Instant prediction of scientific paper cited potential based on semantic and metadata features: Taking artificial intelligence field as an example.

Autor: Zhu H; School of Information Management, Sun Yat-sen University, Guangzhou, China.; Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia., Shuhuai L; School of Information Management, Sun Yat-sen University, Guangzhou, China.; Credit Card Center of China Guangfa Bank Co., Ltd, Guangzhou, China.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Dec 02; Vol. 19 (12), pp. e0312945. Date of Electronic Publication: 2024 Dec 02 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0312945
Abstrakt: With the continuous increase in the number of academic researchers, the volume of scientific papers is also increasing rapidly. The challenge of identifying papers with greater potential academic impact from this large pool has received increasing attention. The citation frequency of a paper is often used as an objective indicator to gauge the academic influence of the paper. The task of citation frequency prediction based on historical citation data in previous studies can achieve high accuracy. However, it can only be executed after the paper has been published for a period. The delay is not conducive to timely discovery of papers with high citation frequency. In this paper, we propose a novel method for predicting cited potential of a paper based on the metadata and semantic information, which can predict the cited potential of academic paper instantly once it has been published. Specifically, the semantic information, such as abstract, semantic span and semantic inflection, is extracted to enhance the ability of the prediction model based on machine learning. To prove the effectiveness and rationality of cited potential prediction model, we conduct two experiments to validate the model and find the most effective combination of input information. The empirical experiments show that the prediction accuracy of our proposed model can reach 88% for the instant prediction of citation.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Zhu, Shuhuai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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