A deep learning-based method for predicting the emerging degree of research topics using emerging index

Zhenyu Yang (), Wenyu Zhang (), Zhimin Wang () and Xiaoling Huang ()
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Zhenyu Yang: Zhejiang University of Finance and Economics
Wenyu Zhang: Zhejiang University of Finance and Economics
Zhimin Wang: Zhejiang University of Finance and Economics
Xiaoling Huang: Zhejiang University of Finance and Economics

Scientometrics, 2024, vol. 129, issue 7, No 14, 4042 pages

Abstract: Abstract With the exponential growth of the volume of scientific literature, it is particularly important to grasp the research frontier. Predicting emerging research topics will help research institutions and scholars promptly discover promising research topics. However, previous studies mainly focused on identifying and detecting emerging research topics and lacked a method to efficiently represent and predict the emerging degree of research topics. Therefore, this study proposes a novel deep learning-based method to predict the emerging degree of research topics. First, a new indicator, the emerging index, is proposed based on the emerging attributes such as novelty, growth, and impact to quantitatively measure the emerging degree of research topics. Second, new features reflecting the emerging attributes of the research topics are extracted by constructing heterogeneous networks of bibliographic entities in the research domain. Finally, a deep learning-based time series model was employed to predict the future emerging index based on these new features. Data from the neoplasms and metabolism research domains in the PubMed Central database were used to validate the proposed method. The experimental results showed that the emerging index proposed effectively measures the emerging degree of the research topics. Furthermore, the deep learning-based model demonstrates superior performance to other models in predicting the emerging index, as evidenced by both error-based and rank-based metrics.

Keywords: Emerging topics prediction; Heterogeneous networks; Deep learning; Emerging index (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-024-05068-2

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