Predicting long-term publication impact through a combination of early citations and journal impact factor
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2021Structured Citation Trend Prediction Using Graph Neural Networkscited by 11
- 2024Is Peer-Reviewing Worth the Effort?cited by 2
- 2022Twin Papers: A Simple Framework of Causal Inference for Citations via Couplingcited by 1
- 2022Quantifying the Online Long-Term Interest in Researchcited by 1
- 2025Comprehensive Manuscript Assessment with Text Summarization Using 69707 articlescited by 0
- 2026The Secretary Problem with a Stochastic Precursorcited by 0
- 2026The Secretary Problem with a Stochastic Precursor21 May 2026
- 2025Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles26 Mar 2025
- 2024Is Peer-Reviewing Worth the Effort?18 Dec 2024
- 2022Quantifying the Online Long-Term Interest in Research13 Sep 2022
- 2022Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling21 Aug 2022
- 2021Structured Citation Trend Prediction Using Graph Neural Networks6 Apr 2021