Computational Statistics & Data Analysis - Nonlinear methods and data mining
Semi-supervised learning with graphs
Semi-supervised learning with graphs
A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Finding the right facts in the crowd: factoid question answering over social media
Proceedings of the 17th international conference on World Wide Web
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Predicting information seeker satisfaction in community question answering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Quality-aware collaborative question answering: methods and evaluation
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Modeling information-seeker satisfaction in community question answering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
Probabilistic question recommendation for question answering communities
Proceedings of the 18th international conference on World wide web
Ranking community answers by modeling question-answer relationships via analogical reasoning
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The use of categorization information in language models for question retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Proceedings of the 19th international conference on World wide web
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Evaluating and predicting answer quality in community QA
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Routing questions to appropriate answerers in community question answering services
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Using graded-relevance metrics for evaluating community QA answer selection
Proceedings of the fourth ACM international conference on Web search and data mining
Question routing in community question answering: putting category in its place
Proceedings of the 20th ACM international conference on Information and knowledge management
Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Wisdom in the social crowd: an analysis of quora
Proceedings of the 22nd international conference on World Wide Web
Fit or unfit: analysis and prediction of 'closed questions' on stack overflow
Proceedings of the first ACM conference on Online social networks
Chaff from the wheat: characterization and modeling of deleted questions on stack overflow
Proceedings of the 23rd international conference on World wide web
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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.