Comparative of Convolutional Neural Network and Support Vector Machine for Fake News Detection

Misinformation is becoming a growing concern in today’s digital world, which poses a problem in people’s trust and worldview. In this study, we compared how Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) perform in detecting fake news using Kaggle’s fake-and-real-news dataset. Based on the evaluation, SVM also showed strong recall, F1-score, and precision, all between 0.96 and 0.97, while CNN had similar results in precision and recall, demonstrating it could generalize without overfitting. From our analysis, SVM stood out for being a simpler, faster to train, and easier to interpret model with an accuracy of 96.58%, whereas CNN was better at capturing complex features but required more computational resources with an accuracy of 96.20%. Overall, SVM performed slightly better than CNN in this study, but the choice between the two models still depends on your specific needs, whether you prioritize a model that is faster to train or one with the ability to learn deeper features.