What can social networks on the internet know about persons who are friends of members, but have no user profile of their own? Researchers from the Interdisciplinary Center for Scientific Computing of Heidelberg University studied this question. Their work shows that through network analytical and machine learning tools the relationships between members and the connection patterns to non-members can be evaluated with regards to non-member relationships. Using simple contact data, it is possible, under certain conditions, to correctly predict that two non-members know each other with approx. 40 percent probability.
Until now, studies of this type were restricted to users of social networks, i.e. persons with a posted user profile who agreed to the given privacy terms. “Non-members, however, have no such agreement. We therefore studied their vulnerability to the automatic generation of so-called shadow profiles”, explains Prof. Dr. Katharina Zweig, who until recently worked at the Interdisciplinary Center for Scientific Computing (IWR) of Heidelberg University.
In an online social network, it is possible to infer information about non-members, for instance by using so-called friend-finder applications. When new Facebook members register, they are asked to make available their full list of e-mail contacts, even of those people who are not Facebook members. “This very basic knowledge of who is acquainted with whom in the social network can be tied to information about who users know outside the network. In turn, this association can be used to deduce a substantial portion of relationships between non-members”, explains Ágnes Horvát, who conducts research at the IWR.
Full Text Research Article: “One Plus One Makes Three (for Social Networks)”
PLoS ONE 7(4): e34740. doi:10.1371/journal.pone.0034740