Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling and managing changes in text databases
ACM Transactions on Database Systems (TODS)
Reducing the Plagiarism Detection Search Space on the Basis of the Kullback-Leibler Distance
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Connecting the dots between news articles
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Social life networks: a multimedia problem?
Proceedings of the 21st ACM international conference on Multimedia
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Nowadays social media are widely used for the broadcasting of different types of information, such as events, activities and opinions. Analyzing this vast amount of data for extracting models that describe individual users or groups of users has gained a lot of attention lately. In this work we analyze individual users and monitor changes in their published content over time. We propose a topic-based user profiling and monitoring approach for change detection and monitoring of profile evolution. Our method is capable of detecting persistent topics representing long term interests of the user as well as short term topics that refer to everyday events or reactions to the news. We evaluate our approach on real data from Twitter.