Observed behavior and perceived value of authors in usenet newsgroups: bridging the gap
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Slash(dot) and burn: distributed moderation in a large online conversation space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploring the community structure of newsgroups
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving marketing intelligence from online discussion
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
You Are Who You Talk To: Detecting Roles in Usenet Newsgroups
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
Improving the classification of newsgroup messages through social network analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Topic Detection in Online Discussion Using Non-negative Matrix Factorization
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Statistical analysis of the social network and discussion threads in slashdot
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Automatically assessing the post quality in online discussions on software
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Blog credibility ranking by exploiting verified content
Proceedings of the 3rd workshop on Information credibility on the web
Trust and Reputation Mining in Professional Virtual Communities
ICWE '9 Proceedings of the 9th International Conference on Web Engineering
Towards the measurement of Arabic Weblogs credibility automatically
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Web-based statistical fact checking of textual documents
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Towards quality discourse in online news comments
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Shopping for top forums: discovering online discussion for product research
Proceedings of the First Workshop on Social Media Analytics
ForAVis: explorative user forum analysis
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Predicting thread discourse structure over technical web forums
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Automatically measuring the quality of user generated content in forums
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Community insights: helping community leaders enhance the value of enterprise online communities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CommunityCompare: visually comparing communities for online community leaders in the enterprise
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Online discussions forums, known as forums for short, are conversational social cyberspaces constituting rich repositories of content and an important source of collaborative knowledge. However, most of this knowledge is buried inside the forum infrastructure and its extraction is both complex and difficult. The ability to automatically rate postings in online discussion forums, based on the value of their contribution, enhances the ability of users to find knowledge within this content. Several key online discussion forums have utilized collaborative intelligence to rate the value of postings made by users. However, a large percentage of posts go unattended and hence lack appropriate rating. In this paper, we focus on automatic rating of postings in online discussion forums. A set of features derived from the posting content and the threaded discussion structure are generated for each posting. These features are grouped into five categories, namely (i) relevance, (ii) originality, (iii) forum-specific features, (iv) surface features, and (v) posting-component features. Using a non-linear SVM classifier, the value of each posting is categorized into one of three levels High, Medium, or Low. This rating represents a seed value for each posting that is leveraged in filtering forum content. Experimental results have shown promising performance on forum data.