On the Ground Validation of Online Diagnosis with Twitter and Medical Records
This addresses the challenge of validating online health monitoring for individuals, though it is incremental as it builds on existing social media tracking methods.
The paper tackled the problem of individual-level disease detection from social media by developing a system that diagnoses influenza using Twitter data, achieving over 99% accuracy even when users do not explicitly discuss their health.
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.