A classification-based review recommender
Knowledge-Based Systems
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
On the relationship between novelty and popularity of user-generated content
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Social manipulation of online recommender systems
SocInfo'10 Proceedings of the Second international conference on Social informatics
Using readability tests to predict helpful product reviews
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Learning from YouTube: an analysis of information literacy in user discourse
Proceedings of the 2011 iConference
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
Hierarchical comments-based clustering
Proceedings of the 2011 ACM Symposium on Applied Computing
Predicting discussions on the social semantic web
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Moderation of Online Discussion Sites
International Journal of Electronic Commerce
We don't need no stinkin' badges: examining the social role of badges in the Huffington Post
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Semi-supervised correction of biased comment ratings
Proceedings of the 21st international conference on World Wide Web
Multi-objective ranking of comments on web
Proceedings of the 21st international conference on World Wide Web
Care to comment?: recommendations for commenting on news stories
Proceedings of the 21st international conference on World Wide Web
Information Retrieval in the Commentsphere
ACM Transactions on Intelligent Systems and Technology (TIST)
On the Relationship between Novelty and Popularity of User-Generated Content
ACM Transactions on Intelligent Systems and Technology (TIST)
Robust detection of comment spam using entropy rate
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Topic-driven reader comments summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
A decentralized recommender system for effective web credibility assessment
Proceedings of the 21st ACM international conference on Information and knowledge management
Can social features help learning to rank youtube videos?
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Ranking News Articles Based on Popularity Prediction
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Web credibility: features exploration and credibility prediction
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Defending imitating attacks in web credibility evaluation systems
Proceedings of the 22nd international conference on World Wide Web companion
On the subjectivity and bias of web content credibility evaluations
Proceedings of the 22nd international conference on World Wide Web companion
How do we deep-link?: leveraging user-contributed time-links for non-linear video access
Proceedings of the 21st ACM international conference on Multimedia
Topic extraction from online reviews for classification and recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
We study how an online community perceives the relative quality of its own user-contributed content, 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. 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, we find that the proposed approach results in significant improvement in ranking quality versus alternative approaches.