As New Scientist explains, Sadilek’s team taught a machine-learning algorithm to sift through 4.4 million tweets (all tagged with GPS data) from more than 630,000 Twitter users in New York City over the course of a month. The algorithm could distinguish between people who were talking about actually being ill and those who used sickness-related words in other contexts—like saying they were “sick” of a certain song, for example. The result: The algorithm could figure out when healthy people would get sick up to eight days ahead of time, with an accuracy rate of 90%.
(via fastcoexist)
Reblogged from Final Boss Form
