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
Temporal Analysis of the Wikigraph
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Statistical analysis of the social network and discussion threads in slashdot
Proceedings of the 17th international conference on World Wide Web
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Predicting information seeker satisfaction in community question answering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The "nays" have it: exploring effects of sentiment in collaborative knowledge sharing
WSA '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
A time-dependent topic model for multiple text streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Online knowledge sharing sites have recently exploded in popularity, and have began to play an important role in online information seeking. Unfortunately, many factors that influence the effectiveness of the information exchange in these communities are not well understood. This paper is an attempt to fill this gap by exploring the dynamics of information sharing in such sites - that is, identifying the factors that can explain how people respond to information requests. As a case study, we use Yahoo! Answers, one of the leading knowledge sharing portals on the web with millions of active participants. We follow the progress of thousands of questions, from posting until resolution. We examine contextual factors such as the topical area of the questions, as well as intrinsic factors of question wording, subjectivity, sentiment, and other characteristics that could influence how a community responds to an information request. Our findings could be useful for improving existing collaborative question answering systems, and for designing the next generation of knowledge sharing communities.