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Many methods have been proposed for detecting emerging events in text streamsusing topic modeling. However, these methods have shortcomings that make themunsuitable for rapid detection of locally emerging events on massive textstreams. We describe Spatially Compact Semantic Scan (SCSS) that has beendeveloped specifically to overcome the shortcomings of current methods indetecting new spatially compact events in text streams. SCSS employsalternating optimization between using semantic scan to estimate contrastiveforeground topics in documents, and discovering spatial neighborhoods with highoccurrence of documents containing the foreground topics. We evaluate ourmethod on Emergency Department chief complaints dataset (ED dataset) to verifythe effectiveness of our method in detecting real-world disease outbreaks fromfree-text ED chief complaint data.

Spatial Semantic Scan: Jointly Detecting Subtle Events and their Spatial Footprint