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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Users tend to ask and answer questions in community question answering (CQA) services to seek information and share knowledge. A corollary is that myriad of questions and answers appear in CQA service. Accordingly, volumes of studies have been taken to explore the answer quality so as to provide a preliminary screening for better answers. However, to our knowledge, less attention has so far been paid to question quality in CQA. Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service. In this paper, we are conducting two studies to investigate the question quality issue. The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality. Based on this finding, in the second study we propose a Mutual Reinforcement-based Label Propagation (MRLP) algorithm to predict question quality. We experiment with Yahoo! Answers data and the results demonstrate the effectiveness of our algorithm in distinguishing high-quality questions from low-quality ones. Asking the right question in the right way is an art (and a science). In a community question-answering setting, a good question is not just one that is found to be useful by other people: a question is good if it is also presented clearly and shows prior research. Using a community question-answering site that allows voting over the questions, we show that there is a notion of question quality that goes beyond mere popularity. We present techniques using latent topic models to automatically predict the quality of questions based on their content. Our best system achieves a prediction accuracy of 72 , beating out strong baselines by a significant amount. We also examine the effect of question quality on the dynamics of user behavior and the longevity of questions. Question answering (Q&A) websites are now large repositories of valuable knowledge. While most Q&A sites were initially aimed at providing useful answers to the question asker, there has been a marked shift towards question answering as a community-driven knowledge creation process whose end product can be of enduring value to a broad audience. As part of this shift, specific expertise and deep knowledge of the subject at hand have become increasingly important, and many Q&A sites employ voting and reputation mechanisms as centerpieces of their design to help users identify the trustworthiness and accuracy of the content. To better understand this shift in focus from one-off answers to a group knowledge-creation process, we consider a question together with its entire set of corresponding answers as our fundamental unit of analysis, in contrast with the focus on individual question-answer pairs that characterized previous work. Our investigation considers the dynamics of the community activity that shapes the set of answers, both how answers and voters arrive over time and how this influences the eventual outcome. For example, we observe significant assortativity in the reputations of co-answerers, relationships between reputation and answer speed, and that the probability of an answer being chosen as the best one strongly depends on temporal characteristics of answer arrivals. We then show that our understanding of such properties is naturally applicable to predicting several important quantities, including the long-term value of the question and its answers, as well as whether a question requires a better answer. Finally, we discuss the implications of these results for the design of Q&A sites. Stack Overflow is the most popular Community based Question Answering (CQA) website for programmers on the web with 2.05M users, 5.1M questions and 9.4M answers. Stack Overflow has explicit, detailed guidelines on how to post questions and an ebullient moderation community. Despite these precise communications and safeguards, questions posted on Stack Overflow can be extremely off topic or very poor in quality. Such questions can be deleted from Stack Overflow at the discretion of experienced community members and moderators. We present the first study of deleted questions on Stack Overflow. We divide our study into two parts - (i) Characterization of deleted questions over 5 years (2008-2013) of data, (ii) Prediction of deletion at the time of question creation. Our characterization study reveals multiple insights on question deletion phenomena. We find that it takes substantial time to vote a question to be deleted but once voted, the community takes swift action. We also see that question authors delete their questions to salvage reputation points. We notice some instances of accidental deletion of good quality questions but such questions are voted back to be undeleted quickly. We discover a pyramidal structure of question quality on Stack Overflow and find that deleted questions lie at the bottom (lowest quality) of the pyramid. We also build a predictive model to detect the deletion of question at the creation time. We experiment with 47 features -- based on User Profile, Community Generated, Question Content and Syntactic style -- and report an accuracy of 66 . Our findings reveal important suggestions for content quality maintenance on community based question answering websites. To the best of our knowledge, this is the first large scale study on poor quality (deleted) questions on Stack Overflow.