Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Talk to me: foundations for successful individual-group interactions in online communities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on 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
Predictors of answer quality in online Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Socializing or knowledge sharing?: characterizing social intent in community question answering
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Journal of the American Society for Information Science and Technology
Wisdom in the social crowd: an analysis of quora
Proceedings of the 22nd international conference on World Wide Web
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On-line community services such as Live QnA and Yahoo! Answers enable their members to ask questions and have them answered by the community. The questions are labelled by the users to facilitate search, navigation, and recommendations. In this paper we provide an in-depth analysis of the question labelling practices by contrasting the use of community generated tags in the Live QnA service with the use of topic categories from a fixed taxonomy in the Yahoo! Answers service. We found that community tagging is related to higher levels of social interactions amongst users. Analysis of the most frequently used community tags reveals that active users may establish strong social ties around specific tags. Furthermore, the discriminative value of individual community tags can be low since the corresponding questions may cover a variety of topics. Thus, appropriate care needs to be taken when designing search, browsing, and recommender features for question discovery.