Early prediction of the duration of protests using probabilistic Latent Dirichlet Allocation and Decision Trees

TL;DR

This paper presents a method combining probabilistic topic modeling and decision trees to accurately predict protest durations from textual descriptions, aiding security planning.

Contribution

It introduces a novel approach integrating LDA and decision trees for early protest duration prediction from free text descriptions.

Findings

Achieved nearly 90% prediction accuracy.

Effectively captured protest nuances through text analysis.

Demonstrated practical utility for security resource planning.

Abstract

Protests and agitations are an integral part of every democratic civil society. In recent years, South Africa has seen a large increase in its protests. The objective of this paper is to provide an early prediction of the duration of protests from its free flowing English text description. Free flowing descriptions of the protests help us in capturing its various nuances such as multiple causes, courses of actions etc. Next we use a combination of unsupervised learning (topic modeling) and supervised learning (decision trees) to predict the duration of the protests. Our results show a high degree (close to 90%) of accuracy in early prediction of the duration of protests.We expect the work to help police and other security services in planning and managing their resources in better handling protests in future.