The digital trail of Ukraine’s 2022 refugee exodus

Nathan Wycoff (), Lisa O. Singh (), Ali Arab (), Katharine M. Donato () and Helge Marahrens ()
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Nathan Wycoff: Georgetown University
Lisa O. Singh: Georgetown University
Ali Arab: Georgetown University
Katharine M. Donato: Georgetown University
Helge Marahrens: Georgetown University

Journal of Computational Social Science, 2024, vol. 7, issue 2, No 37, 2147-2193

Abstract: Abstract When the 2022 Russian full-scale invasion of Ukraine forced millions of people to leave their homes, officials worldwide scrambled to estimate the number of people who would seek refuge in their countries. There were a limited number of official tools in place to lean on to help determine this estimate. In this article, we investigate the possibility of using various publicly available organic (i.e. non-designed) data to predict forced movement from Ukraine early in the crisis. In particular, we establish Ukrainian-language insecurity and contextual indicators from multiple data sources, namely Google Trends, Twitter/X, local newspapers, the ACLED database, and the GDELT database. We compare the usefulness of these indicators in predicting forced migration into three neighboring countries: Poland, Slovakia, and Hungary. To minimize the challenge of temporal misalignment between the organic data and actual movement, we develop a lagging and aggregation framework. Findings reveal Google Trends variables are a robust leading indicator of observed forced migration for this conflict. While other indicators are less strong, they still capture shifts in forced migration flows, highlighting the potential for using publicly available organic data during emerging forced displacement crises.

Keywords: Humanitarian informatics; Forced migration; Forecasting; Internet data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-024-00304-4

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