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.
@cite_24 first propose the questioner satisfaction prediction in CQA. They find that question features (e.g., length of the subject, posting time, etc.) and questioner history information are very useful for the prediction task. Shah and Pomerantz @cite_2 also predict which answers the questioner would be satisfied with. They extract a set of features from the data and found that these features worked better than human labeling on several quality aspects in terms of questioner satisfaction prediction. @cite_6 studied the forum categories and user behavior patterns in Yahoo! Answer. They also formulated a classification problem to predict the acceptance of answers based on the features from the question and the answerer. All the above work considers the prediction of accepted answers. However, accepted answers are not necessarily the highest-quality highest-score answers due to timing and subjectivity issues. Different from the above work, we aim to predict the quality of question and answers which is voted and determined by the whole community.
@cite_24 @cite_6 @cite_2
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Question answering communities such as Naver and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of popular portals such as Yahoo! Answers already have submitted millions of questions and received hundreds of millions of answers from other participants. However, it may also take hours --and sometime days-- until a satisfactory answer is posted. In this paper we introduce the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a largescale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. Our models and predictions could be useful for a variety of applications such as user intent inference, answer ranking, interface design, and query suggestion and routing. Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker. Question answering (QA) helps one go beyond traditional keywords-based querying and retrieve information in more precise form than given by a document or a list of documents. Several community-based QA (CQA) services have emerged allowing information seekers pose their information need as questions and receive answers from their fellow users. A question may receive multiple answers from multiple users and the asker or the community can choose the best answer. While the asker can thus indicate if he was satisfied with the information he received, there is no clear way of evaluating the quality of that information. We present a study to evaluate and predict the quality of an answer in a CQA setting. We chose Yahoo! Answers as such CQA service and selected a small set of questions, each with at least five answers. We asked Amazon Mechanical Turk workers to rate the quality of each answer for a given question based on 13 different criteria. Each answer was rated by five different workers. We then matched their assessments with the actual asker's rating of a given answer. We show that the quality criteria we used faithfully match with asker's perception of a quality answer. We furthered our investigation by extracting various features from questions, answers, and the users who posted them, and training a number of classifiers to select the best answer using those features. We demonstrate a high predictability of our trained models along with the relative merits of each of the features for such prediction. These models support our argument that in case of CQA, contextual information such as a user's profile, can be critical in evaluating and predicting content quality.