There Is a Fly In My Tweet: Tracking Food-Borne Illness the Crowd-Sourced Way

First time accepted submitter kraken9 writes "New research shows that online chatter can help you avoid food poisoning. Leveraging a statistical language model of Twitter users' online communication, 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, each venue is assigned a health score based on the proportion of customers that fell ill shortly after visiting it. The paper shows that this score correlates with the official inspection data from the Department of Health, and argues that 'nEmesis 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.' Similar techniques have been used before to predict the spread of flu from GPS-tagged social data."