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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Data-Driven Governance in Crises: Topic Modelling for the Identification of Refugee Needs |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 979-8-4007-0837-4 |
| Page Range | 1 - 11 |
| Event Title | 24th Annual International Conference on Digital Government Research |
| Event Type | conference |
| Event Location | Gdansk, Poland |
| Event Start Date | July 11 - 2023 |
| Event End Date | July 14 - 2023 |
| Series Name | The Proceedings of the 24th Annual International Conference on Digital Government Research (DGO2023) |
| Place of Publication | New York, NY, USA |
| Publisher | ACM Digital library |
| Abstract Text | The war in Ukraine and the following refugee crisis have recently again highlighted the need for effective refugee management across European countries. Refugee management contemporarily mostly relies on top-down management approaches by governments. These often lead to suboptimal policies for refugees and highlight a need to better identify and integrate refugee needs into management. Here, we show that modern applications of Natural Language Processing (NLP) allow for the effective analysis of large text corpora linked to refugee needs, making it possible to complement top-down approaches with bottom-up knowledge centered around the current needs of the refugee population. By following a Design Science Research Methodology, we utilize 58 semi-structured stakeholder interviews within Switzerland to develop design requirements for NLP applications for refugee management. Based on the design requirements, we developed R2G – “Refugees to Government”, an application based on state-of-the-art topic modeling to identify refugee needs bottom-up through Telegram data. We evaluate R2G with a dedicated workshop held with stakeholders from the public sector and civil society. Thus, we contribute to the ongoing discourse on how to design refugee management applications and showcase how topic modeling can be utilized for data-driven governance during refugee crises. |
| Free access at | DOI |
| Digital Object Identifier | 10.1145/3598469.3598470 |
| Other Identification Number | merlin-id:23901 |
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