  As organizations increasingly rely on professionally oriented networks such
as LinkedIn (the largest such social network) for building business
connections, there is increasing value in having one's profile noticed within
the network. As this value increases, so does the temptation to misuse the
network for unethical purposes. Fake profiles have an adverse effect on the
trustworthiness of the network as a whole, and can represent significant costs
in time and effort in building a connection based on fake information.
Unfortunately, fake profiles are difficult to identify. Approaches have been
proposed for some social networks; however, these generally rely on data that
are not publicly available for LinkedIn profiles. In this research, we identify
the minimal set of profile data necessary for identifying fake profiles in
LinkedIn, and propose an appropriate data mining approach for fake profile
identification. We demonstrate that, even with limited profile data, our
approach can identify fake profiles with 87% accuracy and 94% True Negative
Rate, which is comparable to the results obtained based on larger data sets and
more expansive profile information. Further, when compared to approaches using
similar amounts and types of data, our method provides an improvement of
approximately 14% accuracy.
