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_2: Engaging in a debate with oneself or others to take decisions is an integral part of our day-today life. A debate on a topic (say, use of performance enhancing drugs) typically proceeds by one party making an assertion claim (say, PEDs are bad for health) and then providing an evidence to support the claim (say, a 2006 study shows that PEDs have psychiatric side effects). In this work, we propose the task of automatically detecting such evidences from unstructured text that support a given claim. This task has many practical applications in decision support and persuasion enhancement in a wide range of domains. We first introduce an extensive benchmark data set tailored for this task, which allows training statistical models and assessing their performance. Then, we suggest a system architecture based on supervised learning to address the evidence detection task. Finally, promising experimental results are reported.
summary: . Our work is related to the so-called debating' technologies at IBM @cite_2 . Their work is strongly based on machine learning methods to track correlated text passages that may be spread over a corpus. Going beyond the observational hypotheses that are more on our sight and are typical of social media, the goal is to enable eliciting all kind of textual information that may be relevant as evidence to decide on a claim. An example is: (topic) ''; (claim) ''; (evidence) .'' To date, as far as we know this is yet a fully NLP project, which does not consider multimedia data for grounding on material evidence.