The rise of social media as a source of news consumption has led to the spread of fake news, posing serious consequences for individuals and society. The detection and prevention of fake news is essential, and previous research has shown that incorporating news content and its associated headlines/user comments can improve detection performance. However, semantic relationships between these elements have not been fully explored. This paper proposes a novel approach that models the relations between news body and associated headline/user comments using deep learning techniques, such as fine-tuned Bidirectional Encoder Representations from Transformers (BERT) and cross-level cross-modality attention sub-networks. In our proposed model, we utilize two different configurations of BERT: pool-based representation, which offers a representation of the entire document, and sequence representation, which represents each token within the document (i.e., word level and text level). The approach also encodes user-posting behavioural features and fuses the output of these components to detect fake news using a classification layer. Our experiments on benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art (SOTA) approaches, highlighting the importance of utilizing semantic relationships for improved detection of fake news. These findings have significant implications for combating the spread of fake news and protecting society from its negative effects.