  Social media platforms have become one of the main channels where people
disseminate and acquire information, of which the reliability is severely
threatened by rumors widespread in the network. Existing approaches such as
suspending users or broadcasting real information to combat rumors are either
with high cost or disturbing users. In this paper, we introduce a novel rumor
mitigation paradigm, where only a minimal set of links in the social network
are intervened to decelerate the propagation of rumors, countering
misinformation with low business cost and user awareness. A knowledge-informed
agent embodying rumor propagation mechanisms is developed, which intervenes the
social network with a graph neural network for capturing information flow in
the social media platforms and a policy network for selecting links.
Experiments on real social media platforms demonstrate that the proposed
approach can effectively alleviate the influence of rumors, substantially
reducing the affected populations by over 25%. Codes for this paper are
released at https://github.com/tsinghua-fib-lab/DRL-Rumor-Mitigation.
