Classifying Information from Microblogs during Epidemics

At the outbreak of an epidemic, affected communities want/need to get aware of disease symptoms, preventive measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three communities (i) people who are not affected yet and are looking for prevention-related information (ii) people who are affected and looking for treatment-related information, and (iii) health organizations like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to built an automatic classification approach using low level lexical features which are useful to categorize tweets into different disease-related categories.