Congratulations on Debayan Mandal's first publication, "Algorithmic Uncertainties in Geolocating Social Media Data for Disaster Management" co-authored with Dr. Lei Zou, Joynal Abedin, Bing Zhou, Mingzheng Yang, Binbin Lin, and Dr. Heng Cai. This work has been published in Cartography and Geographic Information Society and is now available online!
This paper addresses an important aspect of disaster management - the uncertainties related to conflagrating geolocating methods of social media data during disasters. It analyzed the margin of error in using geotags or user profile locations or message-mentioned addresses for geolocating social media data, specifically focusing on Twitter data during Hurricane Harvey in 2017. The findings highlight the varying degrees of accuracy at different geographical scales and offer insights into the accuracies for different aspects/uses in disaster management.
This research aims to quantify and visualize the algorithmic uncertainties of geolocating social media data using different methods and attributes and their impacts on analyzing social media for disaster management. Using Twitter data during the 2017 Hurricane Harvey in the United States as an example, we compared the agreements of associating tweets with locations by the “tweet about”, “user from,” and “tweet from” methods through three analyses. The first free 50 prints of the full paper are here: https://lnkd.in/gXveJ4ej.
Link to Paper:
Debayan Mandal, Lei Zou, Joynal Abedin, Bing Zhou, Mingzheng Yang, Binbin Lin & Heng Cai (2023) Algorithmic uncertainties in geolocating social media data for disaster management, Cartography and Geographic Information Science, DOI: 10.1080/15230406.2023.2286385
Congratulations! We look forward to hearing more achievements from GEAR Lab! Gig'em!
