International Journal of Science, Engineering and TechnologyAuthors: Sushant Khanderao Sonawane, Ritik Prakash Patil, Shriraj Suresh Mahajan, Deepali S. Suryawanshi
Abstract: The rapid spread of misleading information across digital platforms has become a significant concern, especially during critical situations such as public health emergencies, elections, and natural disasters. Traditional fact-checking ap- proaches, which depend largely on manual verification, are often too slow to respond effectively to the speed at which information circulates online. This limitation creates a need for automated systems capable of analyzing and verifying information in real time. This research presents an AI-based framework designed to identify and verify potentially false information efficiently. The proposed system combines multiple technologies, including Large Language Models (LLMs), Retrieval-Augmented Gener- ation (RAG), and Optical Character Recognition (OCR), to process both textual and visual content. By retrieving relevant information from trusted sources and analyzing it using intel- ligent models, the system generates clear and evidence-based conclusions along with confidence scores. In addition, the framework incorporates a self-learning knowl- edge structure that improves performance over time by storing validated information and learning from previous analyses. The system is also designed to provide understandable explanations for its decisions, increasing transparency and user trust. Overall, the proposed approach offers a scalable and practical solution for real-time misinformation detection across various digital platforms.
