  Misinformation has become a growing issue on online social platforms (OSPs),
especially during elections or pandemics. To combat this, OSPs have implemented
various policies, such as tagging, to notify users about potentially misleading
information. However, these policies are often transparent and therefore
susceptible to being exploited by content creators, who may not be willing to
invest effort into producing authentic content, causing the viral spread of
misinformation. Instead of mitigating the reach of existing misinformation,
this work focuses on a solution of prevention, aiming to stop the spread of
misinformation before it has a chance to gain momentum. We propose a Bayesian
persuaded branching process ($\operatorname{BP}^2$) to model the strategic
interactions among the OSP, the content creator, and the user. The
misinformation spread on OSP is modeled by a multi-type branching process,
where users' positive and negative comments influence the misinformation
spreading. Using a Lagrangian induced by Bayesian plausibility, we characterize
the OSP's optimal policy under the perfect Bayesian equilibrium. The convexity
of the Lagrangian implies that the OSP's optimal policy is simply the fully
informative tagging policy: revealing the content's accuracy to the user. Such
a tagging policy solicits the best effort from the content creator in reducing
misinformation, even though the OSP exerts no direct control over the content
creator. We corroborate our findings using numerical simulations.
