How do people describe locations during a natural disaster: an analysis of tweets from Hurricane Harvey
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Abstract (arXiv)
Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Sep 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 2.
6 papers.
- 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
- 2024Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IANcited by 23
- 2022Location reference recognition from texts: A survey and comparisoncited by 17
- 2025OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligencecited by 7
- 2025Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approachcited by 7
- 2023Exploring the law of text geographic informationcited by 0
- 2025Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach23 Dec 2025
- 2025OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence20 Mar 2025
- 2024Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN13 Aug 2024
- 2023Exploring the law of text geographic information1 Sep 2023
- 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence13 Apr 2023
- 2022Location reference recognition from texts: A survey and comparison4 Jul 2022