On the spatiotemporal burstiness of terms

Abstract

Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification . Given a term t , a burst is generally exhibited when an unusually high frequency is observed for t . While spatial and temporal burstiness have been studied individually in the past, our work is the first to simultaneously track and measure spatiotemporal term burstiness . In addition, we use the mined burstiness information toward an efficient document-search engine: given a user's query of terms, our engine returns a ranked list of documents discussing influential events with a strong spatiotemporal impact. We demonstrate the efficiency of our methods with an extensive experimental evaluation on real and synthetic datasets.