Discovering trending phrases on information streams
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abstract
We study the problem of efficient discovery of trending phrases from high-volume text streams - be they sequences of Twitter messages, email messages, news articles, or other time-stamped text documents. Most existing approaches return top-k trending phrases. But, this approach neither guarantees that the top-k phrases returned are all trending, nor that all trending phrases are returned. In addition, the value of k is difficult to set and is indifferent to stream dynamics. Hence, we propose an approach that identifies all the trending phrases in a stream and is flexible to the changing stream properties. 2011 ACM.
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Proceedings of the 20th ACM international conference on Information and knowledge management