Fake news detection is one of the most demanding tasks today in the digital era, where, with a hitch, the whole world appears to be infested with the unwanted effects of misinformation possible on giving rise to negative consequences. Most of the existing ones face several challenges such as the low generalization of different content types, reliance on shallow feature extraction, and not being able to work well with textual and contextual information, which compromise the accuracy and robustness of existing approaches. In light of these facts, the present paper proposes the Hierarchical Attention-based Twin Similarity Network (HATS-Net), a new deep learning model that employs hierarchical attention mechanisms to effectively detect fake news. The primary novelty of HATS-Net is that it effectively models text and context features using an innovative twin similarity network implementing multi-level attention basis capturing both local and global dependencies. The operational process of HATS-Net is as follows: after preprocessing the input data, the system extracts new features from the twin similarity context-attentional framework. The model, subsequently, deploys feature classifiers and boasts better results with respect to accuracy, precision, recall, F1-score, AUC-ROC, and MCC. The experimental findings show that HATS-Net can outmatch the existing techniques by a significant margin in performance. The system achieved an accuracy of 98.7%, a precision of 98.6%, a recall of 98.9%, an F1 of 98.8%, an AUC-ROC of 98.7%, and an MCC of 98.5%.