Social Relationship Tracking: You Can Run, But You Can’t Hide
Machine learning team demonstrates how to infer data about non-members of a social network
by +Sophie Wrobel, +EuroTech; Germany

Are you concerned about what the social networks that you are not a member of know about you? Maybe you should be. If Facebook gets their algorithms right, they could make a surprisingly good guess about whether two non-Facebook users are friends with each other. Should you be concerned?

According to a team of researchers from the University of Heidelberg, social networks can predict information about the relationship between a member and a non-members of a given social network platform with 85% accuracy, and information about the relationship between two non-members of the same network with 40% accuracy. Their prediction model works by testing various parameterized neural networks across a set of realistic Facebook training data in order to answer the question of whether a particular non-user is likely to be recruited by a particular user.

But perhaps more important than their methodology are the repercussions of their results. Even when commercial applications of their research are still way off, this will likely turn into another case of balancing out the marketer’s dream with the individual user’s nightmare: how much inferred data should be allowed in targeting advertisements? What non-personal hints can and should be allowed to be used in calculating marketing target probabilities?

How anonymous can an incognito user truly be in light of such elaborate algorithms?

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