Misinformation prevention is a crucial research topic in the social network community, which aims to identify, mitigate, and ultimately prevent the spread of false or misleading information across the social network. Traditional methods typically rely on detailed information on underlying networks and propagation mechanisms. However, these approaches can be impractical due to privacy concerns and restrictions on data accessibility. To overcome this challenge, we propose MetaLearner, a novel framework that leverages historical pairs containing queries and their high-quality decisions. This approach enables us to derive effective decisions for new queries without requiring complete network and diffusion information. We evaluate the performance of MetaLearner by comparing it with existing methods. The results show significant improvements in both accuracy and computational efficiency, demonstrating MetaLearner as a scalable and effective solution for misinformation prevention.