Identification of highly-cited papers using topic-model-based and bibliometric features: The consideration of keyword popularity
Journal
Journal of Informetrics
Journal Volume
14
Journal Issue
1
Date Issued
2020
Author(s)
Hu, Y.-H.
Tai, C.-T.
KANG ERNEST LIU
Cai, C.-F.
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.
Type
journal article