Naren Ramakrishnan: How technology can spot a war before it starts.

How Technology Can Spot a War Before It Starts

How Technology Can Spot a War Before It Starts

The citizen’s guide to the future.
March 5 2014 8:47 AM

Symptoms of Violence

How technology can help spot civil unrest and even war before they start.

Mexican president Enrique Pena Nieto in November 2011 in Washington, DC.
Mexican president Enrique Peña Nieto in November 2011 in Washington, DC.

Photo by Mandel Ngan/AFP/Getty Images

This essay is excerpted from The Naked Future, by Patrick Tucker, published by Current.

The date is June 30, 2012. Computer scientist Naren Ramakrishnan is in his Virginia Tech lab watching a map of the Americas on his computer screen. A band of hundreds of red dots hovers over Mexico City; another band is over the Brazil–Paraguay border. The dot cluster is ringed by concentric circles of yellow, green, and blue. It looks almost like a radiant heat map, as though the capital of Mexico and the Brazilian border town of Foz do Iguaçu are on fire, but they aren’t—at least, not yet. These dots represent geotagged tweets containing the terms país,” “trabajador,” “trabaj,” “president,” and “protest.” The controversial Enrique Peña Nieto is about to be officially elected the president of Mexico, and the geotagged tweets represent a march taking form to protest his election.

In 2012 Nieto represented the return to power of the Partido Revolucionario Institucional (PRI). Despite the insurgent‑sounding moniker, the PRI is very much the old‑power party in Mexico, having governed the country for 71 years until 2000. It has long been associated with chronic corruption and even collusion with drug cartels. Nieto, a young, handsome, not conspicuously bright former governor of the state of México, is seen by many as something of a figurehead for a murky, well‑funded machine.


Having met him, I can attest that he can be very charming, smiles easily, and has a firm handshake. As a governor, he is best known for allowing a particularly brutal army assault on protesters in the city of San Salvador Atenco. The June 30 red‑dot cluster over Mexico indicates a lit fuse around the topic of Nieto on Twitter.

At 11:15 p.m. on July 1, as soon as the election is called for the PRI, the student movement group Yo Soy 132 (I Am 132) will spring into action, challenging the results and accusing the PRI of fraud and voter suppression. The next month will be marked by massive protests, marches, clashes with police, and arrests. This is the future that these red dots on Ramakrishnan’s monitor foretell.

The cluster in Brazil relates to a sudden rise in the use of “país” (“country”), “protest,” “empres” (businesspeople), ciudad” (“city”), and “gobiern” (“govern”). In a few days 2,500 people will close the Friendship Bridge connecting the Brazilian city of Foz de Iguaçu to the Paraguayan Ciudad del Este, another episode in the impeachment drama of Paraguayan President Fernando Lugo.

As soon as clusters appear on Ramakrishnan’s computer, the system automatically sends an alert to government analysis with the Intelligence Advanced Research Projects Activity (IARPA), which is funding Ramakrishnan through a program called Open Source Indicators (OSI). The program seeks to use available public data to model potential future events before they happen. Ramakrishnan and his team are one of several candidates competing for IARPA funds for further development. The different teams are evaluated monthly on the basis of what their predictions were, how much lead time the prediction provided, confidence in the prediction, and other factors.

The OSI program is a descendent to the intelligence practice of analyzing “chatter,” a method of surveillance that first emerged during the Cold War. U.S. intelligence agents would listen in on the Soviet military communication network for clues about impending actions or troop movements. Most of this overheard talk was unremarkable, but when the amount of chatter between missile silo personnel and military headquarters increased, this indicated that a big military exercise was about to get underway. This analysis was a purely human endeavor and a fairly straightforward one, with one enemy, one network to watch, and one set of events to watch out for.

In the post‑9/11 world, where—we are told—potential enemies are everywhere and threats are too numerous to mention, the IARPA considers any event related to “population‑level changes in communication, consumption, and movement” worthy of predicting. That could include a commodity‑price explosion, a civil war, a disease outbreak, the election of a fringe candidate to an allied nation’s parliament—anything that could impact U.S. interests, security, or both. The hope is that if such events can be seen advance, their potential effects can be calculated, different responses can be simulated, and decision-makers can then select the best action.

What this means is that amount of potentially useful data has grown to encompass a far greater number of signals. For U.S. intelligence personnel, Facebook, Twitter, and other social networks now serve the role that chatter served during the Cold War. But as Ramakrishnan admits, Facebook probably is not where the next major national security threat is going to pop up. So intelligence actively monitors about 20,000 blogs, RSS feeds, and other sources of information in the same way newsroom reporters constantly watch Associated Press bulletins and listen to police scanners to catch late‑breaking developments.