The democratization/decentralization of both the production and consumption of information has resulted in a subjective and often misleading depiction of facts known as Fake News-a phenomenon that is effectively shaping the perception of reality for many individuals. Manual fact-checking is time-consuming and cannot scale and although automatic factchecking, vis a vis machine learning holds promise, it is significantly hindered by a deficit of suitable training data. We present both a novel dataset, VERITAS(VERIfying Textual Aspects), a collection of fact-checked claims, containing their original documents and LUX(Language Under eXamination), a text classifier that makes use of an extensive linguistic analysis to infer the likelihood of the input being a piece of fake-news.