Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking dynamics of topic trends using a finite mixture model
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mapping Medline Papers, Genes, and Proteins Related to Melanoma Research
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Complexity - Understanding Complex Systems: Part II
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Expanding the taxonomies of bibliographic archives with persistent long-term themes
Proceedings of the 2006 ACM symposium on Applied computing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mapping Scientific Disciplines and Author Expertise Based on Personal Bibliography Files
IV '06 Proceedings of the conference on Information Visualization
Visualizing evolution and impact of biomedical fields
Journal of Biomedical Informatics
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning the funding momentum of research projects
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Pattern change discovery between high dimensional data sets
Proceedings of the 20th ACM international conference on Information and knowledge management
Discovering global and local bursts in a stream of news
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Evolution of Author's Topic in Authorship Network
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Towards Topic Trend Prediction on a Topic Evolution Model with Social Connection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Whoo.ly: facilitating information seeking for hyperlocal communities using social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Timeline adaptation for text classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Spatio-temporal characteristics of bursty words in Twitter streams
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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For some time there has been increasing interest in the problem of monitoring the occurrence of topics in a stream of events, such as a stream of news articles. This has led to different models of bursts in these streams, i.e., periods of elevated occurrence of events. Today there are several burst definitions and detection algorithms, and their differences can produce very different results in topic streams. These definitions also share a fundamental problem: they define bursts in terms of an arrival rate. This approach is limiting; other stream dimensions can matter. We reconsider the idea of bursts from the standpoint of a simple kind of physics. Instead of focusing on arrival rates, we reconstruct bursts as a dynamic phenomenon, using kinetics concepts from physics -- mass and velocity -- and derive momentum, acceleration, and force from these. We refer to the result as topic dynamics, permitting a hierarchical, expressive model of bursts as intervals of increasing momentum. As a sample application, we present a topic dynamics model for the large PubMed/MEDLINE database of biomedical publications, using the MeSH (Medical Subject Heading) topic hierarchy. We show our model is able to detect bursts for MeSH terms accurately as well as efficiently.