{'fraction_non_alphanumeric': 0.013136288998357963, 'fraction_numerical': 0.0, 'mean_word_length': 6.128654970760234, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spatiotemporal model fusion: multiscale modelling of civil unrest

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, we develop a spatiotemporal multiscale framework to fuse predictions from algorithms mining social media. This novel multiscale spatiotemporal model is developed to satisfy four essential requirements: be scalable to handle massive spatiotemporal data sets, incorporate hierarchical predictions, accommodate predictions of differing quality and uncertainty, and be flexible, allowing revisions to existing algorithms and the addition of new algorithms. The paper details the challenges that are posed by these four requirements and outlines the benefits of our novel multiscale spatiotemporal model relative to existing methods. In particular, our multiscale approach coupled with an efficient sequential Monte Carlo framework enables scalable rapid computation of richly specified Bayesian hierarchical models for spatiotemporal data.