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.
- binary classification
- highly-cited papers
- keyword popularity
- supervised learning
- topic model
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Data for: Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity
Liu, K. E. (Contributor), Tai, C. (Contributor), Hu, Y. (Contributor) & Cai, C. (Contributor), Mendeley Data, 13 Jan 2020