Benchmarking Machine Learning and Deep Learning Models for Fake News Detection Using News Headlines

The proliferation of fake news presents a major challenge for information integrity in the digital age. In this work, we systematically compare the performance of five widely used machine learning and deep learning models—Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), ALBERT, and Gated Recurrent Units (GRU)—on the WELFake dataset, utilizing only the news article headlines as input. Each model’s mathematical formulation and optimization process are described, and their ability to capture salient features in brief textual data is evaluated. Experimental results show that transformer-based ALBERT outperforms all other models in both precision and recall, demonstrating the value of contextual encoding even with limited input. Ensemble models, particularly Random Forest and LightGBM, also deliver strong performance and offer interpretability benefits for practical deployment. This study provides actionable insights for deploying efficient and accurate fake news detection models when only headline information is available.