Predicting the long-term citation impact of recent publications

TL;DR

This paper introduces a quantile regression model to predict the long-term citation impact of recent publications using impact factor and early citation data, providing insights into factors influencing future citations.

Contribution

It presents a novel application of quantile regression for citation prediction, linking it to extreme value theory to analyze high-impact citations.

Findings

Both impact factor and early citations significantly improve prediction accuracy.

The model effectively predicts the distribution of future citations.

Analytical insights into high quantile behavior of citation distribution.

Abstract

A fundamental problem in citation analysis is the prediction of the long-term citation impact of recent publications. We propose a model to predict a probability distribution for the future number of citations of a publication. Two predictors are used: The impact factor of the journal in which a publication has appeared and the number of citations a publication has received one year after its appearance. The proposed model is based on quantile regression. We employ the model to predict the future number of citations of a large set of publications in the field of physics. Our analysis shows that both predictors (i.e., impact factor and early citations) contribute to the accurate prediction of long-term citation impact. We also analytically study the behavior of the quantile regression coefficients for high quantiles of the distribution of citations. This is done by linking the quantile regression approach to a quantile estimation technique from extreme value theory. Our work provides insight into the influence of the impact factor and early citations on the long-term citation impact of a publication, and it takes a step toward a methodology that can be used to assess research institutions based on their most recently published work.