SINIRLARIN ÖTESİNDEKİ DİJİTAL İZLER: GÖÇ KRİZİ ÜZERİNE SİSTEMATİK BİR DERLEME

Öz

Bu çalışma, uluslararası literatürde İngilizce olarak kaleme alınmış, büyük veri analiz araçları kullanılarak göçmen, sığınmacı ve mültecilerle ilgili yapılmış ve yayınlanmış çalışmaların gözden geçirilmesini ve çalışmalardan elde edilen verilerin sistematik bir biçimde incelenmesini amaçlamıştır. Makaleler Scholar, The Web of Science, ProQuest, Science Direct, PubMed ve Scopus veritabanları üzerinden taranmıştır. Taramalarda göçmen ve büyük veri kavramları etrafında yararlanılan kavram seti kullanılmıştır. PRISMA protokol ilkelerine uygun olarak 2022 yılı Aralık ayı sonuna kadar ilgili veritabanlarından elde edilen 258 makale arasından dahil etme ve hariç tutma kriterlerine göre 49 makale incelenmiştir. Taranan makaleler “ele alınan konular”, “veri seti”, “analizler”, “kullanılan yazılım” ve “başlıca bulgular” başlıkları altında incelenerek kategorileştirilmiştir. Araştırmalar, büyük veri araçlarının kullanılması yoluyla özellikle kitleselliği nedeniyle erişilmesi zor bir grup olan bu nüfus hakkında nasıl daha kolay bilgi elde edilebileceğine dair göstergeler sunmaktadır. Bulgularda göçmen, sığınmacı ve mültecilerle ilgili büyük veriye dayalı çalışmaların, bu grupların hedef ülkeye entegrasyonunu kolaylaştırma noktasında katkı sağladığı görülmüştür. Ayrıca bu çalışmaların bu gruplar açısından araştırma gruplarının gizliliğinin ihlal edilmesi, etiketlemeyi üretmesi, gözetimi artırması bağlamında sakıncalı sonuçlar doğurabileceği ortaya konulmuştur. Bunlara ek olarak bu araştırmaların temsil edilebilirlik, doğruluk oranı, aşırı homojenleştirme ve kolay yoldan genelleştirme gibi hususlarda metodolojik handikaplar taşıdığı bulgulanmıştır. Araştırmanın bulgularının büyük veri analiz araçları kullanılarak gerçekleştirilecek uluslararası göç ve mülteci politikalarına ilişkin ışık tutacağı düşünülmektedir.

Anahtar Kelimeler

• Büyük veri
• göç
• sistematik derleme

DIGITAL TRACKS BEYOND BORDERS: A SYSTEMATIC REVIEW ON THE MIGRATION CRISIS

Öz

This study aimed to systematically examine the studies conducted and published on immigrants, asylum seekers, and refugees by using big data written in English. Articles were searched on Scholar, The Web of Science, ProQuest, Science Direct, PubMed and Scopus databases. The concept set centered around the concepts of immigration and big data was used in the surveys. In accordance with the PRISMA protocol principles, 49 articles were examined according to the inclusion and exclusion criteria among 258 articles obtained from the relevant databases until the end of December 2022. The reviewed articles were categorized under the headings of “topics examined”, “dataset”, “analyses”, “software used” and “key findings”. The studies provide indications on how to obtain information about this population, which is difficult to reach group especially due to its massiveness, using big data tools. In the findings, it has been seen that studies based on big data on immigrants, asylum seekers and refugees contribute to facilitating the integration of these groups into the target country. Also, it has been revealed that these studies may lead to undesirable results in terms of violating the confidentiality of research groups, producing labeling, and increasing surveillance for these groups. In addition to these, it has been found that these studies have methodological handicaps in terms of representativeness, accuracy, excessive homogenization, and easy generalization. It is thought that the findings of the study will shed light on the international migration and refugee policies to be carried out using big data analysis tools.

Anahtar Kelimeler

• Big data
• migration
• systematic review

Kaynakça

1. Ahmed, N., Diptu, N.A., Shadhin, M.S.K., Jaki, M.A.F., Hasan, M.F., Islam, M.N. and Rahman, R.M. (2019). Artificial neural network and machine learning based methods for population estimation of Rohingya refugees: Comparing data-driven and satellite image-driven approaches. Vietnam Journal of Computer Science, 6(04), 439-455. DOI: 10.1142/S2196888819500246.
2. Ahmed, N., Firoze, A. and Rahman, R.M. (2020). Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure. Journal of Information and Telecommunication, 4(2), 175-198. DOI: 10.1080/24751839.2019.1704114.
3. Aslan, P. and Ertem Eray, T. (2019). How to analyze big data: a study on understanding what the Turkish think about Syrian refugee crisis. Journal of Selçuk Communication, 12(2), 763-780. DOI: 10.18094/josc.596301.
4. Atar, E. (2021). Systematic analysis of the advantages and disadvantages of using big data in the context of international migration and refugees. Alternative Policy, 13(1), 146-174.
5. Augsburger, M. and Elbert, T. (2017). When do traumatic experiences alter risk-taking behavior? A machine learning analysis of reports from refugees. PLoS ONE, 12(5), e0177617. DOI: 10.1371/journal.pone.0177617.
6. Aydemir, B., Aydın, H., Çetinkaya, A. and Polat, D.Ş. (2022). Predicting the Income Groups and Number of Immigrants by Using Machine Learning (ML). International Journal of Multidisciplinary Studies and Innovative Technologies, 6(2), 162-168. DOI: 10.36287/ijmsit.6.2.162.
7. Azizi, S., Ngwaba, C.A. and Ekhator-Mobayode, U.E. (2021). Can Machine Learning Predict Quantity and Duration of Migration to the USA? The Journal of Prediction Markets, 15(1), 97-107. DOI: 10.5750/jpm.v15i1.1859.
8. Baird, S., Panlilio, R., Seager, J., Smith, S. and Wydick, B. (2022). Identifying psychological trauma among Syrian refugee children for early intervention: Analyzing digitized drawings using machine learning. Journal of Development Economics, 156 (1), 102822. DOI: 10.1016/j.jdeveco.2022.102822.

9. Baym, N. (2010). Personal Connections in a Digital Age. Cambridge: Polity Press.
10. Bell, D. (1999). The coming of post-industrial society. New York: Basic Books.
11. Bertsimas, D. and Fazel-Zarandi, M.M. (2021). Prescriptive machine learning for public policy: The case of immigration enforcement. Computer Sciences. Under Review.
12. Best, K., Gilligan, J., Baroud, H., Carrico, A., Donato, K. and Mallick, B. (2022). Applying machine learning to social datasets: a study of migration in southwestern Bangladesh using random forests. Regional Environmental Change 22(2), 52. DOI: 10.1007/s10113-022-01915-1.
13. Beyer, M.A. and Laney, D. (2012). The importance of ‘big data’: A definition. Gartner Report. Available at: https://www.gartner.com/doc/2057415/importance -big-data-definition.
14. Carammia, M., Iacus, S.M. and Wilkin, T. (2022). Forecasting asylum-related migration flows with machine learning and data at scale. Scientific Reports 12(1), 1-25. DOI: 1457. 10.1038/s41598-022-05241-8.
15. Castells, M. (1996). The rise of the network society. Cambridge. Blackwell.
16. Chang, C.C. (2018). Hakka genealogical migration analysis enhancement using big data on library services. Library Hi Tech, 36(3), 426-442. DOI: 10.1108/LHT-08-2017-0172.
17. Chen, Y., Li, K., Zhou, Q. and Zhang, Y. (2022). Can Population Mobility Make Cities More Resilient? Evidence from the Analysis of Baidu Migration Big Data in China. International Journal of Environmental Research and Public Health, 20(1), 36. DOI: 10.3390/ijerph20010036.
18. Choi, S., Hong, J.Y., Kim, Y.J. and Park, H. (2020). Predicting psychological distress amid the COVID-19 pandemic by machine learning: discrimination and coping mechanisms of Korean immigrants in the US. International Journal of Environmental Research and Public Health, 17(17), 6057. DOI: 10.3390/ijerph17176057.
19. Cox, M. and Ellsworth, D. (1997). Application-controlled demand paging for out-of-core visualization. Report NAS-97-010, MS T27A-2. Moffett Field, CA: NASA Ames Research Center.
20. Davenport, T.H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership, 42(4), 45-50.
21. Diebold, F.X. (2021). What’s the big idea? Big data and its origins. Significance, 18(1), 36-37. DOI: 10.1111/1740-9713.01490
22. Emami, S.N., Yousefi S., Pourghasemi, H.R., Tavangar, S. and Santosh, M. (2020). A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran). Bulletin of Engineering Geology and the Environment, 79, 5291-5308. DOI: 10.1007/s10064-020-01915-7.
23. Fernández-Martínez, J.L., Boga, J.A., de Andrés-Galiana, E., Casado, L., Fernández, J., Menéndez, C., ... Rodríguez-Guardado, A. (2021). A Machine Learning Model for Evaluating Imported Disease Screening Strategies in Immigrant Populations. The American Journal of Tropical Medicine and Hygiene, 105(5), 1413-1419. DOI: 10.4269/ajtmh.20-1443.
24. Gahi, Y., Guennoun, M. and Mouftah, H.T. (2016). Big data analytics: security and privacy Challenges. 2016 IEEE Symposium on Computers and Communication (ISCC), 952-957. Messina. Italy: IEEE.
25. Gao, Y., Nan, Y. and Song, S. (2022). High‐speed rail and city tourism: Evidence from Tencent migration big data on two Chinese golden weeks. Growth and Change, 53(3), 1012-1036. DOI: 10.1111/grow.12473.
26. Garha, N.S. and Domingo, A. (2019). Indian diaspora population and space: national register, UN Global Migration Database and Big Data. Diaspora Studies, 12(2), 134-159. DOI: 10.1080/09739572.2019.1635390.
27. Giang, N.H., Nguyen, T.T., Tay, C.C., Phuong, L.A. and Dang, T.T. (2022). Towards predictive Vietnamese human resource migration by machine learning: A case study in northeast Asian countries. Axioms, 11(4), 151. DOI: 10.3390/axioms11040151.
28. Havas, C., Wendlinger, L., Stier, J., Julka, S., Krieger, V., Ferner, C., ... & Resch, B. (2021). Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics. ISPRS International Journal of Geo-Information, 10(8), 498. DOI: 10.3390/ijgi10080498.
29. Huang, Y. and Shao, M. (2022). Challenges and Countermeasures of Arab Immigrants and International Trade in the Era of Big Data. Mathematical Problems in Engineering, 1(1), 1-11. DOI: 10.1155/2022/1025453.
30. International Committee of the Red Cross (ICRC) & Privacy International (2018). The humanitarian metadata problem: “Doing no harm” in the digital era. Available at: https://privacyinternational.org/report/2509/humanitarian-metadata-problem-doing-no-harm-digital-era
31. Juric, T. (2022b). Predicting refugee flows from Ukraine with an approach to Big (Crisis) Data: a new opportunity for refugee and humanitarian studies. Athens Journal of Technology and Engineering, 9(3), 159-184.
32. Juric, T. (2022c). Ukrainian refugee integration and flows analysis with an approach of Big Data: Social media insights. MedrXiv, Under review.
33. Jurić, T. (2022a). Big (Crisis) Data in Refugee and Migration Studies–Case Study of Ukrainian Refugees. Comparative Southeast European Studies, 70(3), 540-553. DOI: 10.1515/soeu-2022-0048.
34. Katsikouli, P., Byrne, W.H., Gammeltoft-Hansen, T., Høgenhaug, A.H., Møller, N.H., Nielsen, T.R., ... & Slaats, T. (2022). Machine Learning and Asylum Adjudications: From Analysis of Variations to Outcome Predictions. IEEE Access, 10(1), 130955-130967.
35. Khangahi, F.D. and Kiani, F. (2021). Social Mobilization and Migration Predictions by Machine Learning Methods: A study case on Lake Urmia. International Journal of Innovative Technology and Exploring Engineering, 10(6), 123-127. DOI: 10.35940/ijitee. F8833.0410621.
36. Kılıç, Ö.O., Akyol, M.A., Işık, O., Kılıç, B.G., Aydınoğlu, A.U., Sürer, E., ... & Temizel, T.T. (2019). Data analytics without borders: multi-layered insights for Syrian refugee crisis. In Data for refugees challenge workshop.
37. Kiossou, H.S., Schenk, Y., Docquier, F., Houndji, V.R., Nijssen, S. and Schaus, P. (2020). Using an interpretable Machine Learning approach to study the drivers of International Migration. arXiv preprint, Available at: https://aiforgood2020.github.io/papers/AI4SG_paper_46.pdf
38. Korkmaz, E. (2020). Using big data in migration and refugee studies. Science & Enlightenment, 4(3), 241-248.
39. Krupenkin, M. and Rothschild, D. (2019). Using Machine Learning to Measure Changes in Cable News Coverage of Immigration (2014-2019). Computation+ Journalism. Available at: https://bpb-us-w2.wpmucdn.com/sites.northeastern.edu/dist/0/367/files/2020/02/CJ_2020_paper_41.pdf
40. Lai, J. and Pan, J. (2020). China's City Network Structural Characteristics Based on Population Flow during Spring Festival Travel Rush: Empirical Analysis of “Tencent Migration” Big Data. Journal of Urban Planning and Development, 146(2), 04020018.
41. Lee, H. and Song, E. (2022). Peer discrimination toward rural migrant students and academic performance in urban China: A machine learning approach. Cities, 131 (1), 104027. DOI: 10.1016/j.cities.2022.104027.
42. Lu, Y. (2022). Detecting Imperfect Substitution between Comparably Skilled Immigrants and Natives: A Machine Learning Approach. International Migration Review. Under Review.
43. Martey, E. and Armah, R. (2021) Welfare effect of international migration on the left-behind in Ghana: Evidence from machine learning. Migration Studies, 9(3), 872-895. DOI: 10.1093/migration/mnaa025.
44. Masuda, Y. (1990). Managing in the information society. Cambridge: Blackwell.
45. Micevska, M. (2021). Revisiting forced migration: A machine learning perspective. European Journal of Political Economy, 70 (1), 102044. DOI: 10.1016/j.ejpoleco.2021.102044.
46. Molina, M.D., Chau, N., Rodewald, A.D. and Garip, F. (2022). How to model the weather-migration link: a machine-learning approach to variable selection in the Mexico-US context. Journal of Ethnic and Migration Studies, 49(4), 1-27. DOI: 10.1080/1369183X.2022.2100549.
47. Nair, R., Madsen, B.S., Lassen, H., Baduk, S., Nagarajan, S., Mogensen, L.H., ... & Urbak, S. (2019). A machine learning approach to scenario analysis and forecasting of mixed migration. IBM Journal of Research and Development, 64 (1/2), 7-10. DOI: 10.1147/JRD.2019.2948824.
48. Olberg, N. and Seuken, S. (2022). Enabling trade-offs in machine learning-based matching for refugee resettlement. arXiv, 1 (11), 1-19. DOI: 10.48550/arXiv.2203.16176.
49. Quinn, J.A., Nyhan, M.M., Navarro, C., Coluccia, D., Bromley, L. and Luengo-Oroz, M. (2018). Humanitarian applications of machine learning with remote-sensing data: Review and case study in refugee settlement mapping. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376 (2128), 20170363 DOI: 10.1098/rsta.2017.0363.
50. Rahaman, M., Morshed, M.M., and Bhadra, S. (2022). An integrated machine learning and remote sensing approach for monitoring forest degradation due to Rohingya refugee influx in Bangladesh. Remote Sensing Applications: Society and Environment, 25(1), 100696. DOI: 10.1016/j.rsase.2022.100696.
51. Raman, V., Vera, C. and Manna, C.J. (2022). Bias, Consistency, and Partisanship in US Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions. In Equity and Access in Algorithms, Mechanisms, and Optimization, 4(1), 1-14. DOI: 10.1145/3551624.3555288.
52. Ran, X., Xu, Y., Liu, Y. and Jiang, J. (2022). Examining online social behavior changes after migration: An empirical study based on OSN big data. Computers in Human Behavior 129 (1), 107158. DOI: 10.1016/j.chb.2021.107158.
53. Ren, C. and Bloemraad, I. (2022). New Methods and the Study of Vulnerable Groups: Using Machine Learning to Identify Immigrant-Oriented Nonprofit Organizations, Socius, 8(1), 1-14. DOI: 10.1177/23780231221076992.
54. Ruhnke, S.A., Reynolds, M.M., Wilson, F.A. and Stimpson, J.P. (2022). A healthy migrant effect? Estimating health outcomes of the undocumented immigrant population in the United States using machine learning. Social Science & Medicine, 307(1), 115177. DOI: 10.1016/j.socscimed.2022.115177.
55. Ruhnke, S.A., Wilson, F.A. and Stimpson, J.P. (2022). Using machine learning to impute legal status of immigrants in the National Health Interview Survey. MethodsX, 8(9), 101848. DOI: 10.1016/j.mex.2022.101848.
56. Simionescu, M. (2021). The status of immigrants on Italian labour market in the context of economic decline: Evidence from survey, macroeconomic and big data. Economics, Management and Sustainability, 6(1), 34-48. DOI: 10.14254/jems.2021.6-1.3.
57. Štular, B., Lozić, E., Belak, M., Rihter, J., Koch, I., Modrijan, Z., ... Gutjahr, C. (2022). Migration of Alpine Slavs and machine learning: Space-time pattern mining of an archaeological data set. PLoS ONE, 17(9), 0274687. DOI: 10.1371/journal.pone.0274687.
58. Szocska, M., Pollner, P., Schiszler, I., Joo, T., Palicz, T., McKee, M., ... Gaal, P. (2021). Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis. Scientific Reports, 11 (1), 5943. DOI: 10.1038/s41598-021-81873-6
59. Weber, H. (2020). How well can the migration component of regional population change be predicted? A machine learning approach applied to German municipalities. Comparative Population Studies-Zeitschrift für Bevölkerungswissenschaft, 45 (1), 143-178. DOI: 10.12765/CPoS-2020-08en.
60. Wilson, F.A., Zallman, L., Pagán, J.A., Ortega, A.N., Wang, Y., Tatar, M. and Stimpson, J.P. (2020). Comparison of use of health care services and spending for unauthorized immigrants vs authorized immigrants or US citizens using a machine learning model. JAMA network open, 3 (12), e2020.29230. DOI: 10.1001/jamanetworkopen.2020.29230.
61. Xiao Z, Bi M, Zhong Y, Feng X and Ma H (2021) Study on the evolution of the source-flow-sink pattern of China’s Chunyun population migration network: Evidence from Tencent big data. Urban Science, 5(3): 66. DOI: 10.3390/urbansci5030066.
62. Yılmaz, B. and Ozcan, E. (2021). Big data problematic in social sciences of fourth generation human rights, Turkish Studies, 17(3), 473-393. DOI: 10.7827/TurkishStudies.58072.
63. Zhou, X., Chen, Z., Yeh, A.G. and Yue, Y. (2021). Workplace segregation of rural migrants in urban China: A case study of Shenzhen using cellphone big data. Environment and Planning B: Urban Analytics and City Science, 48 (1), 25-42. DOI: 10.1177/2399808319846903.