An effective fake news detection method using WOA-xgbTree algorithm and content-based features

Abstract

In recent years, with the fast development of the internet and online platforms such as social media feeds, news blogs, and online newspapers, deceptive reports have been universally spread online. This manipulated news is a matter of concern due to its potential role in shaping public opinion. Therefore, the fast spread of fake news creates an urgent need for automatic systems to detect deceitful articles. This motivates many researchers to introduce solutions for the automatic classification of news items. This paper proposed a novel system to detect fake news articles based on content-based features and the WOA-Xgbtree algorithm. The proposed system can be applied in different scenarios to classify news articles. The proposed approach consists of two main stages: first, the useful features are extracted and analyzed, and then an Extreme Gradient Boosting Tree (xgbTree) algorithm optimized by the Whale Optimization Algorithm (WOA) to classify news articles using extracted features. In our experiments, we considered the bases of the investigation on classification accuracy and the F1-measure. Then, we compared the optimized model with several benchmark classification algorithms based on a dataset that compiled over 40,000 various news articles recently. The results indicate that the proposed approach achieved good classification accuracy and F1 measure rate and successfully classified over 91 percent of articles.