Flu detector - Tracking Epidemics on Twitter

V Lampos; Tijl  De Bie; Nello Cristianini

doi:10.1007/978-3-642-15939-8

Flu detector - Tracking Epidemics on Twitter

V Lampos, Tijl De Bie, Nello Cristianini

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

185 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.

Translated title of the contribution	Flu detector - Tracking Epidemics on Twitter
Original language	English
Title of host publication	ECML PKDD 2010
Publisher	Springer
Pages	599 - 602
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-642-15939-8
Publication status	Published - 18 Aug 2010

Bibliographical note

Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III
Conference Organiser: José Luis Balcázar, Francesco Bonchi, Aristides Gionis and Michèle Sebag

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@inproceedings{411a88b61cb34cfabc23c75ccfda8ef8,

title = "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{\textquoteright}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.",

author = "V Lampos and {De Bie}, Tijl and Nello Cristianini",

note = "Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III Conference Organiser: Jos{\'e} Luis Balc{\'a}zar, Francesco Bonchi, Aristides Gionis and Mich{\`e}le Sebag",

year = "2010",

month = aug,

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doi = "10.1007/978-3-642-15939-8",

language = "English",

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AU - Lampos, V

AU - De Bie, Tijl

AU - Cristianini, Nello

N1 - Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III Conference Organiser: José Luis Balcázar, Francesco Bonchi, Aristides Gionis and Michèle Sebag

PY - 2010/8/18

Y1 - 2010/8/18

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-15939-8

DO - 10.1007/978-3-642-15939-8

M3 - Conference Contribution (Conference Proceeding)

SP - 599

EP - 602

BT - ECML PKDD 2010

PB - Springer

ER -

Flu detector - Tracking Epidemics on Twitter

Abstract

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