Pachinko Prediction: A Bayesian method for event prediction from social media data

Jonathan Tuke, Andrew Nguyen, Mehwish Nasim, Drew Mellor, Asanga Wickramasinghe, Nigel Bean, Lewis Mitchell

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number102147
JournalInformation Processing and Management
Volume57
Issue number2
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes

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