Predicting long-term publication impact through a combination of early citations and journal impact factor

TL;DR

This study demonstrates that combining early citation data with journal impact factors can effectively predict long-term scientific impact, with accuracy improving over time and varying across disciplines.

Contribution

It introduces a predictive model using early citations and impact factor, showing its effectiveness for long-term impact prediction across a large publication dataset.

Findings

Prediction accuracy improves for citation windows over two years

Impact factor's influence diminishes after two years

Prediction accuracy varies across scientific disciplines

Abstract

The ability to predict the long-term impact of a scientific article soon after its publication is of great value towards accurate assessment of research performance. In this work we test the hypothesis that good predictions of long-term citation counts can be obtained through a combination of a publication's early citations and the impact factor of the hosting journal. The test is performed on a corpus of 123,128 WoS publications authored by Italian scientists, using linear regression models. The average accuracy of the prediction is good for citation time windows above two years, decreases for lowly-cited publications, and varies across disciplines. As expected, the role of the impact factor in the combination becomes negligible after only two years from publication.