Finding high-quality content in social media

Finding high-quality content in social media Finding High-Quality Content in Social Media Eugene Agichtein eugene@mathcs.emory.edu Emory University Atlanta, USA Carlos Castillo Yahoo! Research Barcelona, Spain Debora Donato Yahoo! Research Barcelona, Spain chato@yahoo-inc.com debora@yahoo-inc.com Gilad Mishne Aristides Gionis Yahoo! Research Barcelona, Spain gionis@yahoo-inc.com 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 in 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 classi cation framework for combining the evidence from di €erent sources of information, that can be tuned automatically for a given social media type and quality de nition. In particular, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Finding high-quality content in social media

Association for Computing Machinery — Feb 11, 2008

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2008 by ACM Inc.
ISBN
978-1-59593-927-2
doi
10.1145/1341531.1341557
Publisher site
See Article on Publisher Site

Abstract

Finding High-Quality Content in Social Media Eugene Agichtein eugene@mathcs.emory.edu Emory University Atlanta, USA Carlos Castillo Yahoo! Research Barcelona, Spain Debora Donato Yahoo! Research Barcelona, Spain chato@yahoo-inc.com debora@yahoo-inc.com Gilad Mishne Aristides Gionis Yahoo! Research Barcelona, Spain gionis@yahoo-inc.com 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 in 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 classi cation framework for combining the evidence from di €erent sources of information, that can be tuned automatically for a given social media type and quality de nition. In particular,

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