Emerging Scientific Field Detection Using Citation Networks and Topic Models—A Case Study of the Nanocarbon Field

Abstract

In fields with high science linkage, such as the nanocarbon field, trends in academic papers are particularly important for identifying future technological trends. The use of the number of citations allows us to predict the qualitative trends on a paper-by-paper basis. At the same time, it is necessary to be able to comprehensively discuss both qualitative and quantitative aspects in the subject area. This study aimed to detect emerging areas in the nanocarbon field using network models and topic models. It was possible to not only construct a model that exceeded an 86.2% F1 measure but also to focus on an area that could not be detected by the prediction model. This was accomplished by focusing on paper units, such as the research on the chemical synthesis of zigzag single-walled carbon nanotubes. Thus, it is possible to obtain knowledge that contributes to diversified R&D strategies and innovation policies by considering the emergence of new fields from multiple perspectives.