Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity

Ya-Han Hu, Chun-Tien Tai, Kang Ernest Liu and Cheng-Fang Cai

Journal of Informetrics, 2020, vol. 14, issue 1

Abstract: The number of received citations have been used as an indicator of the impact of academic publications. Developing tools to find papers that have the potential to become highly-cited has recently attracted increasing scientific attention. Topics of concern by scholars may change over time in accordance with research trends, resulting in changes in received citations. Author-defined keywords, title and abstract provide valuable information about a research article. This study performs a latent Dirichlet allocation technique to extract topics and keywords from articles; five keyword popularity (KP) features are defined as indicators of emerging trends of articles. Binary classification models are utilized to predict papers that were highly-cited or less highly-cited by a number of supervised learning techniques. We empirically compare KP features of articles with other commonly used journal-related and author-related features proposed in previous studies. The results show that, with KP features, the prediction models are more effective than those with journal and/or author features, especially in the management information system discipline.

Keywords: highly-cited papers; keyword popularity; supervised learning; binary classification; topic model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:14:y:2020:i:1:s1751157719301099

DOI: 10.1016/j.joi.2019.101004

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