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Quantifying international human mobility patterns using Facebook Network data.

Spyridon Spyratos¹, Michele Vespe¹, Fabrizio Natale¹

¹Knowledge Centre on Migration and Demography, Joint Research Centre, European Commission, Ispra, Italy.

|October 25, 2019

Summary

This summary is machine-generated.

This study explores using Facebook data to track global migration patterns, offering insights for migration governance. While not a replacement for official statistics, Facebook Network (FN) data shows potential for migration trend analysis and early warnings.

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Area of Science:

Demography
Social Sciences
Data Science

Background:

Improving global migration statistics is crucial for effective migration governance.
Current data on international migration is often limited in timeliness and granularity.
Non-traditional data sources are being explored to address these data gaps.

Purpose of the Study:

To investigate the feasibility of using Facebook advertising data for quantifying international mobility.
To estimate global migration patterns using anonymized Facebook Network data.
To assess the validity and potential applications of these novel migration estimates.

Main Methods:

Utilized anonymized, publicly available data from Facebook's advertising platform.
Applied a sample bias correction method to Facebook Network (FN) user data.
Compared FN-derived migration estimates with official statistics and analyzed temporal changes.

Main Results:

Estimated the number of FN "migrants" across 119 countries, disaggregated by age, gender, and origin.
Successfully captured the increase in Venezuelan migration to Colombia and Spain in 2018.
Demonstrated a correlation between FN-derived estimates and official migration data.

Conclusions:

Anonymized Facebook data, after bias correction, can provide valuable insights into migration trends.
FN-derived estimates are suitable for trend analysis and early-warning systems in migration governance.
These estimates complement, but do not replace, official migration statistics due to representativeness and methodological uncertainties.