Making sense of patterns in the Twitterverse

If you think keeping up with what’s happening via Twitter, Facebook and other social media is like drinking from a fire hose, multiply that by 7 billion — and you’ll have a sense of what Court Corley wakes up to every morning.

Corley, a data scientist at the Department of Energy’s Pacific Northwest National Laboratory, has created a powerful digital system capable of analyzing billions of tweets and other social media messages in just seconds, in an effort to discover patterns and make sense of all the information. His social media analysis tool, dubbed “SALSA” (SociAL Sensor Analytics), combined with extensive know-how — and a fair degree of chutzpah — allows someone like Corley to try to grasp it all.

“The world is equipped with human sensors — more than 7 billion and counting. It’s by far the most extensive sensor network on the planet. What can we learn by paying attention?” Corley said.

Among the payoffs Corley envisions are emergency responders who receive crucial early information about natural disasters such as tornadoes; a tool that public health advocates can use to better protect people’s health; and information about social unrest that could help nations protect their citizens. But finding those jewels amidst the effluent of digital minutia is a challenge.

“The task we all face is separating out the trivia, the useless information we all are blasted with every day, from the really good stuff that helps us live better lives. There’s a lot of noise, but there’s some very valuable information too.”

The work by Corley and colleagues Chase Dowling, Stuart Rose and Taylor McKenzie was named best paper given at the IEEE conference on Intelligence and Security Informatics in Seattle this week.

Immensely rich data set

One person’s digital trash is another’s digital treasure. For example, people known in social media circles as “Beliebers,” named after entertainer Justin Bieber, covet inconsequential tidbits about Justin Bieber, while “non-Beliebers” send that data straight to the recycle bin.

The amount of data is mind-bending. In social media posted just in the single year ending Aug. 31, 2012, each hour on average witnessed:

• 30 million comments
• 25 million search queries
• 98,000 new tweets
• 3.8 million blog views
• 4.5 million event invites
• 7.1 million photos uploaded
• 5.5 million status updates
• The equivalent of 453 years of video watched

Several firms routinely sift posts on LinkedIn, Facebook, Twitter, YouTube and other social media, then analyze the data to see what’s trending. These efforts usually require a great deal of software and a lot of person-hours devoted specifically to using that application. It’s what Corley terms a manual approach.

Corley is out to change that, by creating a systematic, science-based, and automated approach for understanding patterns around events found in social media.

It’s not so simple as scanning tweets. Indeed, if Corley were to sit down and read each of the more than 20 billion entries in his data set from just a two-year period, it would take him more than 3,500 years if he spent just 5 seconds on each entry. If he hired 1 million helpers, it would take more than a day.

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But it takes less than 10 seconds when he relies on PNNL’s Institutional Computing resource, drawing on a computer cluster with more than 600 nodes named Olympus, which is among the Top 500 fastest supercomputers in the world.

“We are using the institutional computing horsepower of PNNL to analyze one of the richest data sets ever available to researchers,” Corley said.

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