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Early Detection of Research Trends

Salatino, Angelo (2019). Early Detection of Research Trends. PhD thesis. The Open University.

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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 the pace of collaboration between relevant research areas, which can be seen as the 'ancestors' of the new topic. Based on this understanding, we developed Augur, a novel approach to effectively detect the emergence of new research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 timeframe and outperformed four alternative approaches in terms of both precision and recall.

Item Type: Thesis (PhD)
Copyright Holders: 2018 The Author
Keywords: research management
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Item ID: 67224
Depositing User: Angelo Salatino
Date Deposited: 19 Nov 2019 11:49
Last Modified: 13 Dec 2019 12:23
URI: http://oro.open.ac.uk/id/eprint/67224
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