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Sensing the 'Health State' of a community

Sensing the 'Health State' of a community
Sensing the 'Health State' of a community
Mobile phones are a pervasive platform for opportunistic sensing of behaviors and opinions. We show that location and communication sensors can be used to model individual symptoms, longterm health outcomes, and diffusion of opinions in a community. For individuals, phone-based features can be used to predict changes in health, such as common colds, influenza, and stress, and automatically identify symptomatic days. For longer-term health outcomes such as obesity, we find that weight changes of participants are correlated with exposure to peers who gained weight in the same period, which is in direct contrast to currently accepted theories of social contagion. Finally, as a proxy for understanding health education we examine change in political opinions during the 2008 US presidential election campaign. We discover dynamic patterns of homophily and use topic models (Latent Dirchlet Allocation) to understand the link between specific behaviors and changes
in political opinions.
1536-1268
36 - 45
Madan, Anmol
ae1f1aab-e662-44ae-87e3-dde0f7db62fc
Cebrian, Manuel
942ea6dc-57e6-422b-8228-738601c5afe3
Moturu, Sai
2d31c9b3-ace9-40b6-b136-789cacbed451
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Pentland, Alex (Sandy)
086c87f7-504a-4214-94bf-de6ac050a754
Madan, Anmol
ae1f1aab-e662-44ae-87e3-dde0f7db62fc
Cebrian, Manuel
942ea6dc-57e6-422b-8228-738601c5afe3
Moturu, Sai
2d31c9b3-ace9-40b6-b136-789cacbed451
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Pentland, Alex (Sandy)
086c87f7-504a-4214-94bf-de6ac050a754

Madan, Anmol, Cebrian, Manuel, Moturu, Sai, Farrahi, Katayoun and Pentland, Alex (Sandy) (2012) Sensing the 'Health State' of a community. IEEE Pervasive Computing, 11 (4), 36 - 45. (doi:10.1109/MPRV.2011.79).

Record type: Article

Abstract

Mobile phones are a pervasive platform for opportunistic sensing of behaviors and opinions. We show that location and communication sensors can be used to model individual symptoms, longterm health outcomes, and diffusion of opinions in a community. For individuals, phone-based features can be used to predict changes in health, such as common colds, influenza, and stress, and automatically identify symptomatic days. For longer-term health outcomes such as obesity, we find that weight changes of participants are correlated with exposure to peers who gained weight in the same period, which is in direct contrast to currently accepted theories of social contagion. Finally, as a proxy for understanding health education we examine change in political opinions during the 2008 US presidential election campaign. We discover dynamic patterns of homophily and use topic models (Latent Dirchlet Allocation) to understand the link between specific behaviors and changes
in political opinions.

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More information

e-pub ahead of print date: 8 November 2011
Published date: October 2012

Identifiers

Local EPrints ID: 419792
URI: https://eprints.soton.ac.uk/id/eprint/419792
ISSN: 1536-1268
PURE UUID: fffbd1a8-05ab-4fc1-b89d-54b35b6b5fb2

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Date deposited: 20 Apr 2018 16:30
Last modified: 13 Mar 2019 18:37

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Contributors

Author: Anmol Madan
Author: Manuel Cebrian
Author: Sai Moturu
Author: Katayoun Farrahi
Author: Alex (Sandy) Pentland

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