Accelerating Crisis Response: Automated Image Classification for Geolocating Social Media Content

PROCEEDINGS OF THE 2023 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, ASONAM 2023(2023)

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Abstract
In the immediate aftermath of natural or man-made disasters, social media plays an essential role in assessing the impact of the event. The images from social media demonstrated the potential to accelerate the response to a crisis. However, finding the exact location of relevant social media images remains a problem for both humans and computer systems. This study presents an automated image classifier aimed at accelerating crowdsourced geolocation. The classifier is trained with data annotated by crisis risk experts and predicts the difficulty in geolocating a photo. The experimental results demonstrate that the proposed approach can predict the geolocating difficulty, thus potentially speed up the geolocation process by presenting volunteers images that are easy to geolocate.
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Key words
Social media,geolocating,disaster response
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