A Persuasive Approach to Combating Misinformation

We propose using Bayesian Persuasion as a tool for social media platforms to combat the spread of online misinformation. As platforms can predict the popularity and misinformation features of to-be-shared posts, and users are motivated to only share popular content, platforms can strategically reveal this informational advantage to persuade users to not share misinformed content. Our work mathematically characterizes the optimal information design scheme and the resulting utility when observations are not perfectly observed but arise from an imperfect classifier. Framing the optimization problem as a linear program, we give sufficient and necessary conditions on the classifier accuracy to ensure platform utility under optimal signaling is monotonically increasing and continuous. We next consider this interaction under a performative model, wherein platform intervention through signaling affects the content distribution in the future. We fully characterize the convergence and stability of optimal signaling under this performative process. Lastly, the broader scope of using information design to combat misinformation is discussed throughout.