Unveiling the hidden patterns: A novel semantic deep learning approach to fake news detection on social media

The rise of social media as a source of news consumption has led to the spread of fake news, posing serious consequences for both individuals and society. The detection and prevention of fake news are essential, and previous research has shown that incorporating news content along with its associated headlines and user comments can improve detection performance. However, the semantic relationships between these elements have not been fully explored. This paper proposes a novel approach that models the relationships between news bodies and associated headlines/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 provides a representation of the entire document, and sequence representation, which represents each token within the document (i.e., at the word and text levels). 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 fake news detection (FND). These findings have significant implications for combating the spread of fake news and protecting society from its negative effects.