Detecting Influence Campaigns in Social Networks Using the Ising Model

Understand

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.