document: Learning to recognize reliable users and content in social media with coupled mutual reinforcement Community Question Answering ( CQA ) has emerged as a popular forum for users to pose questions for other users to answer . Over the last few years , CQA portals such as Naver and Yahoo ! Answers have exploded in popularity , and now provide a viable alternative to general purpose Web search . At the same time , the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering . Unfortunately , the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries . Previous approaches for retrieving relevant and high quality content have been proposed , but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains . In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation , that requires relatively few labeled examples to initialize the training process . Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers , questions , and users . More importantly , our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods .
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