The objective of this project is to understand how people describe locations on social media during natural disasters. These data from social media are potentially beneficial in disaster response efforts, and to further this goal, computational algorithms are being developed to extract location information from social media postings. However, uneven use of social media by different populations and varying ways of describing places can complicate the process of identifying locations. This project advances knowledge by enhancing the understanding of the ways in which people describe geographic locations during natural disasters, the effectiveness of different algorithmic approaches for location extraction, and the potential spatial biases in the described locations. Such knowledge benefits society by informing future disaster response practices to help save lives and reduce inequality in response efforts. This project provides interdisciplinary research experience for undergraduates and graduates and will enhance academia and industry partnership. The datasets and algorithmic tools produced from this project will be publicly shared.
Social media platforms, such as Twitter, are increasingly being used by people impacted by natural disasters. Descriptions about the locations of victims and accidents are often contained in help-seeking messages posted on these platforms. However, a limited understanding exists of how locations are described on social media during natural disasters, which hinders their automatic extraction via computational tools. This project addresses three research questions: (1) What are the typical forms of location descriptions used by people on social media during natural disasters? (2) How effective are different geospatial artificial intelligence (GeoAI) approaches for extracting these location descriptions and representing them in geographic space? And (3) What spatial biases have characterized location descriptions on social media during natural disasters? The research team is collaborating with emergency management specialists to understand location descriptions on social media, examine multiple geo-knowledge-informed AI approaches for location extraction, and investigate the spatial biases of the extracted locations and their relation to vulnerable communities. The obtained knowledge about location descriptions and the developed methods can be applied to future disasters in diverse settings.
NSF award page: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117771