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Using 4.4 million tweets with GPS location from over 630,000 users in New York City, Sadilek and his team were able to predict when an individual would get sick with the flu and tweet about it up to eight days in advance of their first symptoms. Researchers found they could predict said results with 90 percent accuracy.
Similar to Google's Flu trends, which uses "flu" search trends to pinpoint where and how outbreaks are spreading, Sadilek's system uses an algorithm to differentiate between alternative definitions of the word 'sick.' For example, "My stomach is in revolt. Knew I shouldn't have licked that door knob. Think I'm sick," is different from "I'm so sick of ESPN's constant coverage of Tim Tebow."