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.
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Keywords
- Ground Truth
- Candidate Feature
- Health Protection Agency
- Perform Feature Selection
- Really Simple Syndication
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Lampos, V., De Bie, T., Cristianini, N. (2010). Flu Detector - Tracking Epidemics on Twitter. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15939-8_42
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DOI: https://doi.org/10.1007/978-3-642-15939-8_42
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