Early prediction of the duration of protests using probabilistic Latent Dirichlet Allocation and Decision Trees
This work helps police and security services in South Africa plan resources for protest management, though it is incremental in applying existing methods to a new domain.
The paper tackles the problem of predicting protest duration from free-text descriptions using topic modeling and decision trees, achieving close to 90% accuracy in early prediction.
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.