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In this paper we tackle the recently proposed problem of hidden streams. In many situations, the data stream that we are interested in, is not directly accessible. Instead, part of the data can be accessed only through applying filters (e.g. keyword filtering). In fact this is the case of the most discussed social
document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a
Spatial event forecasting from social media is potentially extremely useful but suffers from critical challenges, such as the dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches
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