利用多种模型和早期引文预测各研究领域学术论文的引文影响力

IF 3.5 3区 管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Scientometrics Pub Date : 2024-06-25 DOI:10.1007/s11192-024-05086-0
Fang Zhang, Shengli Wu
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引用次数: 0

摘要

随着科学文献数量的迅速增长,准确衡量和预测学术论文的引文影响力变得日益重要。在这方面,引用次数是一个被广泛采用的指标。虽然许多研究人员都探索过预测论文引用次数的技术,但一个普遍的制约因素是在数据集中的所有论文中使用单一模型。这种通用方法适用于小型同质数据集,但对于横跨不同研究领域的大型异质数据集而言,其效果却大打折扣,从而削弱了这些方法的实用性。在本研究中,我们提出了一种开创性的方法,该方法部署了针对不同研究领域的多种模型,并整合了早期引文数据。我们的方法包括基于实例的学习技术,将论文归类到不同的研究领域,以及根据每个领域内论文的早期引用次数训练出的不同预测模型。我们使用来自 DBLP 和 arXiv 的两个广泛数据集对我们的方法进行了评估。我们的实验结果证实,所提出的分类方法在将论文分类到研究领域方面既精确又高效。此外,所提出的预测方法利用了多个特定领域模型和早期引文,在大多数情况下都超越了四种最先进的基线方法,大大提高了对不同学术论文集进行引文影响预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Predicting citation impact of academic papers across research areas using multiple models and early citations

As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers’ citation counts, a prevalent constraint lies in the utilization of a singular model across all papers within a dataset. This universal approach, suitable for small, homogeneous collections, proves less effective for large, heterogeneous collections spanning various research domains, thereby curtailing the practical utility of these methodologies. In this study, we propose a pioneering methodology that deploys multiple models tailored to distinct research domains and integrates early citation data. Our approach encompasses instance-based learning techniques to categorize papers into different research domains and distinct prediction models trained on early citation counts for papers within each domain. We assessed our methodology using two extensive datasets sourced from DBLP and arXiv. Our experimental findings affirm that the proposed classification methodology is both precise and efficient in classifying papers into research domains. Furthermore, the proposed prediction methodology, harnessing multiple domain-specific models and early citations, surpasses four state-of-the-art baseline methods in most instances, substantially enhancing the accuracy of citation impact predictions for diverse collections of academic papers.

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来源期刊
Scientometrics
Scientometrics 管理科学-计算机:跨学科应用
CiteScore
7.20
自引率
17.90%
发文量
351
审稿时长
1.5 months
期刊介绍: Scientometrics aims at publishing original studies, short communications, preliminary reports, review papers, letters to the editor and book reviews on scientometrics. The topics covered are results of research concerned with the quantitative features and characteristics of science. Emphasis is placed on investigations in which the development and mechanism of science are studied by means of (statistical) mathematical methods. The Journal also provides the reader with important up-to-date information about international meetings and events in scientometrics and related fields. Appropriate bibliographic compilations are published as a separate section. Due to its fully interdisciplinary character, Scientometrics is indispensable to research workers and research administrators throughout the world. It provides valuable assistance to librarians and documentalists in central scientific agencies, ministries, research institutes and laboratories. Scientometrics includes the Journal of Research Communication Studies. Consequently its aims and scope cover that of the latter, namely, to bring the results of research investigations together in one place, in such a form that they will be of use not only to the investigators themselves but also to the entrepreneurs and research workers who form the object of these studies.
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