An effective fake news detection using genetic algorithm decision tree

Abstract

With the rapid development of the Internet, the spread of fake news in the media is vast and fast, becomes a social phenomenon. The proliferation of fake news has a devastating impact on the country and society. Many scholars are looking for automated methods to detect fake news. For saving computing resources, fake news detection is often solved by using machine learning algorithms. However, there are many hyperparameters in machine learning algorithms. And different hyperparameters significantly impact the model, which leads to significant differences in the results of the same model for specific problems. Finding a way to obtain stable and accurate hyperparameters has become a research direction of many scholars. This paper first analyzes the characteristics of fake news and then uses Genetic Algorithm to assist the decision tree model in finding hyperparameters. Compared with random search and traversal hyperparameter search within a specific range, this combined method is faster and more accurate. The results verify the high accuracy of the proposed method. The proposed model can improve the classification performance and is a compelling new method to solve fake news detection.