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We consider the problem of identifying coordinated influence campaignsconducted by automated agents or bots in a social network. We study severaldifferent Twitter datasets which contain such campaigns and find that the botsexhibit heterophily - they interact more with humans than with each other. Weuse this observation to develop a probability model for the network structureand bot labels based on the Ising model from statistical physics. We present amethod to find the maximum likelihood assignment of bot labels by solving aminimum cut problem. Our algorithm allows for the simultaneous detection ofmultiple bots that are potentially engaging in a coordinated influencecampaign, in contrast to other methods that identify bots one at a time. Wefind that our algorithm is able to more accurately find bots than existingmethods when compared to a human labeled ground truth. We also look at thecontent posted by the bots we identify and find that they seem to have acoordinated agenda.

Detecting Influence Campaigns in Social Networks Using the Ising Model