Earlier this year, scenes of unrest in Ukraine spilled out into the world. Protesters demanded the resignation of their president, Viktor Yanukovych, and they relied heavily on social media to coordinate and publicize their efforts. They weren’t alone. Half a world away, frustrated citizens of Venezuela also took to Twitter to decry the state of their economy and champion the ouster of President Nicolas Maduro, Hugo Chavez’s successor.
Though the Euromaidan protests in Ukraine garnered more media attention, the Venezuelan uprising was perhaps more notable, not for the actions the protestors took but for who was watching the action from afar: computer scientists Naran Ramakrishnan and his collaborators. In March,
For years, Ramakrishnan, a professor at Virginia Tech, and his team have been sifting through tweets, blog posts, and news articles about Latin America, keeping a close eye on events in ten countries, including Venezuela. These past couple of months have been no different. But Ramakrishnan and his colleagues haven’t been bent over newspapers or straining their eyes scanning streams of tweets. Rather, they were monitoring the dashboard of EMBERS, their computer program that draws on tweets, news articles, and more to predict the future.
EMBERS was the winner of a competition held by the Intelligence Advanced Research Projects Activity, a part of the Office of the Director of National Intelligence, and it is just one of a handful of systems that attempt to anticipate when and where unrest will emerge by sampling the thousands of petabytes of information that flow daily across the internet.
Though scientists have been analyzing big data sets for years in an attempt to forecast future events, it’s only recently have they marshaled the necessary computational sophistication and horsepower to successfully anticipate outcomes. As more people become connected to the internet through smartphones—and as more of them share their current emotions, intents, and activities on social media—predicting the future will seem less like a pipe dream and more like an inevitability.