Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to topic detection and tracking
Topic detection and tracking
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Experiments in Microblog Summarization
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Fast locality-sensitive hashing
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
DASC '11 Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing
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A continuous rise in popularity of social media motivates many people to express their opinions and news on the real-time basis. In this paper, the social networking sites such as Twitter and Facebook are considered as a platform for event detection. Since social information streams are sparse and continuous, the processing time and speed are vital while detecting events. We suggest a novel approach of discovering events from multiple social streams using widely used Euclidean realization of locality sensitive hashing (LSH) algorithm. In our proposed method, the LSH is used twice in event detection. Firstly, it is used to obtain the events independently from both social streams. The cross-over events between social networks are detected by applying the algorithm one more time. The detected events can be trended to show their activeness on different networks. We explore a theoretical approach on the design of event detection and trending in multiple social sites.