Early Detection of Research Trends
Angelo Antonio Salatino

TL;DR
This paper introduces Augur, a novel method for early detection of emerging research topics by analyzing collaboration dynamics between research areas, outperforming existing approaches in identifying new trends before they become prominent.
Contribution
The paper presents Augur, a new approach that detects emerging research topics early by analyzing diachronic relationships and introduces ACPM, a community detection algorithm tailored for this task.
Findings
Augur outperforms four alternative methods in precision and recall.
Emerging topics are preceded by increased collaboration among related research areas.
The approach successfully detects new research trends before they are widely recognized.
Abstract
Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this dissertation, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in…
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Taxonomy
TopicsBig Data and Business Intelligence
