Discovering trending phrases on information streams Conference Paper uri icon


  • 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.

author list (cited authors)

  • Kamath, K. Y., & Caverlee, J.

citation count

  • 4

editor list (cited editors)

  • Macdonald, C., Ounis, I., & Ruthven, I.

publication date

  • January 2011