Next-Generation Fake News Detection: Leveraging Multi-Modal Data and Explainable AI for Enhanced Accuracy

Project Objective

The proliferation of fake news has become a significant challenge in the digital age, affecting public opinion, democratic processes, and societal trust. Traditional fake news detection methods primarily rely on textual analysis, limiting their effectiveness against increasingly sophisticated misinformation that spans multiple forms of media. This paper proposes a next-generation fake news detection framework that leverages multi-modal data, including text, images, videos, and social signals, to enhance detection accuracy. By integrating information from diverse sources, the proposed system captures complex correlations that single-modal approaches may overlook. In addition, the framework incorporates Explainable Artificial Intelligence (XAI) techniques to provide transparent insights into the model's decision-making process, ensuring accountability and fostering user trust. The paper explores state-of-the-art machine learning algorithms, such as transformers and graph neural networks, and applies them to real-world datasets to demonstrate the superiority of multi-modal approaches over traditional methods. Experimental results show significant improvements in detection accuracy, while XAI visualizations offer actionable insights for stakeholders, including fact-checkers, media outlets, and policymakers. This research highlights the potential of combining multi-modal data and explainability to create more robust and trustworthy fake news detection systems, paving the way for a safer and more informed digital landscape.