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This paper discusses the necessity of fake news detection and how selected features can show differences between trustworthy and fake news. To demonstrate this concept, we first identified a set of features, that we believe can distinguish between fake and trustworthy news. We used these features to analyse two real datasets and evaluated our results in various ways. We first used visual analysis by means of boxplots and evidenced the significance of differences by means of the Wilcox singed-rank test. As next, we used three different classification algorithms to train models for distinguishing between trustworthy and fake news using all important features. Finally, we used Principal Component Analysis (PCA) to visualize relations between identified features.