  We consider the problem of identifying coordinated influence campaigns
conducted by automated agents or bots in a social network. We study several
different Twitter datasets which contain such campaigns and find that the bots
exhibit heterophily - they interact more with humans than with each other. We
use this observation to develop a probability model for the network structure
and bot labels based on the Ising model from statistical physics. We present a
method to find the maximum likelihood assignment of bot labels by solving a
minimum cut problem. Our algorithm allows for the simultaneous detection of
multiple bots that are potentially engaging in a coordinated influence
campaign, in contrast to other methods that identify bots one at a time. We
find that our algorithm is able to more accurately find bots than existing
methods when compared to a human labeled ground truth. We also look at the
content posted by the bots we identify and find that they seem to have a
coordinated agenda.
