# Low-supervision urgency detection and transfer in short crisis messages

## TL;DR

This paper introduces a robust, low-supervision system for detecting urgent messages in social media during crises, capable of adapting to new disasters through transfer learning and leveraging both labeled and unlabeled data.

## Contribution

It presents a novel ensemble-based approach that effectively handles data scarcity and disaster variability, with a simple transfer learning method for new crises.

## Key findings

- Outperforms baseline models with high statistical significance.
- Effectively leverages unlabeled data in disaster scenarios.
- Demonstrates adaptability to various types of crises.

## Abstract

Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis. Technology can be used to best mobilize resources such as food and water in the event of a natural disaster, by semi-automatically flagging tweets and short messages as indicating an urgent need. The problem is challenging not just because of the sparseness of data in the immediate aftermath of a disaster, but because of the varying characteristics of disasters in developing countries (making it difficult to train just one system) and the noise and quirks in social media. In this paper, we present a robust, low-supervision social media urgency system that adapts to arbitrary crises by leveraging both labeled and unlabeled data in an ensemble setting. The system is also able to adapt to new crises where an unlabeled background corpus may not be available yet by utilizing a simple and effective transfer learning methodology. Experimentally, our transfer learning and low-supervision approaches are found to outperform viable baselines with high significance on myriad disaster datasets.