How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level

TL;DR

This study investigates how author affiliation influences citation bias in preprints versus published papers, revealing that citation bias is more pronounced in preprints, which has implications for scientific impact assessment.

Contribution

It provides the first analysis of citation bias in preprints at the institution and country level using Lorenz curves and Gini coefficients.

Findings

Higher Gini coefficients for preprints indicate greater citation bias.

Citation bias is more severe in preprints than in publisher versions.

Affiliation-based citation bias impacts scientific impact metrics.

Abstract

Citing is an important aspect of scientific discourse and important for quantifying the scientific impact quantification of researchers. Previous works observed that citations are made not only based on the pure scholarly contributions but also based on non-scholarly attributes, such as the affiliation or gender of authors. In this way, citation bias is produced. Existing works, however, have not analyzed preprints with respect to citation bias, although they play an increasingly important role in modern scholarly communication. In this paper, we investigate whether preprints are affected by citation bias with respect to the author affiliation. We measure citation bias for bioRxiv preprints and their publisher versions at the institution level and country level, using the Lorenz curve and Gini coefficient. This allows us to mitigate the effects of confounding factors and see whether or not citation biases related to author affiliation have an increased effect on preprint citations. We observe consistent higher Gini coefficients for preprints than those for publisher versions. Thus, we can confirm that citation bias exists and that it is more severe in case of preprints. As preprints are on the rise, affiliation-based citation bias is, thus, an important topic not only for authors (e.g., when deciding what to cite), but also to people and institutions that use citations for scientific impact quantification (e.g., funding agencies deciding about funding based on citation counts).