Towards Computational Fact-Checking: Is the information checkable?

Fact-checking has recently become a real world hot topic, especially in what concerns political claims. Several big players, such as, for example, Google or Facebook, have started addressing/making contributions to make "fact-checking" possible/available to the general public. However, most, if not all fact-checking platforms are largely manual, in the sense that most of the contributions and of the actual checking is performed by humans. Automatic computational fact-checking is still very far from being reliable and available on a large scale.The current work is a contribution to the goal of automatic fact-checking by presenting features to distinguish checkable from uncheckable sentences and a fuzzy approach to computing sentence checkability, i.e., to answer the question: "is it possible to know if a sentence is worth to be checked?". Even though, this is a hot topic, few to none solutions have been presented to automatically assess the worthiness and liability of the verification of a sentence. The solution that is proposed is mainly based on Natural Language Processing methods, linguistics and fuzzy logic.Assessing the checkability of a sentence can have many applications besides automatic fact-checking, like, for example: a broader fact-checking view (automatic checking of webpages and articles), the summarizing of information and the evaluation of factual information on texts. The main goal, however, was to focus on analysis of individual sentences, to provide an important tool to automatic fact-checking, by finding if a sentence is worth to, and can, be checked.