Lookup NU author(s): Dr Paolo Missier,
Professor Alexander Romanovsky,
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Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twit- ter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic sea- sonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohe- sive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.
Author(s): Missier P, Romanovsky A, Miu T, Pal A, Daniilakis M, Garcia A, Cedrim D, Sousa L
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2nd International Workshop on Mining the Social Web (SoWeMine 2016) - co-located with ICWE 2016
Year of Conference: 2016
Print publication date: 09/06/2016
Online publication date: 05/10/2016
Acceptance date: 08/05/2016
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Series Title: Lecture Notes in Computer Science (LNCS)