Optimization-Based Countering of Misinformation on Social Networks

False information is prevalent on social media platforms, necessitating effective countering methods to combat its spread. We propose an algorithm that reduces the false information spread while preserving the spread of correct information. We model the social media network as a random network of users in which each news item propagates in the network in consecutive cascades. Existing studies suggest that the cascade behaviors of misinformation and correct information are affected differently by user polarization and reflexivity. We show that this difference can be used to alter network dynamics in a way that selectively hinders the spread of misinformation content. To implement these alterations, we introduce an optimization-based probabilistic dropout method that randomly removes connections between users to achieve minimal propagation of misinformation. We test the algorithm's effectiveness using simulated social networks. In our tests, we use both synthetic network structures based on stochastic block models, and natural network structures that are generated using random sampling of data collected from Twitter. The results show that on average the algorithm decreases the cascade size of misinformation content by up to 70% in synthetic network tests and up to 45% in natural network tests while maintaining a branching ratio of at least 1.5 for correct information.