Enhancing Fake News Detection: A Multimodal Approach Integrating Machine Learning

Fake news on digital platforms is a significant threat to information integrity, shaping public opinion and eroding trust in media sources. This project proposes a comprehensive approach to fake news detection using a [2]multimodal framework that combines [5]Machine learning techniques for text, images, and metadata. Unlike our approach, existing systems use [10]convolutional neural Networks (CNNs) for image analysis and [12]natural language processing (NLP) models for text analysis and metadata to get a holistic view of news content. To build a robust detection mechanism, we use a diverse dataset with real and fake news articles, manipulated images, and misleading [4]metadata. The results show a significant improvement in detection [8]accuracy over single-modal models, with high precision and [8]recall. This project not only contributes to the field of fake news detection but also highlights the importance of ethical considerations in [3]AI systems. Future work will be to extend the model to detect deepfakes and misinformation in multimedia content and apply it to real-world scenarios.