On the other hand, we expect the outcome of this process to help several applications, such as open-domain QA on the Web and retrieval from social media .

On social media , our system should be combined with a component that searches for similar questions already answered; this output can possibly be filtered further by a content-quality module that explores “social” features such as the authority of users, etc.

On the other hand, recent years have seen an explosion of user-generated content (or social media ).

In this paper we address the problem of answer ranking for non-factoid questions from social media content.

In fact, it is likely that an optimal retrieval engine from social media should combine all these three methodologies.

Moreover, our approach might have applications outside of social media (e.g., for open-domain web-based QA), because the ranking model built is based only on open-domain knowledge and the analysis of textual content.