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In February, while the world was
watching citizens of the Ukraine topple their government from behind barricades
of flaming tires, computer scientist Naren Ramakrishnan and his research team
were intently watching a similar situation unfold in Venezuela.
The South American nation has been a tinderbox since early February when
Leopoldo Lopez, mayor of Chacao and an opposition leader, tweeted a call for
#LaSalida on Friday, January 31. We will meet this Sunday, his tweet read, for
#TheExit.
The hashtag was a thinly coded call for the ouster of President Nicolas Maduro,
Hugo Chavez’s successor. The protests, which decry high inflation, shortages of
staple goods, and the country’s soaring homicide rate, started in Chacao and
quickly spread to the capital, Caracas. For a while, demonstrations took place
nearly every day. Since the unrest began, at least 32 people have died.
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.
Lopez’s #LaSalida tweet was probably among those which EMBERS analyzed, and the
meaning of its uncoded message was almost certainly clear to the sophisticated
system. But by that point, EMBERS had already suggested to its operators that
Venezuela was ripe for civil unrest. It had also done the same for Brazil many
months earlier, accurately predicting the June 2013 demonstrations against
rising transit fares.
EMBERS is the result of years’ worth of work by Ramakrishnan and his team,
which includes computer scientists, statisticians, political scientists, social
scientists, and an epidemiologist. It is the winning entrant in the Open Source
Initiative at the Intelligence Advanced Research Projects Activity, a part of
the Office of the Director of National Intelligence.
IARPA, according to its website, “invests in high-risk, high-payoff research
programs that have the potential to provide the United States with an
overwhelming intelligence advantage over future adversaries.” The ability to
accurately forecast civil unrest, epidemics, and elections around the world
could do exactly that.
Soon, EMBERS’s capabilities will expand beyond Latin America to the Middle
East. It will draw on some of the same data sources, but also add new feeds.
Its language processing routines will be adapted to new languages, and portions
of its code will be tailored to that region’s cultures. No one is saying
exactly when EMBERS and its offspring will be used to inform decisions by
intelligence agents, but given IARPA’s role in funding it, that seems to be the
plan.
From Delphi to Digital
Predicting the future is a dream as old as antiquity. People have turned to
sources as varied as the oracle at Delphi, the Bible, Nostradamus, and the
Farmer’s Almanac. Most prophecies have been just plain false or, less
damningly, coincidentally correct. But that hasn’t stopped people from trying to
guess what was coming around the bend.
Today, of course, we make forecasts all the time, and there are plenty of times
we get it right. Our lives revolve around weather forecasts, which are
startlingly accurate as many as ten days out. We try to guess how many people
will take the bus during rush hour or how many turkeys will be sold for
Thanksgiving. But when it comes to predicting most collective human actions, we
haven’t been as successful.
At least, we weren’t. Today, a wealth of data is changing the equation. “In the
past, you had traditional media, you had newspapers,” says Dan Braha, a
complexity scientist at the University of Massachusetts, Dartmouth.
“Information was delayed from one area to another. It was very difficult to get
the real-time information about events.”
“For many hundreds of years, the ratio of people who created content to those
who consumed it was very small. Today, it has inverted.”
In the 1990s, the internet began to dismantle some of those barriers, reducing
the time it took for news to travel from, say, Caracas to New York. Rather than
get a subscription to The New York Times, all people had to do was point their
browsers to the right address. As information flowed more freely, the amount
available to any given person increased.
But even then, the web hadn’t yet changed the dynamics of content creation and
consumption. “When the web appeared, it was a total consumption thing,” says
Bernardo Huberman, director of the social computing lab at Hewlett-Packard
Laboratories.
“Then Web 2.0 appeared, which essentially is the introduction of social media.
Namely, people can generate content. Wikipedia is one, Twitter is another,
Facebook, blogging, and so on. There was an explosion of generated content from
the bottom up. For many hundreds of years, the ratio of people who created
content to those who consumed it was very small. Today, it has inverted.”
Computer scientists and statisticians began mining that data for meaningful
relationships. City managers started studying road usage to predict traffic
jams, retailers combed past purchases to entice customers back into the store,
and social media networks scoured profiles to sell more expensive ads. The era
of Big Data was born.
But simply crunching through mountains of data isn’t sufficient. Take Google
Flu Trends, which purports to predict the severity of flu season by monitoring
the number of searches for flu-related terms. Early on, the tool performed
well. But starting in August 2011, the model overestimated the flu’s prevalence
for 100 out of the next 108 weeks, according to an article recently published
in the journal Science.
Mathematical models like Google Flu Trends can help make sense of big data, but
they can also be misleading. For researchers in the era of big data, it’s a
cautionary tale. “The data is there. The question is, what do you do with the
data?” Braha says. “If you use the wrong models, you get the wrong results.” On
top of that, even sophisticated models are limited by our ability to process
natural language using computers. Plus, as time horizons lengthen, accuracy
tends to decline.
That hasn’t slowed things down, though. If anything, the pace has quickened.
Predictive science is fueled by data, and the more that’s available, the more
it has to run with. “The more data you get, the predictive ability of the model
goes up,” Braha says. “The availability of social media and open source data
sets—this is one of the main reasons that enabled people to develop models.”
From Movies to Mass Protests
In 2010, Huberman and his colleague Sitram Asur published a paper about
predicting box office receipts of newly released movies. Plenty of other papers
had been published on the topic, but theirs had a twist—it relied solely on
tweets. It was among the first—if not the first—study that used social media to
predict some event before it happened. Their model proved impressively
prescient, easily besting the previous gold standard. Huberman and Asur had
proved the utility of 140-character sentiments.
A year later, in April, IARPA announced the Open Source Indicators program
(OSI), which would award substantial grants to three research groups to develop
models that ingested publicly available data like tweets, blog posts, and news
articles to anticipate “significant societal events,” such as unrest,
epidemics, and economic instability. OSI isn’t the organization’s only
program—there are dozens—but it is perhaps the most audacious.
On the surface, the goals of the OSI program don’t appear much different from
what practitioners of statistics, economics, and other disciplines have been
doing for decades—that is, building models that use past data to predict some
event. The difference is, OSI wanted researchers to predict an event that
hadn’t happened yet.
Previous “prediction” algorithms had the benefit of hindsight.
Researchers had a better idea which factors precipitated an event, and that
made it easier to tune the algorithms. “It’s always easy to look at things
retrospectively,” says Ramakrishnan, the EMBERS researcher. What makes this new
breed different is that the outcomes and their causes are unknown. The events
haven’t happened yet, and that makes it harder to tweak a model to spit out the
right forecast.
To create EMBERS, dozens of scientists across a handful of disciplines
developed algorithms to scrutinize Twitter’s firehose of information, unravel
various dialects of Spanish, Portuguese, and French, tally reservation
cancellations on OpenTable, and count cars in satellite images of hospital
parking lots. None of the data sets they use are classified, though some of
them cost money to access. The team has spent two years fine tuning the
algorithms and checking their forecasts against reports assembled by a third
party.
By the end of February 2014, EMBERS had archived over 12 terabytes of data, or
about 3 billion messages. It currently processes about 200 to 2,000 messages
per second and adds 15 gigabytes of raw data to the archive every day.
In the past, that would have required some serious hardware to support. But
thanks to cloud computing—where computing resources are dynamically allocated
and distributed across massive server farms—the system requires just 12 virtual
machines, a number that can be easily increased without buying expensive new
servers.
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.
Since it was first booted up in November 2012, EMBERS has raised over 13,000
alerts.
That’s just one stage. EMBERS also scours tweets for three or more of over 800
specific words or phrases that serve as indicators of unrest. “We look at words
and the sentiment with which the word is being used,” says David Mares, a
professor of political science at the University of California, San Diego and a
principal investigator on EMBERS.
A tweet’s sentiment gives important context that can change how it is
interpreted, like the difference between a Venezuelan calling for “The Exit”
and someone expressing frustration over how they can’t find the exit. The
system also uses an algorithm that looks for other meaningful words that might
have been overlooked and adds them to the list. “We’re always picking up new
words,” Mares says.
While this all this is happening, EMBERS is tracking how tweets flow through
the network—how many people are tweeting about protests, who is retweeting
them, and how many people they reach. When certain thresholds are crossed, the
system fires off an alert. The entire system—which monitors far more than just
tweets—generates about 40 alerts per day. Since it was first booted up in
November 2012, EMBERS has raised over 13,000 alerts.
Those warnings appear on the system’s dashboard, a screen in a desktop
application that looks like a mashup of a Twitter feed, Google Maps, a basketball
tournament bracket, and a cardiac monitor. Alerts appear automatically, without
any input from a human. “It sort of gives you a global picture of what’s
happening,” Ramakrishnan says. “You can see the alerts popping up on the
screen. That at least tells you, ‘These are the most major regions that seem to
be cause for concern.’ ”
For now, according to the researchers working on it, EMBERS isn’t involved in
day-to-day intelligence activities. But it seems likely that analysts will be
using it or something like it in the near future. Ramakrishnan says IARPA is
interested in a “tunable system,” one that analysts can tweak to receive more
or fewer alerts. Much of the work done by EMBERS is manual labor for today’s
analysts, he says. “It provides an opportunity for analysts to use this as a
filter to cut across all the chatter.”
EMBERS also makes sense of data that can help predict the outcome of elections
as well as anticipate disease outbreaks. For the latter, EMBERS draws on
standard epidemiological modeling along with a number of unusual data sources,
including restaurant reservations on OpenTable and parking lot vacancies at
hospitals.
By monitoring reservations and cancellations, the system can spot when people
are staying at home rather than eating out, a potential sign of illness. And by
counting cars in satellite images of hospital parking lots, EMBERS knows the
approximate number of visits well before official statistics trickle out.
A Theory of Conflict
EMBERS represents just one way scientists are trying to solve to the problem of
predicting the future. Others are experimenting with different approaches. Take
the work done a team led by Neil Johnson, a physicist at the University of
Miami, for example.
Johnson and his team were also among the three groups chosen to compete in the
OSI program. They sought develop a theory of human conflict and apply it to
various confrontations. Drawing on various data sets, including those on
infant-parent relationships, protestors and their governments, computer
hackers, high-frequency traders, and terrorists, Johnson and his colleagues
distilled a single equation that they say describes how any two-sided
asymmetric conflicts—the sort where one side has more power than another—will
escalate.
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.”
While asymmetry defines most conflicts, it doesn’t define them all. In World
War II, for example, both sides were fairly evenly matched. The same was true
in the Cold War. Adding civilians to the mix also changes the dynamic
substantially, Johnson says. “That’s something we’re actually looking at now.”
Inevitable Questions
We’re still in the early days of predictive science, but already the field is
raising as many questions as it has answered. Could these algorithms further
tip the asymmetry of power toward the already powerful? What if systems like
EMBERS are developed by oppressive regimes? And what are the consequences of
predicting the actions of your own populace?
The answers, of course, depend. Predictive tools can be powerful enablers of
either democracy or oppression. If a democratic country is wielding them, its
government could prevent protests through preemptive policy changes, says
Braha, the complexity scientist. But, he adds, “If a protest is predicted in
Iran or China, they can use it in a negative fashion, definitely. They can
arrest people before it happens.”
It’s also possible that governments could use this data to track their
citizens. EMBERS and other OSI participants are restricted from tracking U.S.
citizens as well as most foreign individuals, says Mares, the political
scientist; the only exception is public foreign personalities, like
politicians. “If a political candidate has a blog and he’s using it during his
campaign, we can certainly track that. But by law we are not permitted to track
individuals,” he says. Still, the technology is there. “We’re not finding
Juanito in La Paz. But what I’m learning is that if we wanted to find Juanito
in La Paz, we could.”
EMBERS and its kind are possible, of course, because of the sheer amount of
personally identifiable information that’s available online. Much of it is
voluntarily posted to Twitter and Facebook, but plenty is unwittingly provided
to marketing companies and advertisers. Many of those data sources are
unregulated, and many are available for the right price.
In theory, anyone with sufficient resources and brainpower could build their
own predictive software similar to EMBERS. “Pick your favorite baddie—to what
degree are they invested in the same kinds of things?” Mares asks rhetorically.
For millennia, predicting the future seemed far fetched. Today, it seems
inevitable. Predictive science is in its infancy, but as we grow more
connected—and more of our worlds become exposed—systems that anticipate our
actions, both individually and in aggregate, will only grow more sophisticated
and more accurate. Mares puts it best: “We’re just scratching the surface
here.”
Read more at
http://www.prophecynewswatch.com/2014/June10/102.html#phH3qvfB2w77jmR6.99
