A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Topic Extraction from News Archive Using TF*PDF Algorithm
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A System for new event detection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 2nd ACM workshop on Social web search and mining
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Learning to explore spatio-temporal impacts for event evaluation on social media
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
On using inter-document relations in microblog retrieval
Proceedings of the 22nd international conference on World Wide Web companion
Exploiting online social data in ontology learning for event tracking and emergency response
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Journal of Information Science
Learning to create an extensible event ontology model from social-media streams
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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One of the basic human needs is to exchange information and socialize with each other. Online microblogging services such as Twitter allow users to post very short messages related to everything ranging from mundane daily life routines to breaking news events. A key challenging issue of mining such social messages is how to analyze the real-time distributed messages and extract significant features of them in a dynamic environment. In this work, we propose a novel term weighting method, called BursT, using sliding window techniques for weighting message streams. The experimental results show that our weighting technique has an outstanding performance to reflect the shifts of concept drift. The result of this work can be extended to perform a periodic feature extraction, and also be able to integrate other sophisticated clustering methods to enhance the efficiency for real-time event mining in social networks.