AUGUR: Forecasting the Emergence of New Research Topics

AUGUR: Forecasting the Emergence of New Research Topics” is a paper submitted to the ACM/IEEE Joint Conference on Digital Libraries 2018, presented on June 5 2018, in Fort Worth, TX, USA


Angelo Salatino, Francesco Osborne and Enrico Motta



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. We address this issue by introducing Augur, a novel approach to the early detection of 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 interval and outperformed four alternative approaches in terms of both precision and recall.

Download Gold Standard: link


Explore the line charts:

Figure 3: Performance of the Advanced Clique Percolation Method.
Figure 4: Performance of Fast Greedy algorithm.
Need a larger view? Visit here: link


Paper Download

Download paper (from ORO): link
Download paper (from ACM DL): link



On the 5th of June, I presented this work at the session “Topic Modelling and Detection” using the following presentation:




Other reports

From Web Science and Digital Libraries Research Group:

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