The explosive use of social media sites presents a unique opportunity for developing alternative methods for understanding the health of the public. The near ubiquity of smartphones has further increased the volume and resolution of data that is shared through these sites. The emerging field of digital epidemiology has focused on methods to analyze and use this “digital exhaust” to augment traditional epidemiologic methods. When applied to the task of disease detection they often detect outbreaks 1-2 weeks earlier than their traditional counterpart. Many of these approaches successfully employ data mining techniques to detect symptoms associated with influenza-like illness. Others can identify the appearance of novel symptom patterns, allowing the ability to detect the emergence of a new illness in a population. However, behaviors that lead to increased risk for disease have not yet received this treatment.
Objective
Create an analysis pipeline that can detect the behavioral determinants of disease in the population using social media data.