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

arXiv: https://arxiv.org/abs/1909.08907 · HTML: https://mlsanity.com/paper/1909.08907/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Structured Citation Trend Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2104.02562/) (2021)
- [Is Peer-Reviewing Worth the Effort?](https://mlsanity.com/paper/2412.14351/) (2024)
- [Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling](https://mlsanity.com/paper/2208.09862/) (2022)
- [Quantifying the Online Long-Term Interest in Research](https://mlsanity.com/paper/2209.06212/) (2022)
- [Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles](https://mlsanity.com/paper/2503.20835/) (2025)
- [The Secretary Problem with a Stochastic Precursor](https://mlsanity.com/paper/2605.22653/) (2026)

## Abstract (arXiv)

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.