Pachinko Prediction: A Bayesian method for event prediction from social media data. Issue 2 (March 2020)

• Record Type: Journal Article
• Authors: Tuke, Jonathan; Nguyen, Andrew; Nasim, Mehwish; Mellor, Drew; Wickramasinghe, Asanga; Bean, Nigel; Mitchell, Lewis
• Is Part Of: Information processing & management. Volume 57:Issue 2(2020:Mar.)
• Publication Date: 2020-03
• Subjects: Bayesian statistics -- Social unrest -- Machine learning -- Prediction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.102147
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: An empirical Bayesian approach for making probabilistic predictions of events such as social unrest from online social network data. A framework for predictive modelling from social networks by combining machine learning tools. Analysis of datasets relating to social unrest in Australia during 2017/18, including a dataset used to classify protest-relevant tweets included with the paper. 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.