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Quantifying international human mobility patterns using Facebook Network data

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 i...

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Autores principales: Spyratos, Spyridon, Vespe, Michele, Natale, Fabrizio, Weber, Ingmar, Zagheni, Emilio, Rango, Marzia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812739/
https://www.ncbi.nlm.nih.gov/pubmed/31648280
http://dx.doi.org/10.1371/journal.pone.0224134
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author Spyratos, Spyridon
Vespe, Michele
Natale, Fabrizio
Weber, Ingmar
Zagheni, Emilio
Rango, Marzia
author_facet Spyratos, Spyridon
Vespe, Michele
Natale, Fabrizio
Weber, Ingmar
Zagheni, Emilio
Rango, Marzia
author_sort Spyratos, Spyridon
collection PubMed
description 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.
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spelling pubmed-68127392019-11-03 Quantifying international human mobility patterns using Facebook Network data Spyratos, Spyridon Vespe, Michele Natale, Fabrizio Weber, Ingmar Zagheni, Emilio Rango, Marzia PLoS One Research Article 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. Public Library of Science 2019-10-24 /pmc/articles/PMC6812739/ /pubmed/31648280 http://dx.doi.org/10.1371/journal.pone.0224134 Text en © 2019 Spyratos et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Spyratos, Spyridon
Vespe, Michele
Natale, Fabrizio
Weber, Ingmar
Zagheni, Emilio
Rango, Marzia
Quantifying international human mobility patterns using Facebook Network data
title Quantifying international human mobility patterns using Facebook Network data
title_full Quantifying international human mobility patterns using Facebook Network data
title_fullStr Quantifying international human mobility patterns using Facebook Network data
title_full_unstemmed Quantifying international human mobility patterns using Facebook Network data
title_short Quantifying international human mobility patterns using Facebook Network data
title_sort quantifying international human mobility patterns using facebook network data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812739/
https://www.ncbi.nlm.nih.gov/pubmed/31648280
http://dx.doi.org/10.1371/journal.pone.0224134
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