An Intelligent Fake News Detection Framework Using Machine Learning and Natural Language Processing

The rapid spread of misinformation on social media and online platforms has become a critical global challenge, impacting public opinion and decision-making in areas such as politics and public health. This paper presents an automated fake news detection system based on machine learning techniques. The proposed approach preprocesses textual data and transforms it into numerical features using the TF-IDF method. Four classification algorithms—Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Passive Aggressive Classifier—are evaluated for performance. Experimental results demonstrate that Logistic Regression achieves the highest accuracy of approximately 96.8%. Despite its effectiveness, the system is limited by its dependence on training data quality and its inability to detect non-textual misinformation. Overall, the study highlights the potential of machine learning as a practical solution for fake news detection.