ArticleScientometrics2023
An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains.
Article in Scientometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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3 authors.
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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.
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