A Bibliometric Horizon Scanning Methodology for Identifying Emerging Topics in the Scientific Literature

TL;DR

This paper introduces a bibliometric horizon scanning methodology combining topic modeling, growth rate analysis, and specialization metrics, supported by interactive visualizations, to identify and analyze emerging science and technology areas from large datasets.

Contribution

It presents a novel integrated approach using LDA, rate kinetics, and location quotient, with interactive tools, to detect and visualize emerging topics in scientific literature.

Findings

Deep learning for machine vision is the fastest growing area.

China has a stronger focus than the U.S. in this area.

Analyzed ~14 million documents over five years.

Abstract

A bibliometric methodology for scanning for emerging science and technology areas is described, where topics in the science, technology and innovation enterprise are discovered using Latent Dirichlet Allocation, their growth rates are modeled using first-order rate kinetics, and research specialization of various entities in these topics is measured using the location quotient. Multiple interactive visualization interfaces that integrate these results together to assist human analysts are developed. This methodology is demonstrated by analyzing the last five years of publications, patents and grants (~ 14 million documents) showing, for example, that deep learning for machine vision is the fastest growing area, and that China has a stronger focus than the U.S. in this area.