Presentation given at the International Metropolis Conference on June 25 in Ottawa. The conference trip was financially supported by the European Commission's Joint Research Center as well as by ACM's Distinguished Speakers Program.
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...
Digital advertising data for migration research
1. Digital Advertising Data for
Migration Research
Ingmar Weber
@ingmarweber
June 25, 2019
International Metropolis Conference 2019
Or How I learned to Love Online Advertising
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15. Bias Reduction via Model-Fitting
Mean out-of-sample absolute percentage error 37%,
down from 56% without origin-age bias correction
Adjusted R^2 = .70
Does not use GDP, language, internet penetration, …
z = age-gender group
i = country of birth
j = US state of residence
17. 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:
?
18. 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:
?
19. Obtaining an Assimilation Score
Migrant Group Assim. Score
Austrian migrants .900
Spanish migrants .864
French migrants .803
Turkish-speaking migrants .746
Arabic-speaking migrants .643
A: Women, non-uni, 45-64 .461
A: Men, uni, 18-24 .677
• Experimental methodology: take with a ton, not just a grain of salt
• Needs to be validated externally
• Goals include finding “bridging” interests/patterns
24. Studied in X, Lives in Y
• Compile a list of all universities for European
countries
• Query number of LinkedIn users who studied
in country X who now live in country Y
• Disaggregate by gender, age, industry, …
29. Strengths and Weaknesses
+ Facebook, LinkedIn, Twitter, Snapchat, Google, ...
+ Real-time estimates
+ Uses anonymous and aggregate data
+ Gender, age, location, country of origin, ….
- Black box on how attributes are inferred
- Needs modeling for bias correction
- Usage patterns and black box change over time
- No historic data available
- Risk of group-level misuse