An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains

Seyyed Reza Taher Harikandeh (), Sadegh Aliakbary () and Soroush Taheri ()
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Seyyed Reza Taher Harikandeh: Shahid Beheshti University
Sadegh Aliakbary: Shahid Beheshti University
Soroush Taheri: Shahid Beheshti University

Scientometrics, 2023, vol. 128, issue 3, No 6, 1567-1582

Abstract: Abstract The study of topic evolution aims to analyze the behavior of different research fields by utilizing various features such as the relationships between articles. In recent years, many published papers consider more than one field of study which has led to a significant increase in the number of inter-field and interdisciplinary articles. Therefore, we can analyze the similarity/dissimilarity and convergence/divergence of research fields based on topic analysis of the published papers. Our research intends to create a methodology for studying the evolution of the research fields. In this paper, we propose an embedding approach for modeling each research topics as a multidimensional vector. Using this model, we measure the topic’s distances over the years and investigate how topics evolve over time. The proposed similarity metric showed many advantages over other alternatives (such as Jaccard similarity) and it resulted in better stability and accuracy. As a case study, we applied the proposed method to subsets of computer science for experimental purposes, and the results were quite comprehensible and coherent.

Keywords: Topic evolution; Topic embedding; Scientometrics; Informetrics; Data mining; Similarity metrics (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-023-04642-4

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