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

arXiv: https://arxiv.org/abs/1809.08427 · HTML: https://mlsanity.com/paper/1809.08427/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Detection of financial opportunities in micro-blogging data with a stacked classification system](https://mlsanity.com/paper/2404.07224/) (2024)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Personality Profiling: How informative are social media profiles in predicting personal information?](https://mlsanity.com/paper/2309.13065/) (2023)
- [Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages](https://mlsanity.com/paper/2404.08665/) (2024)

## Abstract (arXiv)

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.