@article{chen2007reputation, abstract = {In this paper, we propose a user reputation model and apply it to a user-interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user-interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.}, author = {Chen, Wei and Zeng, Qingtian and Wenyin, Liu and Hao, Tianyong}, doi = {10.1002/cpe.1142}, interhash = {c304f655ee6ee183e07192b9fed0d618}, intrahash = {858df3646b706ce6308a12cbf1585d58}, issn = {1532-0634}, journal = {Concurrency and Computation: Practice and Experience}, number = 15, pages = {2091--2103}, publisher = {John Wiley & Sons, Ltd.}, title = {A user reputation model for a user-interactive question answering system}, url = {http://dx.doi.org/10.1002/cpe.1142}, volume = 19, year = 2007 } @inproceedings{jurczyk2007discovering, abstract = {Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.}, acmid = {1321575}, address = {New York, NY, USA}, author = {Jurczyk, Pawel and Agichtein, Eugene}, booktitle = {Proceedings of the sixteenth ACM conference on Conference on information and knowledge management}, doi = {10.1145/1321440.1321575}, interhash = {1c2953be3517384681b6ac831da2c766}, intrahash = {35394620d2654db8543d5da60f6f00dc}, isbn = {978-1-59593-803-9}, location = {Lisbon, Portugal}, numpages = {4}, pages = {919--922}, publisher = {ACM}, title = {Discovering authorities in question answer communities by using link analysis}, url = {http://doi.acm.org/10.1145/1321440.1321575}, year = 2007 } @inproceedings{agichtein2008finding, abstract = {The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans}, acmid = {1341557}, address = {New York, NY, USA}, author = {Agichtein, Eugene and Castillo, Carlos and Donato, Debora and Gionis, Aristides and Mishne, Gilad}, booktitle = {Proceedings of the international conference on Web search and web data mining}, doi = {10.1145/1341531.1341557}, interhash = {72c7bf5d1c983c47bfc3c6cc9084c26c}, intrahash = {29c5c74d95dce215a9692b94fc619839}, isbn = {978-1-59593-927-2}, location = {Palo Alto, California, USA}, numpages = {12}, pages = {183--194}, publisher = {ACM}, title = {Finding high-quality content in social media}, url = {http://doi.acm.org/10.1145/1341531.1341557}, year = 2008 }