  The serious privacy and security problems related to online social networks
(OSNs) are what fueled two complementary studies as part of this thesis. In the
first study, we developed a general algorithm for the mining of data of
targeted organizations by using Facebook (currently the most popular OSN) and
socialbots. By friending employees in a targeted organization, our active
socialbots were able to find new employees and informal organizational links
that we could not find by crawling with passive socialbots. We evaluated our
method on the Facebook OSN and were able to reconstruct the social networks of
employees in three distinct, actual organizations. Furthermore, in the crawling
process with our active socialbots we discovered up to 13.55% more employees
and 22.27% more informal organizational links in contrast to the crawling
process that was performed by passive socialbots with no company associations
as friends.
  In our second study, we developed a general algorithm for reaching specific
OSN users who declared themselves to be employees of targeted organizations,
using the topologies of organizational social networks and utilizing
socialbots. We evaluated the proposed method on targeted users from three
actual organizations on Facebook, and two actual organizations on the Xing OSN
(another popular OSN platform). Eventually, our socialbots were able to reach
specific users with a success rate of up to 70% on Facebook, and up to 60% on
Xing.
