The rapid development of different social media and content-sharing platforms has been largely exploited to spread misinformation and fake news that make people believing in harmful stories, which allow to influence public opinion, and could cause panic and chaos among population. Thus, fake news detection has become an important research topic, aiming at flagging a specific content as fake or legitimate. The fake news detection solutions can be divided into three main categories: content-based, social context-based, and knowledge-based approaches. In this paper, we propose a novel hybrid fake news detection system that combines linguistic and knowledge-based approaches and inherits their advantages, by employing two different sets of features: (1) linguistic features (i.e., title, number of words, reading ease, lexical diversity,and sentiment), and (2) a novel set of knowledge-based features, called fact-verification features that comprise three types of information namely, (i) reputation of the website where the news is published, (ii) coverage , i.e., number of sources that published the news, and (iii) fact-check , i.e., opinion of well-known fact-checking websites about the news, i.e., true or false. The proposed system only employs eight features, which is less than most of the state-of-the-art approaches. Also, the evaluation results on a fake news dataset show that the proposed system employing both types of features can reach an accuracy of 94.4%, which is better compared to that obtained from separately employing linguistic features (i.e., accuracy=89.4%) and fact-verification features (i.e., accuracy=81.2%).