Low-supervision urgency detection and transfer in short crisis messages
This work addresses the problem of resource mobilization in humanitarian disasters for aid organizations by improving urgency detection in social media, though it appears incremental as it builds on existing low-supervision and transfer learning methods.
The paper tackles the challenge of automatically detecting urgent needs in short crisis messages from social media, especially in diverse disaster scenarios with limited labeled data, and presents a system that outperforms baselines with high significance across multiple disaster datasets.
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.