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conference paper

Discovering health-related knowledge in social media using ensembles of heterogeneous features

Tuarob, Suppawong
•
Tucker, Conrad
•
Salathé, Marcel  
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2013
Proceedings of the 22nd ACM international conference on Information & Knowledge Management
22nd ACM international conference on Information & Knowledge Management (CIKM 2013)

Social media is emerging as a powerful source of communication, information dissemination and mining. Being colloquial and ubiquitous in nature makes it easier for users to express their opinions and preferences in a seamless, dynamic manner. Epidemic surveillance systems that utilize social media to detect the emergence of diseases have been proposed in the literature. These systems mostly employ traditional document classification techniques that represent a document with a bag of N-grams. However, such techniques are not optimal for social media where sparsity and noise are norms. The authors address the limitations posed by the traditional N-gram based methods and propose to use features that represent different semantic aspects of the data in combination with ensemble machine learning techniques to identify health-related messages in a heterogenous pool of social media data. Furthermore, the results reveal significant improvement in identifying health related social media content which can be critical in the emergence of a novel, unknown disease epidemic

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Type
conference paper
DOI
10.1145/2505515.2505629
Author(s)
Tuarob, Suppawong
•
Tucker, Conrad
•
Salathé, Marcel  
•
Ram, Nilam
Date Issued

2013

Publisher

ACM

Publisher place

New York, NY, USA

Published in
Proceedings of the 22nd ACM international conference on Information & Knowledge Management
ISBN of the book

978-1-4503-2263-8

Start page

1685

End page

1690

Peer reviewed

NON-REVIEWED

Written at

EPFL

EPFL units
UPSALATHE1  
Event nameEvent placeEvent date
22nd ACM international conference on Information & Knowledge Management (CIKM 2013)

San Francisco, CA, USA

October 27 - November 01, 2013

Available on Infoscience
December 10, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/121589
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