Abstract
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.
Abstract (translated by Google)
社区问答（CQA）网站已经成为宝贵的知识库，它们拥有大量的人类知识。为了最大限度地利用这些知识，评估现有问题或答案的质量至关重要，特别是在CQA网站上发布之后不久。 在本文中，我们通过软件CQA（Stack Overflow）的案例研究来研究推断问题和答案质量的问题。我们的主要发现是答案的质量与其问题的质量强烈正相关。有了这一观察结果，我们提出了一系列算法来联合预测问题和答案的质量，既可以量化数字质量得分，又可以将高质量的问题/答案与低质量的问题/答案区分开来。我们进行了广泛的实验评估，以证明我们的方法的有效性和效率。
URL
http://arxiv.org/abs/1311.6876