Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Learning to Perform Moderation in Online Forums
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Slash(dot) and burn: distributed moderation in a large online conversation space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Context-sensitive text mining and belief revision for intelligent information retrieval on the web
Web Intelligence and Agent Systems
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Web Intelligence and Agent Systems
Follow the reader: filtering comments on slashdot
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Relaxed online SVMs for spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Social Information Processing in News Aggregation
IEEE Internet Computing
Proceedings of the 2007 international ACM conference on Supporting group work
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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
Comments-oriented document summarization: understanding documents with readers' feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
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Large-scale socially-generated metadata --like user-contributed tags, comments, and ratings --is one of the key features driving the growth and success of the emerging Social Web. While tags and ratings provide succinct metadata about Social Web content e.g., a tag is often a single keyword, user-contributed comments offer the promise of a rich source of contextual information about Social Web content but in a potentially “messier” form, considering the wide variability in quality, style, and substance of comments generated by a legion of Social Web participants. In this paper, we study how an online community perceives the relative quality of its own user-contributed comments, which has important implications for the successful self-regulation and growth of the Social Web in the presence of increasing spam and a flood of Social Web metadata. Concretely, we propose and evaluate a machine learning-based approach for ranking comments on the Social Web based on the community's expressed preferences, which can be used to promote high-quality comments and filter out low-quality comments. We study several factors impacting community preference, including the contributor's reputation and community activity level, as well as the complexity and richness of the comment. Through experiments over three social news platforms Digg, Reddit, and the New York Times, we find that the proposed approach results in significant improvement in ranking quality versus alternative approaches.