IDEAS home Printed from https://ideas.repec.org/p/zbw/esprep/251215.html
   My bibliography  Save this paper

Predicting refugee flows from Ukraine with an approach to Big (Crisis) Data: a new opportunity for refugee and humanitarian studies

Author

Listed:
  • Jurić, Tado

Abstract

Background: This paper shows that Big Data and the so-called tools of digital demography, such as Google Trends (GT) and insights from social networks such as Instagram, Twitter and Facebook, can be useful for determining, estimating, and predicting the forced migration flows to the EU caused by the war in Ukraine. Objective: The objective of this study was to test the usefulness of Google Trends indexes to predict further forced migration from Ukraine to the EU (mainly to Germany) and gain demographic insights from social networks into the age and gender structure of refugees. Methods: The primary methodological concept of our approach is to monitor the digital trace of Internet searches in Ukrainian, Russian and English with the Google Trends analytical tool (trends.google.com). Initially, keywords were chosen that are most predictive, specific, and common enough to predict the forced migration from Ukraine. We requested the data before and during the war outbreak and divided the keyword frequency for each migration-related query to standardise the data. We compared this search frequency index with official statistics from UNHCR to prove the significations of results and correlations and test the models predictive potential. Since UNHCR does not yet have complete data on the demographic structure of refugees, to fill this gap, we used three other alternative Big Data sources: Facebook, Twitter and Instagram. Results: All tested migration-related search queries about emigration planning from Ukraine show the positive linear association between Google index and data from official UNHCR statistics; R2 = 0.1211 for searches in Russian and R2 = 0.1831 for searches in Ukrainian. It is noticed that Ukrainians use the Russian language more often to search for terms than Ukrainian. Increase in migration-related search activities in Ukraine such as граница (Rus. border), кордону (Ukr. border); Польща (Poland); Германия (Rus. Germany), Німеччина (Ukr. Germany) and Угорщина and Венгрия (Hungary) correlate strongly with officially UNHCR data for externally displaced persons from Ukraine. All three languages show that the interest in Poland is the highest. When refugees arrive in nearby countries, the search for terms related to Germany, such as crossing the border + Germany, etc., is proliferating. This result confirms our hypothesis that one-third of all refugees will cross into Germany. According to Big Data insights, the estimate of the total number of expected refugees is to expect 5,4 Million refugees. The age group most represented is between 24 and 45 years (data for children are unavailable), and over 65% are women. Conclusion: The increase in migration-related search queries is correlated with the rise in the number of refugees from Ukraine in the EU. Thus this method allows reliable forecasts. Understanding the consequences of forced migration from Ukraine is crucial to enabling UNHCR and governments to develop optimal humanitarian strategies and prepare for refugee reception and possible integration. The benefit of this method is reliable estimates and forecasting that can allow governments and UNHCR to prepare and better respond to the recent humanitarian crisis.

Suggested Citation

  • Jurić, Tado, 2022. "Predicting refugee flows from Ukraine with an approach to Big (Crisis) Data: a new opportunity for refugee and humanitarian studies," EconStor Preprints 251215, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:251215
    DOI: 10.1101/2022.03.15.22272428
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/251215/1/Medrxiv-Predicting-refugee-flows-from-Ukraine-with-an-approach-to-Big-Crisis-Data-JURIC-22.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.1101/2022.03.15.22272428?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Emilio Zagheni & Ingmar Weber & Krishna Gummadi, 2017. "Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants," Population and Development Review, The Population Council, Inc., vol. 43(4), pages 721-734, December.
    2. Alessandra Righi, 2019. "Assessing migration through social media: a review," Mathematical Population Studies, Taylor & Francis Journals, vol. 26(2), pages 80-91, April.
    3. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    4. Andrei SCRINIC, 2014. "Humanitarian aid and political aims in Eastern Ukraine: Russian involvement and European response," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 5, pages 77-88, December.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Tuba Bircan & Emre Eren Korkmaz, 2021. "Big data for whose sake? Governing migration through artificial intelligence," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-5, December.
    7. Kyriaki Kalimeri & Mariano G. Beiró & Andrea Bonanomi & Alessandro Rosina & Ciro Cattuto, 2020. "Traditional versus Facebook-based surveys: Evaluation of biases in self-reported demographic and psychometric information," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(5), pages 133-148.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed Shoukry Rashad, 2022. "The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai," Forecasting, MDPI, vol. 4(3), pages 1-11, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    2. Jurić, Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(1), pages 125-152.
    3. Jurić Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," Comparative Southeast European Studies, De Gruyter, vol. 70(1), pages 125-152, March.
    4. André Grow & Daniela Perrotta & Emanuele Del Fava & Jorge Cimentada & Francesco Rampazzo & B. Sofia Gil-Clavel & Emilio Zagheni & René D. Flores & Ilana Ventura & Ingmar G. Weber, 2021. "How reliable is Facebook’s advertising data for use in social science research? Insights from a cross-national online survey," MPIDR Working Papers WP-2021-006, Max Planck Institute for Demographic Research, Rostock, Germany.
    5. Lawrence M Berger & Giulia Ferrari & Marion Leturcq & Lidia Panico & Anne Solaz, 2021. "COVID-19 lockdowns and demographically-relevant Google Trends: A cross-national analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.
    6. Monica Alexander & Kivan Polimis & Emilio Zagheni, 2022. "Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(1), pages 1-28, February.
    7. Douglas R. Leasure & Ridhi Kashyap & Francesco Rampazzo & Claire A. Dooley & Benjamin Elbers & Maksym Bondarenko & Mark Verhagen & Arun Frey & Jiani Yan & Evelina T. Akimova & Masoomali Fatehkia & Rob, 2023. "Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data," Population and Development Review, The Population Council, Inc., vol. 49(2), pages 231-254, June.
    8. Selin Köksal & Luca Maria Pesando & Valentina Rotondi & Ebru Şanlıtürk, 2022. "Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After the COVID-19 Outbreak," European Journal of Population, Springer;European Association for Population Studies, vol. 38(3), pages 517-545, August.
    9. Ridhi Kashyap & Masoomali Fatehkia & Reham Al Tamime & Ingmar Weber, 2020. "Monitoring global digital gender inequality using the online populations of Facebook and Google," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(27), pages 779-816.
    10. Jurić, Tado, 2021. "Google Trends as a Method to Predict New COVID-19 Cases and Socio-Psychological Consequences of the Pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(forthcomi).
    11. Mattia Mazzoli & Boris Diechtiareff & Antònia Tugores & Willian Wives & Natalia Adler & Pere Colet & José J Ramasco, 2020. "Migrant mobility flows characterized with digital data," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    12. André Grow & Daniela Perrotta & Emanuele Del Fava & Jorge Cimentada & Francesco Rampazzo & Sofia Gil‐Clavel & Emilio Zagheni & René D. Flores & Ilana Ventura & Ingmar Weber, 2022. "Is Facebook's advertising data accurate enough for use in social science research? Insights from a cross‐national online survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 343-363, December.
    13. Mioara, POPESCU, 2015. "Construction Of Economic Indicators Using Internet Searches," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 6(1), pages 25-31.
    14. Andrea Bonanomi & Alessandro Rosina, 2022. "Employment Status and Well-Being: A Longitudinal Study on Young Italian People," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 581-598, June.
    15. Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021. "Studying Information Acquisition in the Field: A Practical Guide and Review," CEBI working paper series 21-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    16. David W Carter & Scott Crosson & Christopher Liese, 2015. "Nowcasting Intraseasonal Recreational Fishing Harvest with Internet Search Volume," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-18, September.
    17. Tuba Bircan & Almila Alkim Akdag Salah, 2022. "A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    18. Sansone, Dario, 2019. "Pink work: Same-sex marriage, employment and discrimination," Journal of Public Economics, Elsevier, vol. 180(C).
    19. Pulkit Sharma & Achut Manandhar & Patrick Thomson & Jacob Katuva & Robert Hope & David A. Clifton, 2019. "Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    20. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.

    More about this item

    Keywords

    refugee; Ukraine; Big Data; forced migration; Google Trends; UNHCR;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:esprep:251215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.