Fake News Detection

This research demonstrates that fake news detection can be effectively addressed using text-based machine learning and deep learning approaches. By evaluating a wide range of models from traditional classifiers to advanced transformer architectures. This study provides a comprehensive comparison of techniques for misinformation detection. The results show that BERT-based models achieve the highest performance due to their contextual understanding of language. At the same time, classical machine learning models using TF-IDF features offer competitive accuracy with significantly lower computational cost, making them suitable for real-time applications. Overall, this study highlights the importance of combining multiple approaches to build robust and scalable fake news detection systems. The proposed framework can be applied to news verification platforms, social media monitoring systems, and content moderation tools. Future work may explore multimodal data, explainable AI techniques, and real-time deployment strategies to further enhance misinformation detection.

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