Fake news, a deliberately designed news to mislead others, is becoming a big societal threat with its fast dissemination over the Web and social media and its power to shape public opinion. Many researchers have been working to understand the underlying features that help identify these fake news on the Web. Recently, Horne and Adali found, on a small amount of data, that news title stylistic and linguistic features are better than the same type of features extracted from the news body in predicting fake news. In this paper, we present our attempt to reproduce the same results to validate their findings. We show which of their findings can be generalized to larger political and gossip news datasets.