Bursty event detection from text streams for disaster management

In this paper, an approach to automatically identifying bursty events from multiple text streams is presented. We investigate the characteristics of bursty terms that appear in the documents generated from text streams, and incorporate those characteristics into a term weighting scheme that distinguishes bursty terms from other non-bursty terms. Experimental results based on the news corpus show that our approach outperforms the existing alternatives in extracting bursty terms from multiple text streams. The proposed research is expected to contribute to increasing the situational awareness of ongoing events particularly when a natural or economic disaster occurs. Copyright is held by the International World Wide Web Conference Committee (IW3C2).