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        <title>Realistic Citation Count Prediction Task for Newly Published Papers</title>
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    <abstract>Citation count prediction is the task of predicting the future citation counts of academic papers, which is particularly useful for estimating the future impacts of an ever-growing number of academic papers. Although there have been many studies on citation count prediction, they are not applicable to predicting the citation counts of newly published papers, because they assume the availability of future citation counts for papers that have not had enough time pass since publication. In this paper, we first identify problems in the settings of existing studies and introduce a realistic citation count prediction task that strictly uses information available at the time of a target paper’s publication. For realistic citation count prediction, we then propose two methods to leverage the citation counts of papers shortly after publication. Through experiments using papers collected from arXiv and bioRxiv, we demonstrate that our methods considerably improve the performance of citation count prediction for newly published papers in a realistic setting.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.84</identifier>
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        <url>https://aclanthology.org/2023.findings-eacl.84</url>
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        <date>2023-05</date>
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%0 Conference Proceedings
%T Realistic Citation Count Prediction Task for Newly Published Papers
%A Hirako, Jun
%A Sasano, Ryohei
%A Takeda, Koichi
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F hirako-etal-2023-realistic
%X Citation count prediction is the task of predicting the future citation counts of academic papers, which is particularly useful for estimating the future impacts of an ever-growing number of academic papers. Although there have been many studies on citation count prediction, they are not applicable to predicting the citation counts of newly published papers, because they assume the availability of future citation counts for papers that have not had enough time pass since publication. In this paper, we first identify problems in the settings of existing studies and introduce a realistic citation count prediction task that strictly uses information available at the time of a target paper’s publication. For realistic citation count prediction, we then propose two methods to leverage the citation counts of papers shortly after publication. Through experiments using papers collected from arXiv and bioRxiv, we demonstrate that our methods considerably improve the performance of citation count prediction for newly published papers in a realistic setting.
%R 10.18653/v1/2023.findings-eacl.84
%U https://aclanthology.org/2023.findings-eacl.84
%U https://doi.org/10.18653/v1/2023.findings-eacl.84
%P 1131-1141

Markdown (Informal)

[Realistic Citation Count Prediction Task for Newly Published Papers](https://aclanthology.org/2023.findings-eacl.84) (Hirako et al., Findings 2023)

• Realistic Citation Count Prediction Task for Newly Published Papers (Hirako et al., Findings 2023)

ACL

• Jun Hirako, Ryohei Sasano, and Koichi Takeda. 2023. Realistic Citation Count Prediction Task for Newly Published Papers. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1131–1141, Dubrovnik, Croatia. Association for Computational Linguistics.