  In this paper, we introduce a two-stage Bayesian persuasion model in which a
third-party platform controls the information available to the sender about
users' preferences. We aim to characterize the optimal information disclosure
policy of the platform, which maximizes average user utility, under the
assumption that the sender also follows its own optimal policy. We show that
this problem can be reduced to a model of market segmentation, in which
probabilities are mapped into valuations. We then introduce a repeated
variation of the persuasion platform problem in which myopic users arrive
sequentially. In this setting, the platform controls the sender's information
about users and maintains a reputation for the sender, punishing it if it fails
to act truthfully on a certain subset of signals. We provide a characterization
of the optimal platform policy in the reputation-based setting, which is then
used to simplify the optimization problem of the platform.
