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Using internet advertising data for studying international migration


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Slides used at #BigData4Migration workshop.

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Using internet advertising data for studying international migration

  1. 1. Using Internet Advertising Data for Studying International Migration Ingmar Weber November 30, 2017 European Commission – Joint Research Centre, Ispra
  2. 2. Facebook’s Audience Estimates
  3. 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. 4. LEVERAGING FACEBOOK’S ADVERTISING PLATFORM TO MONITOR STOCKS OF MIGRANTS Emilio Zagheni, Ingmar Weber, Krishna Gummadi Published in Population and Development Review
  5. 5. Expats Across US States 2014 2017
  6. 6. Expats Across Countries 2015 2017
  7. 7. Age-Specific Selection Biases
  8. 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. 9. MEASURING ASSIMILATION OF INTERESTS IN SYRIAN REFUGEES IN GERMANY Antoine Dubois, Kiran Garimella, Ingmar Weber, Emilio Zagheni Ongoing work
  10. 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. 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. 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. 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. 14. Thanks! Ask me about data viz for Doha during break