  Online social networks have become a fertile ground for spreading fake news.
Methods to automatically mitigate fake news propagation have been proposed.
Some studies focus on selecting top k influential users on social networks as
debunkers, but the social influence of debunkers may not translate to wide
mitigation information propagation as expected. Other studies assume a given
set of debunkers and focus on optimizing intensity for debunkers to publish
true news, but as debunkers are fixed, even if with high social influence
and/or high intensity to post true news, the true news may not reach users
exposed to fake news and therefore mitigation effect may be limited. In this
paper, we propose the multi-stage fake news mitigation campaign where debunkers
are dynamically selected within budget at each stage. We formulate it as a
reinforcement learning problem and propose a greedy algorithm optimized by
predicting future states so that the debunkers can be selected in a way that
maximizes the overall mitigation effect. We conducted extensive experiments on
synthetic and real-world social networks and show that our solution outperforms
state-of-the-art baselines in terms of mitigation effect.
