Slash(dot) and burn: distributed moderation in a large online conversation space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Wisdom of Crowds
The influence of interaction attributes on trust in virtual communities
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Handling subjective user feedback for reputation computation in virtual reality
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Text classification for assisting moderators in online health communities
Journal of Biomedical Informatics
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Collaborative filtering systems have been developed to manage information overload in online communities. In these systems, users rank content provided by other users on the validity or usefulness within their particular context. Slash-dot is an example of such a community where peers rate each others' comments based on their relevance to the post. This work extracts a wide variety of features from the Slashdot metadata and posts' linguistic contents to identify features that can predict the community rating. We find that author reputation, use of pronouns, and author sentiment are salient. We achieve 76% accuracy at predicting the community's rating of the post as good, neutral, or bad.