An adaptive cyclical learning rate based hybrid model for Dravidian fake news detection

Fake news has evolved into a pervasive issue in the era of information overload, influencing public opinion and challenging the credibility of news sources. While various approaches have been proposed to combat fake news, most existing research focuses on high-resource languages, leaving low-resource languages vulnerable to misinformation. In this study, we propose a hybrid deep learning model architecture that integrates dilated temporal convolutional neural networks (DTCN), bidirectional long-short-term memory (BiLSTM), and a contextualized attention mechanism (CAM) to address the problem of detecting fake news in low-resourced Dravidian languages. DTCN is employed to capture temporal dependencies due to its sequential nature, BiLSTM is employed to seize long-range dependencies efficiently, and CAM is used to emphasize important information while downplaying irrelevant content. Additionally, we incorporate an adaptive-based cyclical learning rate with an early stopping mechanism to enhance model convergence. The results demonstrate that the proposed model surpasses the state-of-the-art and baseline models and achieves a higher average accuracy of 93.97% on the Dravidian_Fake dataset in four Dravidian languages.