Improving Social Media Geolocation for Disaster Response by Using Text From Images and ChatGPT

Abstract

Social media can serve as a valuable source of timely and valuable information about the impacts on people and infrastructure during a disaster. However, due to the lack of geographical data in most social media posts, this information is often underutilized by first responders. This paper proposes and analyses an approach that combines text from social media posts with textual information extracted from images to improve the geolocation of social media data during a given disaster. The implementation incorporates ChatGPT in location prediction. We use real-world dataset from Twitter that represent four different events, including floods and earthquake. The experimental results demonstrate that our proposal improves the location prediction’s quantity and precision. We expect that our findings help policymakers consider the application of the proposed methodology in disaster response.

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