Question Answering (CQA) websites have become valu- able repositories which host a massive volume of human knowl- edge. 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 ques- tions 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. A rmed with this observation, we propose a family of algorithms to jointly pre- dict the quality of questions and answers, for both quantifying nu- merical quality scores and differentiating the high-quali ty ques- tions answers from those of low quality. We conduct extensive experimental evaluations to demonstrate the effectiveness and ef- ficiency of our methods.
To overcome the subjectivity issue of questioner satisfaction, many proposals resort to the quality measures derived from long-term community voting or human labeling. For example, @cite_4 conduct a field study on several question answering websites to seek the reasons for high-quality answers. They use the human labels as the quality indicator and find that factors such as community effect and payment play important roles in answer quality while rhetorical strategy and question type have little effect. @cite_3 study the answer quality of code examples in Stack Overflow. They use the community voted score of an answer as the quality measure. Similar to this work, we also use the voted score, which is the difference between the number of up-votes and down-votes from the community, as the quality measure.
@cite_4 @cite_3
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Question and answer (Q&A) sites such as Yahoo! Answers are places where users ask questions and others answer them. In this paper, we investigate predictors of answer quality through a comparative, controlled field study of responses provided across several online Q&A sites. Along with several quantitative results concerning the effects of factors such as question topic and rhetorical strategy, we present two high-level messages. First, you get what you pay for in Q&A sites. Answer quality was typically higher in Google Answers (a fee-based site) than in the free sites we studied, and paying more money for an answer led to better outcomes. Second, we find that a Q&A site's community of users contributes to its success. Yahoo! Answers, a Q&A site where anybody can answer questions, outperformed sites that depend on specific individuals to answer questions, such as library reference services. Programmers learning how to use an API or a programming language often rely on code examples to support their learning activities. However, what makes for an effective ode example remains an open question. Finding the haracteristics of the effective examples is essential in improving the appropriateness of these learning aids. To help answer this question we have onducted a qualitative analysis of the questions and answers posted to a programming Q&A web site called StackOverflow. On StackOverflow answers can be voted on, indicating which answers were found helpful by users of the site. By analyzing these well-received answers we identified haracteristics of effective examples. We found that the explanations acompanying examples are as important as the examples themselves. Our findings have implications for the way the API documentation and example set should be developed and evolved as well as the design of the tools assisting the development of these materials.