Computer Vision
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
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
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
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
On burstiness-aware search for document sequences
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Event detection from flickr data through wavelet-based spatial analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying, attributing and describing spatial bursts
Proceedings of the VLDB Endowment
Proceedings of the 20th ACM international conference on Information and knowledge management
Bursty event detection from collaborative tags
World Wide Web
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
On the spatiotemporal burstiness of terms
Proceedings of the VLDB Endowment
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
Socioscope: spatio-temporal signal recovery from social media
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Latent geographic feature extraction from social media
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
EventRadar: A Real-Time Local Event Detection Scheme Using Twitter Stream
GREENCOM '12 Proceedings of the 2012 IEEE International Conference on Green Computing and Communications
Reliable spatio-temporal signal extraction and exploration from human activity records
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged. In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.