Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
ETree: Effective and Efficient Event Modeling for Real-Time Online Social Media Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Discovering emerging topics in unlabelled text collections
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
Event identification for local areas using social media streaming data
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
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Reports on major events like hurricanes and earthquakes, and major topics like the financial crisis or the Egyptian revolution appear in Internet news and become (ir)regularly updated, as new insights are acquired. Tracking emerging subtopics in a major or even local event is important for the news readers but challenging for the operator: subtopics may emerge gradually or in a bursty way; they may be of some importance inside the event, but too rare to be visible inside the whole stream of news. In this study, we propose a text stream clustering method that detects, tracks and updates large and small bursts of news in a two-level topic hierarchy. We report on our first results on a stream of news from February to April 2011.