This document proposes using a convolutional neural network (CNN) to detect and classify fake news. It first discusses the implications of fake news spreading on social media and the need for automated identification. It then explores existing fake news datasets and data preprocessing techniques. Deep learning approaches like word embeddings and CNNs are presented as promising techniques to capture semantics in text for classification. The document outlines a CNN architecture with word embedding, convolutional, max pooling and fully connected layers to output probabilities for fake/real classification. It reports the CNN approach achieved 99.8% accuracy on a 2.5GB dataset, significantly outperforming baseline models like SVM and naive bayes. Finally, contact information is provided for questions.