Article’s

The rapid proliferation of digital media has democratized information sharing, but it has simultaneously catalyzed the spread of fake news, misinformation, and disinformation. The velocity and volume at which false narratives propagate pose significant threats to public discourse, social stability, and democratic institutions. This paper proposes a novel Multimodal Context-Aware Architecture (MCAA) driven by Artificial Intelligence (AI) to detect fake information in real-time. By integrating advanced Natural Language Processing (NLP) techniques for textual analysis with Computer Vision for media verification, the proposed system evaluates the veracity of news articles and social media posts. We employ a hybrid deep learning model combining a pre-trained Transformer network (RoBERTa) for text and a Convolutional Neural Network (ResNet) for visual feature extraction, alongside a metadata analyzer. Experimental evaluations on benchmark datasets (such as FakeNewsNet and LIAR) demonstrate that our multimodal approach achieves an accuracy of 94.2%, outperforming unimodal baselines. This paper details the system architecture, methodology, implementation, and future directions for robust misinformation detection.