  Online Social Networks (OSN) are increasingly being used as platform for an
effective communication, to engage with other users, and to create a social
worth via number of likes, followers and shares. Such metrics and crowd-sourced
ratings give the OSN user a sense of social reputation which she tries to
maintain and boost to be more influential. Users artificially bolster their
social reputation via black-market web services. In this work, we identify
users which manipulate their projected follower count using an unsupervised
local neighborhood detection method. We identify a neighborhood of the user
based on a robust set of features which reflect user similarity in terms of the
expected follower count. We show that follower count estimation using our
method has 84.2% accuracy with a low error rate. In addition, we estimate the
follower count of the user under suspicion by finding its neighborhood drawn
from a large random sample of Twitter. We show that our method is highly
tolerant to synthetic manipulation of followers. Using the deviation of
predicted follower count from the displayed count, we are also able to detect
customers with a high precision of 98.62%
