Brennan, Michael Robert, Stacey Wrazien, and Rachel Greenstadt. "Learning to Extract Quality Discourse in Online Communities." Collaboratively-Built Knowledge Sources and AI. 2010.

This paper discusses collaborative filtering systems in online communities, which aim to manage information overload and improve discourse by allowing users to rank content based on validity or usefulness. However, these systems have known weaknesses, such as dependency on a large crowd for ratings and concerns about representativeness. The study proposes using statistical machine learning to predict community ratings, extracting features from metadata and linguistic content to identify predictors of community ratings. Using Slashdot as an example, the study finds that author reputation, use of pronouns, and author sentiment are significant predictors, achieving 76% accuracy in predicting community ratings.