Realistic Citation Count Prediction Task for Newly Published Papers

Jun Hirako, Ryohei Sasano, Koichi Takeda


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.
Anthology ID:
2023.findings-eacl.84
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1131–1141
Language:
URL:
https://aclanthology.org/2023.findings-eacl.84
DOI:
10.18653/v1/2023.findings-eacl.84
Bibkey:
Cite (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.
Cite (Informal):
Realistic Citation Count Prediction Task for Newly Published Papers (Hirako et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.84.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.84.mp4