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

Source: [A Bibliometric Horizon Scanning Methodology for Identifying Emerging Topics in the Scientific Literature](<https://www.sota2.com/research/paper/68fa6bdd1e209131cea30b33>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:51.976Z

Authors: Artjay Javier, Beth Masimore, John Chase, F.G. Serpa, John T. Rigsby, Avory Bryant, Jeffrey Solka, Ryan J. Zelnio

Published: 2022-02-27T23:29:31.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2202.13480>)

[PDF](<https://arxiv.org/pdf/2202.13480.pdf>)