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


Sprenkamp, Kilian; Zavolokina, Liudmila; Angst, Mario; Dolata, Mateusz (2023). Data-Driven Governance in Crises: Topic Modelling for the Identification of Refugee Needs. In: 24th Annual International Conference on Digital Government Research, Gdansk, Poland, 11 July 2023 - 14 July 2023. ACM Digital library, 1-11.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Human-Computer Interaction
Physical Sciences > Computer Networks and Communications
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Software
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:14 July 2023
Deposited On:19 Sep 2023 13:26
Last Modified:20 Jun 2024 10:19
Publisher:ACM Digital library
Series Name:The Proceedings of the 24th Annual International Conference on Digital Government Research (DGO2023)
ISBN:979-8-4007-0837-4
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/3598469.3598470
Other Identification Number:merlin-id:23901
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)