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Abstract:
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Civil unrest is a complicated, multifaceted social phenomenon that is difficult to forecast. Relevant data for predicting future protests consist of a massive set of heterogeneous sources of data, primarily from social media. Using a modular approach to extract pertinent information from disparate sources of data, a spatiotemporal multiscale framework is developed to fuse predictions from algorithms mining social media. This novel multiscale spatiotemporal model is developed to satisfy three essential requirements: to handle massive spatiotemporal data sets, accommodate predictions of differing quality and uncertainty, and be flexible, allowing revisions to existing algorithms and the addition of new algorithms. The talk details the challenges that are posed by these three requirements, outlines the benefits of our novel multiscale spatiotemporal model relative to existing methods, and introduces a fully Bayesian algorithm for learning multiscale partitions for areal data.
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