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.

近年来，随着互联网和社交媒体、新闻博客、网络报纸等网络平台的快速发展，虚假报道在网络上普遍传播。由于其在塑造公众舆论方面的潜在作用，这种被操纵的新闻令人担忧。因此，假新闻的快速传播迫切需要自动系统来检测欺骗性文章。这促使许多研究人员为新闻项目的自动分类引入解决方案。本文提出了一种基于内容特征和 WOA-Xgbtree 算法来检测假新闻文章的新系统。所提出的系统可以应用于不同的场景来对新闻文章进行分类。所提出的方法包括两个主要阶段：首先，提取和分析有用的特征，然后是通过 Whale 优化算法 (WOA) 优化的极限梯度提升树 (xgbTree) 算法，使用提取的特征对新闻文章进行分类。在我们的实验中，我们考虑了分类准确度和 F1 度量的调查基础。然后，我们基于最近编译了 40,000 多篇各种新闻文章的数据集，将优化后的模型与几种基准分类算法进行了比较。结果表明，所提出的方法实现了良好的分类准确率和 F1 测量率，成功分类了超过 91% 的文章。然后，我们基于最近编译了 40,000 多篇各种新闻文章的数据集，将优化后的模型与几种基准分类算法进行了比较。结果表明，所提出的方法实现了良好的分类准确率和 F1 测量率，成功分类了超过 91% 的文章。然后，我们基于最近编译了 40,000 多篇各种新闻文章的数据集，将优化后的模型与几种基准分类算法进行了比较。结果表明，所提出的方法实现了良好的分类准确率和 F1 测量率，成功分类了超过 91% 的文章。