A social media platform for infectious disease analytics

Abstract

The effect of seasonal epidemics and potentially pandemics represents a significant issue for public health. In this context, early warnings and real time tracking of the spread of disease is highly desirable. In this paper, we address the problem of detecting disease outbreaks through an automated, scalable Cloud-based system for collecting, tracking and analyzing social media data. Specifically, the focus here is targeted to three prevalent diseases (flu, chickenpox and measles) across three Australian cities using data from the Twitter micro-blogging platform. The epidemics related tweets are extracted using an ensemble learning classifier consisting of a combination of Support Vector Machines, Naïve Bayes and Logistic Regression and comparing the results with the Google Trend data to assess the effectiveness of the overall approach.