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.
( @cite_19 ) propose a hierarchical LDA model consisting of a set of pure topics which suffer variations with region to form regional topics that finally generate the documents. The model assumes a fixed number of regions, and that pure topics exist in the form of regional variants in every region. A region is modeled using a bivariate Gaussian distribution which assumes that each region has a center where its regional topics are concentrated and the effect of the regional topics decays away from this center. While consideriing a bivariate Gaussian distribution for modeling the location of documents in SCSS is possible, it implies that the effect of a topic decays away from the epicenter. This might not be true in the case of steady state of a disease outbreak where all documents in the affected spatial neighborhood may be equally likely to contain the emerging topic.
@cite_19
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The rapid growth of geotagged social media raises new computational possibilities for investigating geographic linguistic variation. In this paper, we present a multi-level generative model that reasons jointly about latent topics and geographical regions. High-level topics such as "sports" or "entertainment" are rendered differently in each geographic region, revealing topic-specific regional distinctions. Applied to a new dataset of geotagged microblogs, our model recovers coherent topics and their regional variants, while identifying geographic areas of linguistic consistency. The model also enables prediction of an author's geographic location from raw text, outperforming both text regression and supervised topic models.