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
Data Mining and Knowledge Discovery
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Complex Time-Series Data by Learning Markovian Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining common topics from multiple asynchronous text streams
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Early online identification of attention gathering items in social media
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Context modeling for ranking and tagging bursty features in text streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining named entities with temporally correlated bursts from multilingual web news streams
Proceedings of the fourth ACM international conference on Web search and data mining
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Structural trend analysis for online social networks
Proceedings of the VLDB Endowment
Classifying trending topics: a typology of conversation triggers on Twitter
Proceedings of the 20th ACM international conference on Information and knowledge management
From chatter to headlines: harnessing the real-time web for personalized news recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Correlating financial time series with micro-blogging activity
Proceedings of the fifth ACM international conference on Web search and data mining
Twitter catches the flu: detecting influenza epidemics using Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
EventSearch: a system for event discovery and retrieval on multi-type historical data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments on a large Twitter dataset shows our methods are very effective.