Flu detector: tracking epidemics on twitter

Authors:
Vasileios Lampos;Tijl De Bie;Nello Cristianini
Affiliations:
Intelligent Systems Laboratory, University of Bristol, UK;Intelligent Systems Laboratory, University of Bristol, UK;Intelligent Systems Laboratory, University of Bristol, UK
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Year:
2010

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Abstract

We present an automated tool with a web interface for tracking the prevalence of Influenza-like Illness (ILI) in several regions of the United Kingdom using the contents of Twitter's microblogging service. Our data is comprised by a daily average of approximately 200,000 geolocated tweets collected by targeting 49 urban centres in the UK for a time period of 40 weeks. Official ILI rates from the Health Protection Agency (HPA) form our ground truth. Bolasso, the bootstrapped version of LASSO, is applied in order to extract a consistent set of features, which are then used for learning a regression model.