Online social net­works such as Face­book, Twitter and Live­Journal are for most of us a tran­quil diver­sion, a way of killing time and keeping in touch by uploading photos, streaming videos and posting pithy updates on our lives.

But for North­eastern assis­tant pro­fessor of com­puter and infor­ma­tion sci­ence Alan Mis­love, they¹re the sub­ject of serious inves­ti­ga­tion into how data on these net­works can be used, and pos­sibly, manipulated.

Mis­love, whose schol­ar­ship focuses on the struc­ture, growth and appli­ca­tions of online social net­works, recently devel­oped an algo­rithm that can pre­dict the per­sonal infor­ma­tion of any given Face­book user in the Rice Uni­ver­sity net­work, including campus res­i­dence, matric­u­la­tion year and aca­d­emic focus.

As a post-​​doctoral researcher at the Max Planck Insti­tute for Soft­ware Sys­tems, in Ger­many, Mis­love coau­thored a paper on his research titled, “You are who you know: Infer­ring user pro­files in online social net­works.” He pre­sented the paper in early Feb­ruary at the Asso­ci­a­tion for Com­puting Machinery’s Inter­na­tional Con­fer­ence on Web Search and Data Mining in New York.

Mislove’s find­ings under­score a fast-​​spreading real­iza­tion about social net­works: hiding, cen­soring or blocking aspects of our per­sonal lives on our favorite sites doesn’t nec­es­sarily mean that we’re any more anony­mous than the user who doc­u­ments his every move through photos and status updates.

By locating only 10 to 20 per­cent of all res­i­dents of a par­tic­ular col­lege res­i­dence hall on Face­book, Mis­love can pick out other users who live in the same building with 95 per­cent accu­racy. By sin­gling out 20 to 30 per­cent of a university’s stu­dents who enroll in a par­tic­ular year, he can pre­dict which other Face­book users arrived on campus at the same time.

Face­book users who belong to the same com­mu­nity tend to be each other’s friends. And because social net­works have high rates of clustering—the chances that two of any given user’s friends know each other is 30 percent—a tiny bit of infor­ma­tion can go a long way to making the notion of net­work pri­vacy a thing of the past.

Pri­vacy is no longer a func­tion of the things you do,” Mis­love explains. “It’s also a func­tion of what your friends and mem­bers of your com­mu­nity do.”

In other words, a Rice Uni­ver­sity stu­dent would not have to include the fact that he lives in “Building A” for Mis­love to make a highly accu­rate guess. New users don’t even have to bother to fill out their own pro­files, Mis­love jokes; with just a jot of data, he’ll do it for them.

Mis­love, who is cur­rently turning his atten­tion to New Orleans’ regional Face­book net­work, says he had no idea four years ago that his research would take this direc­tion. But as the new Web tech­nology sprung into place, he found a niche among a few social net­working experts.

Nobody was really looking into it,” he says. “In the Web 1.0 days, people designed com­puter sys­tems and page rank helped people find pages.” Put another way, users were dis­con­nected from their Internet expe­ri­ence, but online social net­working changed all that.

Social net­working Web sites are bringing users into the system,” he says.

To learn more about Northeastern’s Col­lege of Com­puter and Infor­ma­tion Sci­ence, please visit http://​www​.ccs​.neu​.edu/

To learn more about Northeastern’s Center for Com­plex Net­work Research, please visit http://​www​.barabasilab​.com/