Fake news detection in Dravidian languages using multiscale residual CNN_BiLSTM hybrid model

Fake news detection is the process of identifying news that contain purposeful misinformation disseminated through traditional news sources or social media platforms. Detecting fake news in Dravidian languages poses unique challenges due to the linguistic diversity and limited resources available for these languages. In this study, we propose a novel approach for fake news detection in Dravidian languages, leveraging contextualized word embeddings from MuRIL. MuRIL provides contextual embeddings that capture nuanced linguistic nuances, making it well-suited for the complexities of Dravidian languages. The proposed hybrid model combines multiscale residual CNN and BiLSTM layers to capture local and global dependencies in textual data effectively. The multiscale architecture allows the model to extract features at various levels of granularity while the BiLSTM layer captures long-range dependencies and contextual information. We performed extensive experiments on diverse datasets in Dravidian languages to evaluate the proposed approach. The results demonstrate the effectiveness of our hybrid model in discerning between genuine and fake news articles. •An optimized hybrid model is proposed for fake news detection.•Multiscale residual CNN is used for n-gram features.•Training optimization techniques are used to prevent overfitting.•The proposed model outperforms SOTA in Dravidian fake news detection.