text: Subjective questions such as does neymar dive', or is clinton lying', or is trump a fascist', are popular queries to web search engines, as can be seen by autocompletion suggestions on Google, Yahoo and Bing. In the era of cognitive computing, beyond search, they could be handled as hypotheses issued for evaluation. Our vision is to leverage on unstructured data and metadata of the rich user-generated multimedia that is often shared as material evidence in favor or against hypotheses in social media platforms. In this paper we present two preliminary experiments along those lines and discuss challenges for a cognitive computing system that collects material evidence from user-generated multimedia towards aggregating it into some form of collective decision on the hypothesis.
  @cite_11: This paper investigates a solution to yes no question answering, which can be mapped to the task of determining the correctness of a given proposition. Generally it is hard to obtain explicit evidence to conclude a proposition is false from an information source, so we convert this task to a set of factoid-style questions and use an existing question answering system as a subsystem. By aggregating the answers and confidence values from a factoid-style question answering system we can determine the correctness of the entire proposition or the substitutions that make the proposition false. We evaluated the system on multiple-choice questions from a university admission test on world history, and found it to be highly accurate.
summary: . Propositional (yes no) questions are an important class of questions that are addressed by Q &A systems @cite_11 , but our understanding of hypotheses as a specific kind of propositional questions is an important distinction in the enormous scope of Q &A systems. Our focus is not on factoid-style questions, e.g., Was X born in year Y?.'' By hypotheses' we mean the more subjective questions that are too intriguing to be freely answered, requiring some compelling (material) evidence.