  Social media has been considered as a data source for tracking disease.
However, most analyses are based on models that prioritize strong correlation
with population-level disease rates over determining whether or not specific
individual users are actually sick. Taking a different approach, we develop a
novel system for social-media based disease detection at the individual level
using a sample of professionally diagnosed individuals. Specifically, we
develop a system for making an accurate influenza diagnosis based on an
individual's publicly available Twitter data. We find that about half (17/35 =
48.57%) of the users in our sample that were sick explicitly discuss their
disease on Twitter. By developing a meta classifier that combines text
analysis, anomaly detection, and social network analysis, we are able to
diagnose an individual with greater than 99% accuracy even if she does not
discuss her health.
