Machine learning algorithm can identify drunken tweeting

To do that, he and his team collected thousands of geotagged posts tweeted between July 2013 and July 2014 in New York state, and then winnowed them down to tweets containing booze-related keywords (ranging from "beer keg" to "shitfaced"). Each tweet passed through three human "Turkers," who were asked three questions: Q1: Does the tweet make any reference to drinking alcoholic beverages? Q3: if so, is it likely that the tweet was sent at the time and place the tweeter was drinking alcoholic beverages? The success rate--that is, the rate at which the machines' answers matched the Turkers' consensus--ranged from 92 percent for the algorithm answering Q1, to 82 percent for the drunk-spotting algorithm answering Q3.