Query routing for Web search engines: architectures and experiments
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Question answering from the web using knowledge annotation and knowledge mining techniques
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Modeling information-seeker satisfaction in community question answering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Crowdsourcing human-based computation
Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
A community question-answering refinement system
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Health conversational system based on contextual matching of community-driven question-answer pairs
Proceedings of the 20th ACM international conference on Information and knowledge management
Understanding user intent in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Penguins in sweaters, or serendipitous entity search on user-generated content
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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While question answering communities have been gaining popularity for several years, we wonder if the increased popularity actually improves or degrades the user experience. In addition, automatic QA systems, which utilize different sources such as search engines and social media, are emerging rapidly. QA communities have already created abundant resources of millions of questions and hundreds of millions of answers. The question whether they will continue to serve as an effective source is of information for web search and question answering is of vital importance. In this poster, we investigate the temporal evolution of a popular QA community - Yahoo! Answers, with respect to its effectiveness in answering three basic types of questions: factoid, opinion and complex questions. Our experiments show that Yahoo! Answers keeps growing rapidly, while its overall quality as an information source for factoid question-answering degrades. However, instead of answering factoid questions, it might be more effective to answer opinion and complex questions.