TOWARDS EARLY DISCOVERY OF SALIENT HEALTH THREATS: A SOCIAL MEDIA EMOTION CLASSIFICATION TECHNIQUE

Bahadorreza Ofoghi¹, Meghan Mann¹, Karin Verspoor^1,2

¹Department of Computing and Information Systems, The University of Melbourne
²Health and Biomedical Informatics Centre, The University of Melbourne

Pacific Symposium on Biocomputing 21:504-515(2016)

© 2016 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. In this paper, we explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early discovery or alarming of such events. We analyse user tweets around recent incidents of Ebola, finding differences in the expression of emotions in tweets posted prior to and after the incidents have emerged. We also analyse differences in the nature of the tweets in the immediately affected area as compared to areas remote to the events. The results of this analysis suggest that emotions in social media microblogging data (from Twitter in particular) may be utilized effectively as a source of evidence for disease outbreak detection and monitoring.