News false information detection algorithm based on the integration of virtual reality and generative adversarial networks

Abstract

With the development of the Internet and social media, the spread speed and breadth of fake news have significantly increased, which has brought severe negative impacts to society. Existing false information detection algorithms often lack robustness and generalization when dealing with constantly changing, complex news content. The problems with traditional research mainly include limited training data, insufficient robustness of detection algorithms, and poor generalization ability in diverse environments. This article first used GAN (Generative Adversarial Network) to generate realistic fake news data and expand the training set; secondly, a virtual reality environment was constructed to simulate users’ news consumption experiences in different contexts and collect user interaction data; then, combining Text, images, and user interaction data, deep learning models such as long short-term memory networks and convolutional neural networks were used to extract multimodal features; finally, the multimodal features were input into the fusion detection model for comprehensive analysis and decision-making. Through these steps, this article achieved significant detection results in a complex and ever-changing news environment. The research results indicated that the method proposed in this article not only improved the accuracy of fake news detection but also enhanced the model's adaptability across different contexts. The detection model's accuracy increased by 12%, from 80% to 92%. The fusion of virtual reality and generative adversarial networks significantly improved the detection model's robustness across diverse news contexts, with strong application prospects and practical value.