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Friday 5 July 2013

Everything Gmail Knows About You and Your Friends, Visualized

 for National Journal   

When Google hands over email records to the government, it includes basic envelope information, or metadata, that reveals the names and email addresses of senders and recipients in your account. The feds, in turn, can mine that information for patterns that might be useful in a law enforcement investigation.
What kind of relationships do they typically find? Thanks to the researchers at theMassachusetts Institute of Technology Media Lab, you can now find out. They've developed a tool called Immersion that taps into your Gmail and displays the results as an interactive graphic. (That's mine, above.)
The chart depicts all of your contacts as nodes, and the gray lines between those nodes represent connections between people by email. The larger the circle, the more prominent that person is in your digital life.
A word of warning for the privacy conscious: To use the service, you need to give MIT permission to analyze your email metadata. Once you've done so, it'll take a few minutes to compile everything. When you're done, you're given the option to delete your metadata from MIT's servers.

What you see in my chart are five and a half years' worth of emails. The yellow circles indicate family and close family friends. All of my college friends are in red, and my D.C. friends are in green. Blue nodes denote my colleagues at The Atlantic; pink, my coworkers at National Journal; and gray, people who generally don't share connections with the other major networks in my life.

In all, MIT counted 606 "collaborators" in my inbox, totaling some 83,000 emails. But you can also break down that data by year, month or even the past week. Pretty amazing stuff — and a good reminder not only how much information Google knows about you, but what that information can uncover about other people. If you can learn this much just from looking at one account, imagine what crunching hundreds or thousands of interconnected accounts must be like.
Image courtesy of MIT Media Lab
This article originally published at National Journal here

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