  The combination of large open data sources with machine learning approaches
presents a potentially powerful way to predict events such as protest or social
unrest. However, accounting for uncertainty in such models, particularly when
using diverse, unstructured datasets such as social media, is essential to
guarantee the appropriate use of such methods. Here we develop a Bayesian
method for predicting social unrest events in Australia using social media
data. This method uses machine learning methods to classify individual postings
to social media as being relevant, and an empirical Bayesian approach to
calculate posterior event probabilities. We use the method to predict events in
Australian cities over a period in 2017/18.
