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

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

Published in:
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 1685-1690
Presented at:
22nd ACM international conference on Information & Knowledge Management (CIKM 2013), San Francisco, CA, USA, October 27 - November 01, 2013
New York, NY, USA, ACM

 Record created 2015-12-10, last modified 2018-09-13

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