Fake News Detection And Verification With Machine Learning

The rapid growth of digital media platforms and social networks has significantly increased the spread of misinformation and fake news, posing serious threats to public opinion, social stability, and democratic processes. This paper presents a machine learning-based approach for the detection and verification of fake news by analyzing textual content and metadata associated with news articles. The proposed system leverages Natural Language Processing (NLP) techniques for feature extraction, including tokenization, stop-word removal, and vectorization methods such as TF-IDF. Various supervised machine learning algorithms, including Logistic Regression, Support Vector Machines, and Random Forest, are employed to classify news articles as genuine or fake. To enhance verification, the system incorporates credibility scoring based on source reliability and linguistic patterns commonly associated with misinformation. A comparative performance analysis is conducted using standard evaluation metrics such as accuracy, precision, recall, and F1-score on benchmark datasets. Experimental results demonstrate that the proposed model achieves high classification accuracy while maintaining robustness against diverse writing styles and misleading patterns. The system is designed to be scalable and adaptable, enabling real-time detection and integration with social media platforms. This research highlights the potential of combining machine learning techniques with linguistic analysis to combat the growing challenge of fake news dissemination. Future work includes incorporating deep learning models and fact-checking APIs to further improve verification accuracy and system reliability.