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

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

Content Provider	ACM Digital Library
Author	Salathe, Marcel Ram, Nilam Tuarob, Suppawong Tucker, Conrad S.
Abstract	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.
Starting Page	1685
Ending Page	1690
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505629
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social media Classification Machine learning
Content Type	Text
Resource Type	Article

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