Enhancing Fake News Detection: A Comprehensive Framework Leveraging XGBoost and CNNs

This study proposes a practical technique for detecting false information in both textual and video formats utilizing the eXtreme gradient boost (XGBoost) and Convolutional Neural Networks (CNNs) classification techniques. We increase a robust and flexible system that integrates human insights with the computational energy of XGBoost and CNNs leveraging these strategies. Initially specializing in textual analysis, linguistic skills are employed to enhance the system’s knowledge of written content, drawing insights from social media posts and information memories. Inspired by the adaptable nature of XGBoost and CNN algorithms, these insights facilitate the creation of a capable fake news detection system. Furthermore, spotting the increasing significance of video content material in the propagation of incorrect information, the proposed scheme is adapted to assess video material thoroughly. We analyze video speech content to detect fixed and misleading content using CNN and XGBoost together. Through rigorous testing with different datasets and simulated scenarios, our system demonstrates an impressive accuracy of around 96 %. Structured to deal with various types of misinformation in text and video content, our approach provides a practical way of identifying misinformation, ensuring the credibility of the content in the digital age. Compared with other existing methods, it reveals high accuracy, highlighting the effectiveness of our collaboration method in truthful detection.