Pachinko Prediction: A Bayesian method for event prediction from social media data
This addresses the need for uncertainty-aware event prediction from unstructured social media data, but it is incremental as it applies an existing Bayesian approach to a specific domain.
The authors tackled the problem of predicting social unrest events by developing a Bayesian method that uses social media data, achieving predictions for events in Australian cities during 2017/18.
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.