  In many instances one may want to gain situational awareness in an
environment by monitoring the content of local social media users. Often the
challenge is how to build a set of users from a target location. Here we
introduce a method for building such a set of users by using an
\emph{expand-classify} approach which begins with a small set of seed users
from the target location and then iteratively collects their neighbors and then
classifies their locations. We perform this classification using maximum
likelihood estimation on a factor graph model which incorporates features of
the user profile and also social network connections. We show that maximum
likelihood estimation reduces to solving a minimum cut problem on an
appropriately defined graph. We are able to obtain several thousand users
within a few hours for many diverse locations using our approach. Using
geo-located data, we find that our approach typically achieves good accuracy
for population centers with less than 500,000 inhabitants, while for larger
cities performance degrades somewhat. We also find that our approach is able to
collect many more users with higher accuracy than existing search methods.
Finally, we show that by studying the content of location specific users
obtained with our approach, we can identify the onset of significant social
unrest in locations such as the Philippines.
