Slides used at #BigData4Migration workshop. https://ec.europa.eu/jrc/en/event/workshop/big-data-and-alternative-data-sources-migration-case-studies-policy-support See https://ingmarweber.de/publications/ for some related publications.

1. Using Internet Advertising Data for
Studying International Migration
Ingmar Weber
iweber@hbku.edu.qa
November 30, 2017
European Commission – Joint Research Centre, Ispra

2. Facebook’s Audience Estimates

3. Audience Estimate = Digital User Census
• Facebook, Twitter and LinkedIn all provide “how
many users match criteria X” data
• Typically used for ad campaign planning
• FB supports ad campaign targeting for
– Age and gender (self-reported)
– Home country (inferred from “home city”)
– Education level (inferred from “education history”)
– Interests (inferred from “Likes”)
– Mostly US: income, ethnic affinity, political leaning

4. LEVERAGING FACEBOOK’S ADVERTISING
PLATFORM TO MONITOR STOCKS OF MIGRANTS
Emilio Zagheni, Ingmar Weber, Krishna Gummadi
Published in Population and Development Review

5. Expats Across US States
2014
2017

6. Expats Across Countries
2015
2017

7. Age-Specific Selection Biases

8. Bias Reduction via Model-Fitting
Mean out-of-sample absolute percentage error 37%,
down from 56% without origin-age bias correction
Adj. R^2 = .70
Does not use GDP, language, internet penetration, …
z = age-gender group
i = country of birth
j = US state of residence

9. MEASURING ASSIMILATION OF INTERESTS IN
SYRIAN REFUGEES IN GERMANY
Antoine Dubois, Kiran Garimella, Ingmar Weber, Emilio Zagheni
Ongoing work

10. Do Refugees Share German Interests?
What interests to consider? Everybody likes “Music” and “Technology”.
How to interpret the score? High/low compared to European migrants?
Germans in DEU
FB Interests:
Football (90%)
Max Planck (70%)
Sauerkraut (40%)
…
Arabs in MENA
FB Interests:
Quran (80%)
Ibn Al-Haytham (60%)
Falafel (60%)
…
Arabs in DEU
FB Interests:
?

11. Pros and Cons of FB Ad Audience Data
+ International reach (~214M of ~244M expats)
+ Supports disaggregation (age, gender, …)
+ Very timely data (~ weekly updates)
+ Supports measuring “soft” attributes
- Selection bias (can be corrected, if understood)
- Built on black box (but possible to validate)
- Relies on FB’s existence (but idea more general)
- Fake accounts (unclear if this affects models)

12. Related Ongoing Projects
Studying European Migrants to the UK
Francesco Rampazzo, Jakub Bijak, Agnese Vitali, Ingmar Weber, Emilio
Zagheni
Using FB Advertising Data to Track the Global Digital
Gender Gap
Masoomali Fatehkia, Ridhi Kashyap, Ingmar Weber
Male Fertility: What Can FB Advertising Data Tell Us?
Francesco Rampazzo, Francesco Billari, Maria Rita Testa, Ingmar
Weber, Emilio Zagheni
Gender Skill Gaps on LinkedIn Across the UK
Karri Harranko, Kiran Garimella, Ingmar Weber, Emilio Zagheni

13. Key Collaborator
Emilio Zagheni
Associate Prof. at Univ. of Washington
Collaborating since 2011
9 joint peer-reviewed publications so far
Always happy to explore collaborations!

14. Thanks!
Ask me about data viz for Doha during break