People have been increasingly using social media to post messages during a natural disaster, and describe the locations of victims, damages, difficult situations, and relief resources. Many of these location descriptions are in the forms of detailed and multi-entity descriptions, such as door number addresses, road intersections, and highway exits. Currently, there is limited availability of datasets that contain these detailed location descriptions labeled in disaster-related messages. A lack of these datasets hinders the understanding of how people describe locations during disasters and the automatic extraction of these location descriptions. This paper fills this gap by providing a dataset that covers ten disasters in the United States and in five disaster types: hurricanes, floods, wildfires, tornados, and winter storms. The messages containing location descriptions are collected from the social media platform Twitter/X, and we describe the collection, labeling, and validation of this dataset. This dataset can be used for studying the ways people describe locations under disaster contexts and for training AI models to extract these important locations.
Sun et al. (Mon,) studied this question.