News dissemination false information detection algorithms based on intelligent information processing and hybrid networks

Abstract

Traditional news false information detection can no longer adapt to the current information detection evasion mode. This article addressed the language changes and diversity in identifying news false information, and improved the accuracy of false information detection. Firstly, the system goal was defined to improve the accuracy of false information detection. Then, news data from FakeNewNet, BuzzFeedNews, and PoliFact platforms were collected, and the data was subjected to data cleaning, segmentation, removal of stop words, One-Hot encoding, TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction, and word embedding preprocessing. After establishing a Hybrid Neural Network (HNN) model, model training, evaluation, and optimization work were carried out. In the experimental stage, 5 new datasets were added and 5 other detection algorithm models were applied to compare the detection accuracy with the proposed model. Robustness experiments were conducted to verify the robustness of the model. The experimental results showed that the accuracy of the model in this article on eight news information datasets ranged from 0.9972 to 0.9998, with a mean of 0.9987. The average accuracy of the other five algorithms was 0.8010, 0.7738, 0.7394, 0.8676, and 0.7689, respectively. The detection accuracy of the algorithm in this article was much higher than the other five algorithms, and it had high robustness. Intelligent information processing and hybrid networks have brought a more comprehensive and accurate solution to the detection of false information in news dissemination, successfully solving the problem of identifying the changing styles of false information, improving the accuracy of fake news detection, and providing a very good idea for fake news detection.