Integrating Traditional and Social Media Data to Predict Bilateral Migrant Stocks in the European Union

Dilek Yildiz; Arkadiusz Wisniowski; Guy J. Abel; Ingmar Weber; Emilio Zagheni; Cloé Gendronneau; Stijn Hoorens

doi:10.1177/01979183241249969

Integrating Traditional and Social Media Data to Predict Bilateral Migrant Stocks in the European Union

Dilek Yildiz
, Arkadiusz Wisniowski^*
, Guy J. Abel^*
, Ingmar Weber
, Emilio Zagheni
, Cloé Gendronneau
, Stijn Hoorens

^*Corresponding author for this work

Social Statistics

Research output: Contribution to journal › Article › peer-review

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Abstract

Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.

Original language	English
Pages (from-to)	90-118
Journal	International Migration Review
Volume	59
Issue number	1
Early online date	29 May 2024
DOIs	https://doi.org/10.1177/01979183241249969
Publication status	Published - 29 May 2024

Keywords

European Union
social media data
bilateral migrant stocks

Research Beacons, Institutes and Platforms

Cathie Marsh Institute

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1177/01979183241249969Licence: CC BY

yildiz-et-al-2024-integrating-traditional-and-social-media-data-to-predict-bilateral-migrant-stocks-in-the-europeanFinal published version, 2.52 MBLicence: CC BY

3 Finished

Estimating within EU migration flows with new temporal definitions using big data
Wisniowski, A. (PI)
1/12/19 → 30/11/22
Project: Research
Statistical Modelling
Onah, C. N. (PI), Shlomo, N. (PI), Chandola, T. (PI), Schoch, D. (PI), Cuitún Coronado, J. (PI), Aparicio-Castro, A. (PI), Thestrup, S. (PI), Olsen, W. K. (PI), Cernat, A. (PI), Shryane, N. (PI), Wisniowski, A. (PI), Smith, D. (PI), Aparicio-Castro, A. (PI), Shafie, T. (PI), Hannemann, T. (PI), Morales-Gómez, A. (PI), Mellon, J. (PI), Troncoso Ruiz, P. (PI), Taub, J. (PI), Murphy, J. (PI), Guest, E. (PI), Kim, J. (PI), Watson, J. (PI), Pashazadeh, F. (PI), Hoór, D. (PI), Jones, P. (PI) & Gosling, Z. (Support team)
1/08/19 → 30/09/22
Project: Research
Measuring Labour Mobility and migration Using Big Data Data
Wisniowski, A. (PI)
2/02/18 → 31/12/18
Project: Other

1 Commissioned report

Measuring Labour Mobility and Migration Using Big Data: Exploring the potential of social-media data for measuring EU mobility flows and stocks of EU movers
Gendronneau, C., Wiśniowski, A., Yildiz, D., Zagheni, E., Fiorio, L., Hsiao, Y., Stepanek, M., Weber, I., Abel, G. & Hoorens, S., 2019, Publications Office of the European Union. 121 p.
Research output: Book/Report › Commissioned report › peer-review

Open Access
File

4 Invited talk

Usage of traditional and social media data to assess migration and mobility patterns
Wisniowski, A. (Speaker)
17 Sept 2025
Activity: Talk or presentation › Invited talk › Research
Measuring and forecasting migration with traditional and novel data
Wisniowski, A. (Speaker)
2 Sept 2025
Activity: Talk or presentation › Invited talk › Research
Estimation and forecasting of migration with traditional and novel data sources
Wisniowski, A. (Speaker)
28 Aug 2025
Activity: Talk or presentation › Invited talk › Research

Predicting EU migration trends: merging traditional and social media data
Yildiz, D., Wisniowski, A., Abel, G. J., Weber, I., Zagheni, E., Gendronneau, C. & Hoorens, S.
24/06/24
1 item of Media coverage
Press/Media: Blogs and social media

Cite this

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abstract = "Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.",

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AU - Yildiz, Dilek

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AU - Abel, Guy J.

AU - Weber, Ingmar

AU - Zagheni, Emilio

AU - Gendronneau, Cloé

AU - Hoorens, Stijn

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Integrating Traditional and Social Media Data to Predict Bilateral Migrant Stocks in the European Union

Abstract

Keywords

Research Beacons, Institutes and Platforms

UN SDGs

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Cite this