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Predictable by publication: discovery of early highly cited academic papers based on their own features

Xiaobo Tang (School of Information Management, Wuhan University, Wuhan, China) (Center for Studies of Information System, Wuhan University, Wuhan, China)
Heshen Zhou (School of Information Management, Wuhan University, Wuhan, China)
Shixuan Li (School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China)

Library Hi Tech

ISSN: 0737-8831

Article publication date: 6 February 2023

202

Abstract

Purpose

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

Design/methodology/approach

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Findings

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Originality/value

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

Keywords

Acknowledgements

This study is supported by the Major Projects of National Social Science Foundation of China (Grant Number: 19ZDA349).

Citation

Tang, X., Zhou, H. and Li, S. (2023), "Predictable by publication: discovery of early highly cited academic papers based on their own features", Library Hi Tech, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LHT-06-2022-0305

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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