nemesis: Which restaurants should you avoid today

Computational approaches to health monitoring and epi-demiology continue to evolve rapidly. We present an end-to-end system, nEmesis, that automatically identi-fies restaurants posing public health risks. Leveraging a language model of Twitter users ’ online communica-tion, nEmesis finds individuals who are likely suffering from a foodborne illness. People’s visits to restaurants are modeled by matching GPS data embedded in the messages with restaurant addresses. As a result, we can assign each venue a “health score ” based on the pro-portion of customers that fell ill shortly after visiting it. Statistical analysis reveals that our inferred health score correlates (r = 0.30) with the official inspection data from the Department of Health and Mental Hygiene (DOHMH). We investigate the joint associations of mul-tiple factors mined from online data with the DOHMH violation scores and find that over 23 % of variance can be explained by our factors. We demonstrate that read-ily accessible online data can be used to detect cases of foodborne illness in a timely manner. This approach offers an inexpensive way to enhance current methods to monitor food safety (e.g., adaptive inspections) and identify potentially problematic venues in near-real time