Enhancing Online Social Media Fake News Recognition Using Deep Learning Models

New tools that can be used to sway public opinion on community media stages take lived made possible by recent developments in accepted linguistic generation. To improve the identification of false information on social media, a standard Convolutional Neural Network (CNN) architecture is suggested. Term Frequency (TF), term frequency-inverse document frequency (TF-IDF), Fast Text embeddings, and Fake News Acceptance features are some of the standard mechanism education models that are compared to the suggested method in order to show how effective it is. Furthermore, the suggested CNN model's performance is assessed in comparison to alternative deep learning methodologies, including hybrid CNN-LSTM architectures and Long Short-Term Memory (LSTM) networks. The grades reveal that the CNN-based method, mainly when combined beside Fake News Recognition features, achieves superior accuracy—reaching up to 93%—in classifying tweet data. This highlights the model's efficiency and effectiveness in detecting fake social media content.