Discovering global and local bursts in a stream of news.

Reports on major events like hurricanes and earthquakes, and major topics like the financial crisis or the Egyptian revolution appear in Internet news and become (ir)regularly updated, as new insights are acquired. Tracking emerging subtopics in a major or even local event is important for the news readers but challenging for the operator: subtopics may emerge gradually or in a bursty way; they may be of some importance inside the event, but too rare to be visible inside the whole stream of news. In this study, we propose a text stream clustering method that detects, tracks and updates large and small bursts of news in a two-level topic hierarchy. We report on our first results on a stream of news from February to April 2011.