://doi.org/10.1007/s11192-026-05841-5
Primary Topic
scientometrics and bibliometrics research
Type
article
Field-Weighted Citation Impact
0.00
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article

Citation accuracy, citation noise, and citation bias: a foundation of citation analysis

Lutz Bornmann, Christian Leibel
Scientometrics
scientometrics and bibliometrics research
article

Citation accuracy, citation noise, and citation bias: a foundation of citation analysis

Lutz Bornmann, Christian Leibel
article en

Abstract

Abstract Citation analysis is widely used in research evaluation to assess the impact of scientific papers. These analyses rest on the assumption that citation decisions by authors are accurate, representing the flow of knowledge from cited to citing papers. However, in practice, researchers often cite for reasons that are not related to the fact that there has been (intellectual) input from previous papers. Citations made for rhetorical reasons or without reading the cited work compromise the value of citations as instrument for research evaluation. Past research on threats to the accuracy of citations has mainly focused on citation bias as the primary concern. In this paper, we argue that citation noise—the undesirable variance in citation decisions—represents an equally critical but underexplored challenge in citation analysis. We define and differentiate two types of citation noise: citation level noise and citation pattern noise. Each type of noise is described in terms of how it arises and the specific ways it can undermine the validity of citation-based research assessments. By conceptually differing citation noise from citation accuracy and citation bias, we propose a framework for the foundation of citation analysis. We discuss strategies and interventions to minimize citation noise, aiming to improve the reliability and validity of citation analysis in research evaluation. We recommend that the current professional reform movement in research evaluation such as the Coalition for Advancing Research Assessment (CoARA) pick up these strategies and interventions as an additional building block for careful, responsible use of bibliometric indicators in research evaluation.

Scientometrics
Max Planck Society (DE), LMU Klinikum (DE), Ludwig-Maximilians-Universität München (DE)
Openalex Percentile: Top 100%
scientometrics and bibliometrics research
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