  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 detecting 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.
