Fake News Detection Using Machine Learning and BERT: A Comparative Study

This study presents a comparative analysis of traditional machine learning models and a transformer-based approach for fake news detection. I investigate the performance of Logistic Regression and Support Vector Machines (SVM), utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, against a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. Using a balanced dataset of 4,000 fake and 4,000 real news articles, I apply standard preprocessing and perform an 80/20 train-test split. Experimental results demonstrate that Logistic Regression and SVM achieve accuracies of 97.94% and 98.62%, respectively, while BERT significantly outperforms both with an accuracy of 99.94%. Each model is evaluated using metrics including precision, recall, F1-score, and ROC-AUC, as well as training time and inference latency. The results emphasize the trade-offs between computational cost and classification performance, with BERT offering the highest accuracy at the expense of increased resource demands. This research contributes to the development of effective and scalable solutions for combating misinformation in digital media.