A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Combining Topic Models and Social Networks for Chat Data Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Social Computing and Weighting to Identify Member Roles in Online Communities
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
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
Automatic online news topic ranking using media focus and user attention based on aging theory
Proceedings of the 17th ACM conference on Information and knowledge management
Topic Detection and Tracking for Threaded Discussion Communities
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
BBS based hot topic retrieval using back-propagation neural network
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Semi-automatic hot event detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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BBS(Bulletin Board Systems) is one of the most common places for threaded discussion. It becomes more and more popular among web users, especially in China. Everyday a huge amount of new discussions are generated on BBS. It is too difficult to find hot topics. To solve this issue, we propose a novel approach to detect hot topics on BBS for any period of time. Our solution consists of three steps. First of all, candidate topics are extracted using the clustering method. Secondly, based on the extracted topics, aging theory is employed to valuate the hotness of topics. Both two steps above are carried out incrementally over time. Finally, topics are ranked and hot topics are detected. Experiments performed on practical BBS data show that our method is quite effective.