A study in Scientific Reports presents a hybrid uncertainty-aware deep learning framework for reliable Malayalam fake news detection, addressing the spread of fake news through online platforms and social media, which can influence public opinion and affect trust in legitimate information sources. The authors note that deep learning models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, BiLSTMs, Gated Recurrent Units (GRUs), and hybrid architectures have shown strong capabilities in learning complex semantic, syntactic, and contextual patterns from textual data, and that FastText integration captures rich semantic relationships between words. However, existing deep learning models provide only a single prediction for each news instance and fail to quantify confidence in decisions, which is more problematic in low-resource language settings due to limited training data and linguistic variability. The work incorporates Uncertainty Quantification (UQ) techniques into deep learning based Malayalam fake news detection, estimating the reliability of model predictions and identifying instances where the model lacks sufficient confidence. Multiple uncertainty quantification methods including MC dropout, Deep Ensembles, TTA and Evidential Deep Learning are incorporated, enabling estimation of predictive uncertainty by capturing model variability and parameter uncertainty.