  Many methods have been proposed for detecting emerging events in text streams
using topic modeling. However, these methods have shortcomings that make them
unsuitable for rapid detection of locally emerging events on massive text
streams. We describe Spatially Compact Semantic Scan (SCSS) that has been
developed specifically to overcome the shortcomings of current methods in
detecting new spatially compact events in text streams. SCSS employs
alternating optimization between using semantic scan to estimate contrastive
foreground topics in documents, and discovering spatial neighborhoods with high
occurrence of documents containing the foreground topics. We evaluate our
method on Emergency Department chief complaints dataset (ED dataset) to verify
the effectiveness of our method in detecting real-world disease outbreaks from
free-text ED chief complaint data.
