Learning Early Detection of Emergencies from Word Usage Patterns on Social Media

In the early stages of an emergency, information extracted from social media can support crisis response with evidence-based content. In order to capture this evidence, the events of interest must be first promptly detected. An automated detection system is able to activate other tasks, such as preemptive data processing for extracting eventrelated information. In this paper, we extend the human-in-the-loop approach in our previous work, TriggerCit, with a machine-learning-based event detection system trained on word count time series and coupled with an automated lexicon building algorithm.We design this framework in a language-agnostic fashion. In this way, the system can be deployed to any language without substantial effort. We evaluate the capacity of the proposed work against authoritative flood data for Nepal recorded over two years.