Flu Detector - Tracking Epidemics on Twitter

Lampos Vasileios, Bie Tijl De, Nello Cristianini

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

154 Citations (Scopus)

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. Visit Flu Detector's website
Translated title of the contributionFlu Detector - Tracking Epidemics on Twitter
Original languageEnglish
Title of host publicationEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
PublisherSpringer
Publication statusPublished - 2010

Bibliographical note

Other page information: 599-602
Conference Proceedings/Title of Journal: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
Other identifier: 2001214

Fingerprint Dive into the research topics of 'Flu Detector - Tracking Epidemics on Twitter'. Together they form a unique fingerprint.

Cite this