Comparative Analysis for Fake News Detection using Machine Learning and Hybrid Deep Learning Techniques

This paper compares two machine learning models (Logistic Regression and Decision Tree) with a hybrid CNN–LSTM deep learning model for fake news detection. While the ML models achieved high accuracy on the training set, they failed to generalize on unseen data due to overfitting. In contrast, the CNN–LSTM model, trained on Kaggle dataset with robust preprocessing, achieved an accuracy of 93.95%, along with balanced precision, recall, and F1-score. The results highlight the superior generalization of deep learning (DL)and suggest future improvement using transformer-based architectures.