title: TOWARDS EARLY DISCOVERY OF SALIENT HEALTH THREATS: A SOCIAL MEDIA EMOTION CLASSIFICATION TECHNIQUE.
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.
authors.affiliation: Department of Computing and Information Systems, Australia.
authors.forename: Bahadorreza
authors.initials: B
authors.lastname: Ofoghi
authors.forename: Meghan
authors.initials: M
authors.lastname: Mann
authors.forename: Karin
authors.initials: K
authors.lastname: Verspoor
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D004644:Emotions / Q000145:classification*; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology / Q000523:psychology; D006801:Humans; D062486:Public Health Surveillance / Q000379:methods*; D061108:Social Media / Q000706:statistics & numerical data*; D013997:Time Factors; D000069558:Unsupervised Machine Learning / Q000706:statistics & numerical data