The swift propagation of fake news via online social networks severely undermines public trust and social harmony, compelling the development of resilient and efficient detection strategies. While prevailing methodologies primarily focus on either content-based analysis, leveraging linguistic and visual features, or context-based methods, utilizing social network dynamics, they often fail to fully integrate the semantic depth of news content with the relational complexity of its propagation. To tackle these challenges, this research introduces an innovative framework that synergizes Hypergraph Neural Networks (HGNN) with Large Language Models (LLMs) to enhance fake news detection. The framework leverages LLMs to extract intricate linguistic and semantic features from news content, while using HGNN to capture the complex, multi-faceted interactions within the news propagation network. The integration of these components fortifies the framework’ ability to discern fake news, synergizing the advantages of both content-driven and context-aware methodologies. Experiments demonstrate that HGNN significantly outperforms traditional Graph Neural Networks (GNNs) in capturing complex relational information, leading to superior robust detection performance. The integration of LLMs further improves text encoding accuracy. This research underscores the efficacy of leveraging advanced language models in conjunction with hypergraph-based architectures, providing a more resilient and holistic approach to fake news detection within social media context. Future work will focus on addressing dynamic hypergraph construction challenges and scalability improvements for real-world applications.