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Data-Driven Governance in Crises: Topic Modelling for the Identification of Refugee Needs


๐Ÿ“ฃ Our latest research, "Data-Driven Governance in Crises: Topic Modelling for the Identification of Refugee Needs," has been accepted and soon to be presented at the 24th Annual International Conference on Digital Government Research!


๐ŸŒ In this paper, we tackle the ongoing challenge of effective refugee management, particularly highlighted by the recent refugee crisis resulting from the war in Ukraine. Traditional top-down approaches, while important, often fall short in fully addressing refugee needs.

How can we better integrate these needs into management processes?



๐Ÿ‘ฅ Born out of the DIZH "Government as a Platform" project, Kilian Sprenkamp, Mario Angst, Mateusz Dolata and I have examined how Natural Language Processing (NLP) can enhance refugee management.


๐Ÿ“Š We've developed R2G - "Refugees to Government," an innovative application that employs topic modelling to better understand refugee needs using Telegram data. This is a significant stride towards a more inclusive, data-driven approach to refugee management.


Check out our paper for more insights! Your thoughts and feedback are most welcome! ๐Ÿ˜Š


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