Fake News Detection in Online Media Using Deep Learning–Based Text Classification

Online media platforms have exploded in recent years, and with that, fake news has spread everywhere. It’s become a real problem—socially, politically, and even economically. Because it’s so easy to publish and share anything online, most people have a tough time figuring out what’s actually true. This makes automated fake news detection more important than ever. That’s where machine learning and deep learning come in. Researchers are putting a lot of energy into using these tools to spot fake news. The deep learning approach that relies on a Convolutional Neural Network (CNN) to classify news articles as real or fake. start by cleaning up the news text—breaking it into tokens, removing stop words, and normalizing everything so it all lines up. Turn the text into numbers using word embeddings, which helps capture the meaning and relationships between words. These numerical representations go into the CNN, which pulls out important semantic and syntactic features using convolution and pooling layers. The fully connected layers take over and classify the news as real or fake. Test our framework on a widely-used fake news dataset and measure its performance using accuracy, precision, recall, and F1- score. The CNN-based model holds its own against traditional machine learning methods, showing it’s both effective and reliable for detecting fake news in online media.