Quantifying international human mobility patterns using Facebook Network data

Autoři: Spyridon Spyratos aff001;  Michele Vespe aff001;  Fabrizio Natale aff001;  Ingmar Weber aff002;  Emilio Zagheni aff003;  Marzia Rango aff004
Působiště autorů: Knowledge Centre on Migration and Demography, Joint Research Centre, European Commission, Ispra, Italy aff001;  Qatar Computing Research Institute at Hamad Bin Khalifa University, Doha, Qatar aff002;  Max Planck Institute for Demographic Research, Rostock, Germany aff003;  Global Migration Data Analysis Centre, International Organization for Migration, Berlin, Germany aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224134


Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook’s advertising platform. Facebook’s advertising platform classifies its users as “lived in country X” if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) “migrants” in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.

Klíčová slova:

Advertising – Age groups – Economics of migration – Facebook – Human mobility – Social media – Social networks – United States


1. Facebook. Facebook—Log In or Sign Up [Internet]. 2019 [cited 8 May 2019]. Available: https://www.facebook.com/

2. Instagram. Instagram [Internet]. 2019 [cited 8 May 2019]. Available: https://www.instagram.com/

3. Messenger. Messenger [Internet]. 2019 [cited 8 May 2019]. Available: https://www.messenger.com/

4. Facebook Audience Network. Facebook Audience Network: Monetize Your App or Web Property [Internet]. 2019 [cited 8 May 2019]. Available: https://www.facebook.com/audiencenetwork

5. UNDESA. Trends in International Migrant Stock: The 2017 Revision (United Nations database, POP/DB/MIG/Stock/Rev.2017). [Internet]. United Nations; 2017 p. 16. Available: http://www.un.org/en/development/desa/population/migration/data/estimates2/estimates17.shtml

6. Zagheni E, Weber I. Demographic research with non-representative internet data. Int J Manpow. 2015;36: 13–25. doi: 10.1108/IJM-12-2014-0261

7. Zagheni E, Weber I, Gummadi K. Leveraging Facebook’s Advertising Platform to Monitor Stocks of Migrants. Popul Dev Rev. 2017;43: 721–734.

8. Spyratos S, Vespe M, Natale F, Weber I, Zagheni E, Ranco M. Migration Data using Social Media—a European Perspective [Internet]. Luxembourg: Publications Office of the European Union; 2018. doi: 10.2760/964282

9. Zagheni E, Polimis K, Alexander M, Weber I, Billari FC. Combining Social Media Data and Traditional Surveys to Nowcast Migration Stocks. 2018; 1–17.

10. Hausmann R, Hinz J, Yildirim MA. Measuring Venezuelan emigration with Twitter, Kiel Working Paper, No. 2106. Kiel; 2018. Report No.: 2106.

11. Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C. Geo-located Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci. Taylor & Francis; 2014;41: 260–271. doi: 10.1080/15230406.2014.890072 27019645

12. Zagheni E, Garimella KVR, Weber I, State B. Inferring international and internal migration patterns from Twitter data. Proceedings of the 23rd International Conference on World Wide Web. Seoul, Korea: ACM; 2014. pp. 439–444. doi: 10.1145/2567948.2576930

13. Yildiz D, Munson J, Vitali A, Tinati R, Holland JA. Using Twitter data for demographic research. Demogr Res. 2017;37: 1477–1514. doi: 10.4054/DemRes.2017.37.46

14. State B, Ingmar W, Zagheni E. Studying Inter-National Mobility through IP Geolocation. WSDM’13. Rome, Italy: ACM; 2013. pp. 265–274.

15. Zagheni E, Weber I. You are where you e-mail: using e-mail data to estimate international migration rates. Proc 3rd Annu ACM Web Sci Conf. 2012; 348–351. doi: 10.1145/2380718.2380764

16. Messias J, Benevenuto F, Weber I, Zagheni E. From migration corridors to clusters: The value of Google+ data for migration studies. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. 2016. pp. 421–428. doi: 10.1109/ASONAM.2016.7752269

17. State B, Rodriguez M, Helbing D, Zagheni E. Migration of Professionals to the U.S. Evidence from LinkedIn data. Soc Informatics. 2014;8851: 531–543. doi: 10.1007/978-3-319-13734-6

18. Barslund M, Busse M. How Mobile is Tech Talent? A Case Study of IT Professionals Based on Data from LinkedIn. Brussels: CEPS; 2016.

19. Barchiesi D, Moat HS, Alis C, Bishop S, Preis T. Quantifying international travel flows using Flickr. PLoS One. 2015;10: 1–8. doi: 10.1371/journal.pone.0128470 26147500

20. Ahas R, Silm S, Tiru M. Tracking Transnationalism Originating in Estonia Through Mobile Roaming Data, Estonian Human Development Report 2017 [Internet]. 2017. Available: https://inimareng.ee/en/open-to-the-world/tracking-trans-nationalism-with-mobile-telephone-data/

21. Hughes C, Zagheni E, Abel GJ, Sorichetta A, Wi’sniowski A, Weber I, et al. Inferring Migrations: Traditional Methods and New Approaches based on Mobile Phone, Social Media, and other Big Data: Feasibility study on Inferring (labour) [Internet]. Luxembourg; 2016. doi: 10.2767/61617

22. Rango M, Vespe M. Big Data and alternative data sources on migration: From case-studies to policy support Summary report. Ispra, Italy; 2017.

23. Smith A, Anderson M. Social Media Use in 2018 [Internet]. Pew Research Center; 2018. Available: http://www.pewinternet.org/2018/03/01/social-media-use-in-2018/

24. Facebook. ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 For the fiscal year ended December 31, 2018 [Internet]. 2018. Available: https://www.sec.gov/Archives/edgar/data/1326801/000132680115000032/fb-9302015x10q.htm

25. Richards NM, King JH. Big Data ethics. Big Data Soc. 2014;49: 393. doi: 10.1177/2053951714559253

26. Hiltin P, Rainie L. Facebook Algorithms and Personal Data [Internet]. 2019. Available: https://www.pewinternet.org/2019/01/16/facebook-algorithms-and-personal-data/

27. Facebook. Advertising on Facebook | Facebook Business [Internet]. 2019 [cited 8 May 2019]. Available: https://www.facebook.com/business/products/ads

28. Herdagdelen A, State B, Adamic L, Mason W. The social ties of immigrant communities in the United. Proceedings of the 8th ACM Conference on Web Science. ACM; 2016. pp. 78–84. doi: 10.1145/2908131.2908163

29. United Nations. Recommendations on Statistics of International Migration, Revision 1. New York; 1998.

30. Fatehkia M, Kashyap R, Weber I. Using Facebook ad data to track the global digital gender gap. World Dev. The Author(s); 2018;107: 189–209. doi: 10.1016/j.worlddev.2018.03.007

31. US Census Bureau. American Community Survey Sample Questionnaire [Internet]. 2018 [cited 21 Nov 2018]. Available: https://www.census.gov/programs-surveys/acs/

32. Eurostat. Population on 1 January by age group, sex and country of birth [Internet]. 2017 [cited 12 Jun 2018]. Available: http://ec.europa.eu/eurostat/web/products-datasets/-/migr_pop3ctb

33. OECD. Database on Immigrants in OECD and non-OECD Countries [Internet]. 2011 [cited 22 Mar 2018]. Available: http://www.oecd.org/els/mig/dioc.htm

34. UNDESA. Population by 5-year age groups, annually from 1950 to 2100: medium projection variant [Internet]. 2017 [cited 20 Mar 2018]. Available: https://esa.un.org/unpd/wpp/Download/Standard/CSV/

35. Lamb K. “I felt disgusted”: inside Indonesia’s fake Twitter account factories. In: The Guardian [Internet]. 2018 [cited 1 Apr 2019]. Available: https://www.theguardian.com/world/2018/jul/23/indonesias-fake-twitter-account-factories-jakarta-politic

36. Armario C. Colombia tightens border control as Venezuela migrants surge. In: The Associated Press [Internet]. 2018 [cited 29 Nov 2018]. Available: https://apnews.com/0cb8aa4801134173ab62889bf580b8d8

37. Taj M. Brazil sends army to border as Venezuelans flee crisis at home. In: Reuters [Internet]. 2018 [cited 29 Nov 2018]. Available: https://www.reuters.com/article/us-venezuela-migration-summit/brazil-sends-army-to-border-as-venezuelans-flee-crisis-at-home-idUSKCN1LD27Q

38. Instituto Nacional del Estadística. Population by country of birth, age (five-years groups) and sex, Advance of the Municipal Register at 1st January 2018. Provisional results. [Internet]. 2018 [cited 31 May 2018]. Available: http://www.ine.es/jaxiPx/Tabla.htm?path=/t20/e245/p04/provi/l0/&file=0ccaa005.px&L=1

Článek vyšel v časopise


2019 Číslo 10
Nejčtenější tento týden