Leveraging Internet Advertising Data for Migration Studies

 
Using Internet Advertising Data for
Studying International Migration
 
Ingmar Weber
iweber@hbku.edu.qa
November 30, 2017
European Commission – Joint Research Centre, Ispra
Facebook’s Audience Estimates
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
 
LEVERAGING FACEBOOK’S ADVERTISING
PLATFORM TO MONITOR STOCKS OF MIGRANTS
 
Emilio Zagheni, Ingmar Weber, Krishna Gummadi
 
Published in Population and Development Review
Expats Across US States
 
FB underestimates
 
FB overestimates
2014
2017
Expats Across Countries
 
FB underestimates
 
FB overestimates
2015
2017
 
Age-Specific Selection Biases
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
 
MEASURING ASSIMILATION OF INTERESTS IN
SYRIAN REFUGEES IN GERMANY
 
Antoine Dubois, Kiran Garimella, Ingmar Weber, Emilio Zagheni
 
Ongoing work
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:
?
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)
 
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
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!
 
Thanks!
Ask me about data viz for Doha during break
http://fb-doha.qcri.org/
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This study explores the innovative use of internet advertising data, particularly from platforms like Facebook, to analyze international migration patterns and trends. The research delves into how digital user census information can be utilized for ad campaign planning and monitoring migrant populations. Various research works and studies are highlighted, focusing on topics such as assimilation of interests in refugee populations and bias reduction in migration modeling. The analysis encompasses a broad spectrum of data sources and methodologies to gain insights into global migration dynamics.

  • Migration Studies
  • Internet Advertising
  • Data Analysis
  • Social Media
  • Research

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  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. Facebooks 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. Emilio Zagheni, Ingmar Weber, Krishna Gummadi LEVERAGING FACEBOOK S ADVERTISING PLATFORM TO MONITOR STOCKS OF MIGRANTS 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 z = age-gender group i = country of birth j = US state of residence 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,

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

  10. Do Refugees Share German Interests? 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: ? What interests to consider? Everybody likes Music and Technology . How to interpret the score? High/low compared to European migrants?

  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 http://fb-doha.qcri.org/

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