  This paper addresses the problem of determining the best answer in
Community-based Question Answering (CQA) websites by focussing on the content.
In particular, we present a system, ACQUA [http://acqua.kmi.open.ac.uk], that
can be installed onto the majority of browsers as a plugin. The service offers
a seamless and accurate prediction of the answer to be accepted. Previous
research on this topic relies on the exploitation of community feedback on the
answers, which involves rating of either users (e.g., reputation) or answers
(e.g. scores manually assigned to answers). We propose a new technique that
leverages the content/textual features of answers in a novel way. Our approach
delivers better results than related linguistics-based solutions and manages to
match rating-based approaches. More specifically, the gain in performance is
achieved by rendering the values of these features into a discretised form. We
also show how our technique manages to deliver equally good results in
real-time settings, as opposed to having to rely on information not always
readily available, such as user ratings and answer scores. We ran an evaluation
on 21 StackExchange websites covering around 4 million questions and more than
8 million answers. We obtain 84% average precision and 70% recall, which shows
that our technique is robust, effective, and widely applicable.
