Comparative Study of Supervised Machine Learning Models for Fake News Detection with Interpretability and Statistical Validation

The rapid proliferation of fake news across digital platforms has intensified the need for reliable and computationally efficient automated detection systems. While deep learning models have demonstrated strong performance, their high computational cost and limited interpretability restrict practical deployment in real-time systems. This study proposes a structured comparative framework that evaluates seven supervised machine learning algorithms—Decision Tree, Passive Aggressive, Support Vector Machine (SVM), Random Forest, Logistic Regression, Perceptron, and Naïve Bayes—under identical preprocessing and feature engineering conditions using a balanced dataset of 44,989 news articles. Unlike prior works that emphasize accuracy alone, this research integrates statistical validation, computational efficiency analysis, and interpretability assessment using SHAP explanations. Experimental results show that the Decision Tree model achieved the highest accuracy of 99.58%, closely followed by Passive Aggressive (99.57%) and SVM (99.45%). Additionally, tree-based and linear classifiers demonstrated superior stability and lower computational overhead compared to more complex architectures. The findings indicate that interpretable and computationally efficient supervised models remain highly competitive for large-scale fake news detection, offering practical advantages for real-time deployment in digital media monitoring systems.