Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
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
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discovering groups of people in Google news
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeking stable clusters in the blogosphere
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Combating spam in tagging systems: An evaluation
ACM Transactions on the Web (TWEB)
User generated content: how good is it?
Proceedings of the 3rd workshop on Information credibility on the web
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Tweet the debates: understanding community annotation of uncollected sources
WSM '09 Proceedings of the first SIGMM workshop on Social media
Improved search for socially annotated data
Proceedings of the VLDB Endowment
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Situation detection and control using spatio-temporal analysis of microblogs
Proceedings of the 19th international conference on World wide web
The topic-perspective model for social tagging systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
PET: a statistical model for popular events tracking in social communities
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Growing a tree in the forest: constructing folksonomies by integrating structured metadata
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Social pixels: genesis and evaluation
Proceedings of the international conference on Multimedia
Evolutionary taxonomy construction from dynamic tag space
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Questions about questions: an empirical analysis of information needs on Twitter
Proceedings of the 22nd international conference on World Wide Web
Creation and growth of online social network
World Wide Web
Spatio-temporal characteristics of bursty words in Twitter streams
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A time decoupling approach for studying forum dynamics
World Wide Web
Extracting news blog hot topics based on the W2T Methodology
World Wide Web
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Collaborative tagging have emerged as a ubiquitous way to annotate and organize online resources. As a kind of descriptive keyword, large amount of tags are created and associated to multiple types of resources, e.g., web pages, photos, videos and tweets. Users' tagging actions over time reflect their changing interests. Monitoring and analyzing the temporal patterns of tags can provide important insights to trace hot topics on the web. Existing work focuses on deriving temporal patterns for individual tags. However, there exist remarkable correlations among tags assigned to online resources. In this paper, we propose a new approach to detect bursty tagging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. This kind of bursty tagging event generally corresponds to a real life event. It profiles the events with more representative and comprehensible clues. The proposed approach is divided into three stages. We exploit the sliding time intervals to extract bursty features as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. We discuss the choice of similarity and granularity for event detection. After that, we further utilize an automatically generated tag taxonomy to organize bursty events to facilitate the burst oriented navigation and analysis. The experimental study on a large real data set demonstrates the superiority of our new approach.