After EMBERS ingests the raw data, it gleans a variety of metadata, including where a tweet originated and what locations are mentioned, the geographic focus of news articles, the organizations being discussed, and so on. Enriched, the data moves on to the four prediction models.
In the case of predicting civil unrest using Twitter, algorithms look for key words or phrases that suggest a protest is in the works. When EMBERS finds a tweet that contains a key word or phrase—like #LaSalida—it looks for mentions of times or dates. The system then sifts through the geographic metadata to determine where the protest might take place.
Exciting (but scary) stuff.
Love the name, too. EMBERS.
Using their equation, Johnson and his colleagues can predict how a conflict will develop based on the frequency of clashes early on. If confrontations are infrequent at first, any subsequent escalation will be rapid. But when two parties meet each other frequently, the escalation will be more gradual. It’s a pattern that’s shows up throughout their varied data sources, from infants fussing for their mothers’ attention all the way up to the Troubles in Northern Ireland. “The common feature of all these systems we looked at is they’re all, like most systems are, asymmetric,” Johnson says. “One side is trying to pick away at the other.”
They mention that intelligence services aren’t really doing this yet, but I would guess they’re probably quite wrong about that.
I wonder when this will become more mainstream. More of a common marketing tool. Seems obvious to me.
The Beats add during the World Cup, for example. Viral spreading of advertisements seems like a prime use case in the civilian world.
We’ll see more of this for sure.