2 research outputs found
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