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: 10.1371/journal.pone.0224134

Souhrn

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


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Článek vyšel v časopise

PLOS One


2019 Číslo 10