<p>Social media has become a primary source of information, but its rapid growth has facilitated the spread of misinformation. Existing methods often rely on static graph neural networks (GNNs) to analyze news propagation patterns, overlooking temporal dynamics and semantic nuances. To address this, we propose a Temporal Graph Convolution Network with a Hierarchical Co-Attention (TGCN-HCA) model for effective fake news detection. TGCN-HCA leverages a dynamic graph representation to capture the evolving nature of news propagation. By incorporating temporal information, we can accurately model the sequential spread of news and identify patterns associated with fake news. To enhance semantic understanding, we employ GPT-2 to extract deep contextual features from news content. These features, combined with structural information from the graph, provide a rich representation of news propagation. A hierarchical co-attention mechanism further refines feature representation by jointly considering multiple perspectives: the importance of nodes within the graph and the relevance of semantic features to the task of fake news detection. By integrating these components, TGCN-HCA effectively captures the complex interplay between temporal dynamics, structural information, and semantic content. Extensive experiments on Twitter15, Twitter16, and Weibo datasets demonstrate the superior performance of TGCN-HCA over state-of-the-art methods. Our model achieves significant accuracy gains of 5.37%, 4.08%, and 1.03% on these datasets, respectively.</p>