Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Modeling spread of ideas in online social networks
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Proceedings of the VLDB Endowment
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering of time series data-a survey
Pattern Recognition
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
How Does Research Evolve? Pattern Mining for Research Meme Cycles
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Socially motivated multimedia topic timeline summarization
Proceedings of the 2nd international workshop on Socially-aware multimedia
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The Social Web makes visible the ebb and flow of popular interest in topics both newsworthy ("GulfSpill") and trivial ("Lolcat"). Understanding this emergent behavior is a fundamental goal for Social Web research. Key problems include discovering emergent topics from online text sources, modeling burst activity, and predicting the future trajectory of a given topic. Past work has addressed such problems individually for specific applications, but has lacked a generalizable framework for performing both classification and prediction of topic usage. Our approach is to model a topic as a temporally ordered sequence of derived feature states and capture characteristic changes in the topic trend. These sequences are drawn from a dynamic segmentation of frequency data based on change point analysis. We employ Partitioning Around Medoids clustering on these segments to produce signatures which highlight characteristic patterns of usage growth and decay. We demonstrate how this signature model can be used to define distinctive classes of topics in multiple online contexts, including tagging systems and web-based information retrieval. Additionally, we show how the model can predict the general trajectory of interest in a particular topic.