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

Suppawong Tuarob, Conrad S. Tucker, Marcel Salathe, Nilam Ram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages1685-1690
Number of pages6
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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