Detecting Structural Changes In The Nanocarbon Domain Based On The Time Distribution Of Text Information Of Academic Papers

In recent years, there has been an increasing need for the early detection of emerging research fronts. Research in this field usually employs citation networks, but this methodology does not address the citation lag problem. Text information is required to solve the time gap in citation networks because text information is available immediately when papers are published. However, text information has an inherent domain dependency problem. To address this, we introduce the "Dynamic Topic Model" (DENT). In a DTM, text information is represented in an abstract "topic" form and text information is captured as an increase or decrease in topics. We apply a DTM to the nanocarbon domain, which has experienced significant structural changes. We note that the choice of a suitable number of topics for the DTM requires further research. In this paper, we show that the proposed methodology, text information analysis with a DTM, can detect emerging research fronts earlier than the citation network technique.