| 54345 |
Creator |
166ff8803e7e4bc39bf57257c1241a04 |
| 54345 |
Creator |
c9aa7f2e582d191ed728ad414c5ea711 |
| 54345 |
Creator |
21e3abf33e3daaa89c07ea7d5da24bb0 |
| 54345 |
Creator |
25b3b10b9da03c08922eae28b1249552 |
| 54345 |
Date |
2018-05-23 |
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Is Part Of |
repository |
| 54345 |
Is Part Of |
pf4483357e72e6b1fd586e2e04dd83d71 |
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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. 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 <i>Advanced Clique Percolation Method</i> (ACPM),
a new community detection algorithm developed specifically for supporting this task.
<i>Augur</i> 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. |
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authorList |
authors |
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presentedAt |
ext-305ff8f1a99295f15043825245a7c125 |
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status |
peerReviewed |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/646151 |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/646152 |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/646153 |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/646154 |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/646155 |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/646156 |
| 54345 |
uri |
http://data.open.ac.uk/oro/document/662667 |
| 54345 |
type |
AcademicArticle |
| 54345 |
type |
Article |
| 54345 |
label |
Salatino, Angelo A. ; Osborne, Francesco and Motta, Enrico (2018). AUGUR: Forecasting
the Emergence of New Research Topics. In: JCDL '18: Proceedings of the 18th ACM/IEEE
on Joint Conference on Digital Libraries, ACM, New York, NY, USA pp. 303–312. |
| 54345 |
label |
Salatino, Angelo A. ; Osborne, Francesco and Motta, Enrico (2018). AUGUR: Forecasting
the Emergence of New Research Topics. In: JCDL '18: Proceedings of the 18th ACM/IEEE
on Joint Conference on Digital Libraries, ACM, New York, NY, USA pp. 303–312.
|
| 54345 |
Publisher |
ext-8984db64be0ea0584ffa1935ca7d4159 |
| 54345 |
Title |
AUGUR: Forecasting the Emergence of New Research Topics |
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in dataset |
oro |