Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sifting micro-blogging stream for events of user interest
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A spatio-temporal framework for related topic search in micro-blogging
AMT'10 Proceedings of the 6th international conference on Active media technology
Dynamic relationship and event discovery
Proceedings of the fourth ACM international conference on Web search and data mining
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Over the last few years, Twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. In this paper, we propose a dynamic topic association detection model to discover relations between Twitter topics, by which users can gain insights into richer information about topics of interest. The proposed model utilizes a time constrained method to extract event-based spatio-temporal topic association, and constructs a dynamic temporal map to represent the obtained result. Experimental results show the improvement of the proposed model compared to static spatio-temporal method and co-occurrence method.