  We propose the first multistage intervention framework that tackles fake news
in social networks by combining reinforcement learning with a point process
network activity model. The spread of fake news and mitigation events within
the network is modeled by a multivariate Hawkes process with additional
exogenous control terms. By choosing a feature representation of states,
defining mitigation actions and constructing reward functions to measure the
effectiveness of mitigation activities, we map the problem of fake news
mitigation into the reinforcement learning framework. We develop a policy
iteration method unique to the multivariate networked point process, with the
goal of optimizing the actions for maximal total reward under budget
constraints. Our method shows promising performance in real-time intervention
experiments on a Twitter network to mitigate a surrogate fake news campaign,
and outperforms alternatives on synthetic datasets.
