Flu Detector - Tracking Epidemics on Twitter
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.
Cite
Text
Lampos et al. "Flu Detector - Tracking Epidemics on Twitter." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_42Markdown
[Lampos et al. "Flu Detector - Tracking Epidemics on Twitter." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/lampos2010ecmlpkdd-flu/) doi:10.1007/978-3-642-15939-8_42BibTeX
@inproceedings{lampos2010ecmlpkdd-flu,
title = {{Flu Detector - Tracking Epidemics on Twitter}},
author = {Lampos, Vasileios and De Bie, Tijl and Cristianini, Nello},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {599-602},
doi = {10.1007/978-3-642-15939-8_42},
url = {https://mlanthology.org/ecmlpkdd/2010/lampos2010ecmlpkdd-flu/}
}