  Community Question Answering (CQA) websites have become valuable repositories
which host a massive volume of human knowledge. To maximize the utility of such
knowledge, it is essential to evaluate the quality of an existing question or
answer, especially soon after it is posted on the CQA website.
  In this paper, we study the problem of inferring the quality of questions and
answers through a case study of a software CQA (Stack Overflow). Our key
finding is that the quality of an answer is strongly positively correlated with
that of its question. Armed with this observation, we propose a family of
algorithms to jointly predict the quality of questions and answers, for both
quantifying numerical quality scores and differentiating the high-quality
questions/answers from those of low quality. We conduct extensive experimental
evaluations to demonstrate the effectiveness and efficiency of our methods.
