A proof-of-concept and feasibility analysis of using social sensors in the context of causal machine learning-based emergency management

The goals of emergency management are to restore human safety and security, and to help the authorities understand what causes such events. It requires information that is both highly accurate, and can be generated very quickly. This research addresses these concerns with a machine learning model based on cause-and-effect using a Bayesian belief network. This employs human critical thinking and amplified context to encode the model structures, which contribute towards its imitation of human-intelligent understanding, and the model parameters are fitted using social media data. The results show that our model is a natural fit for a real-world environment required emergency management.