Enhancing hierarchical attention networks with CNN and stylistic features for fake news detection

The rise of social media platforms has led to a proliferation of false information in various forms. Identifying malicious entities on these platforms is challenging due to the complexities of natural language and the sheer volume of textual data. Compounding this difficulty is the ability of these entities to deliberately modify their writing style to make false information appear trustworthy. In this study, we propose a neural-based framework that leverages the hierarchical structure of input text to detect both fake news content and fake news spreaders. Our approach utilizes enhanced Hierarchical Convolutional Attention Networks (eHCAN), which incorporates both style-based and sentiment-based features to enhance model performance. Our results show that eHCAN outperforms several strong baseline methods, highlighting the effectiveness of integrating deep learning (DL) with stylistic features. Additionally, the framework uses attention weights to identify the most critical words and sentences, providing a clear explanation for the model's predictions. eHCAN not only demonstrates exceptional performance but also offers robust evidence to support its predictions.