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Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine… (see more) 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.
This study proposes an enhancement to the ConText-GAN, an image synthesis model using a controllable texture input. The improvement consists… (see more) in using a texture feature fusion module to reduce the complexity of the model, and enable the use of the OASIS architecture for image generation.
2023-10-15
2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (published)
