  Mature social networking services are one of the greatest assets of today's
organizations. This valuable asset, however, can also be a threat to an
organization's confidentiality. Members of social networking websites expose
not only their personal information, but also details about the organizations
for which they work. In this paper we analyze several commercial organizations
by mining data which their employees have exposed on Facebook, LinkedIn, and
other publicly available sources. Using a web crawler designed for this
purpose, we extract a network of informal social relationships among employees
of a given target organization. Our results, obtained using centrality analysis
and Machine Learning techniques applied to the structure of the informal
relationships network, show that it is possible to identify leadership roles
within the organization solely by this means. It is also possible to gain
valuable non-trivial insights on an organization's structure by clustering its
social network and gathering publicly available information on the employees
within each cluster. Organizations wanting to conceal their internal structure,
identity of leaders, location and specialization of branches offices, etc.,
must enforce strict policies to control the use of social media by their
employees.
