AI based Fake News Detection in Social Platforms using Transfer Learning Models

This project focuses on developing and assessing AI-driven techniques for the automated detection of fake news on social media platforms. It investigates two primary model architectures: a conventional deep learning model and a transfer learning model based on pretrained language representations. Both models are trained on large-scale annotated datasets containing news articles and social media posts with verified authenticity labels. The conventional deep learning approach provides a solid baseline for classification tasks, while the transfer learning method leverages contextual understanding from pre-trained models. Experimental evaluations reveal that the transfer learning model outperforms the conventional model in both accuracy and generalization. It demonstrates robustness across diverse data distributions and unseen content. The study highlights the adaptability of transfer learning in handling evolving patterns of misinformation. The models are assessed using performance metrics such as precision, recall, and F1-score. Results show that the proposed approach enhances detection efficiency significantly. The framework can be integrated into social media analysis pipelines for real-time detection. Automated flagging of potential misinformation supports timely human intervention. The system’s scalable architecture allows deployment across multiple platforms. Continuous learning mechanisms further refine the model over time. Ultimately, this research contributes to reducing misinformation and fostering trust in digital communication spaces.