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摘要

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