  Bayesian Persuasion is proposed as a tool for social media platforms to
combat the spread of misinformation. Since platforms can use machine learning
to predict the popularity and misinformation features of to-be-shared posts,
and users are largely motivated to share popular content, platforms can
strategically signal this informational advantage to change user beliefs and
persuade them not to share misinformation. We characterize the optimal
signaling scheme with imperfect predictions as a linear program and give
sufficient and necessary conditions on the classifier to ensure optimal
platform utility is non-decreasing and continuous. Next, this interaction is
considered under a performative model, wherein platform intervention affects
the user's future behaviour. The convergence and stability of optimal signaling
under this performative process are fully characterized. Lastly, we
experimentally validate that our approach significantly reduces misinformation
in both the single round and performative setting and discuss the broader scope
of using information design to combat misinformation.
