Lecture given as part of the BIGSSS 2019 summer school on migration (https://bigsss-css.jacobs-university.de/migration2019/migration/). See https://ingmarweber.de/publications/ for related publications. Mostly joint work with Emilio Zagheni.

1. Digital Trace Data for
Demographic Research
Ingmar Weber
@ingmarweber
June 12, 2019
Lecture at BIGSSS CSS 2019
Or How I learned to Love Online Advertising

2. Amazing Collaborators! (Alphab.)
• Natalie Adler, Musa Al-Asad, Francesco Billari,
Antoine Dubois, Masoomali Fatehkia, Harsh
Gandhi, Manuel Garcia, Kiran Garimella, Krishna
Gummadi, Karri Haranko, Ridhi Kashyap, Yelena
Mejova, Alfredo Morales, Fabrizio Natale, Joao
Palotti, Tejas Rafaliya, Francesco Rampazzo, Marzia
Rango, Vedran Sekara, Vatsala Singh, Spyridon
Spyratos, Bogdan State, Reham Tamime, Michele
Vespe, Jeffrey Villaveces, Agnese Vitali, Emilio
Zagheni, …

3. What is Demography?
Demography is the statistical study of populations.
According to IP address 70.67.193.176, user Pbsouthwood and other
contributors to https://en.wikipedia.org/wiki/Demography

4. The Population Equation
Change in population = Inputs – Outputs
Inputs = Births + In-migration
Outputs = Deaths + Out-Migration
• ∆P = (B + I) − (D + O)
Fertility, Mortality and Migration

5. Quant: How much? Where? When?
• Births
- Birth registry: India: ~75%, Kenya: ~65%, Liberia: ~25% (2017)
• Deaths
- “Global Burden of Disease” (Murray and Lopez, 1997):
“Medically certified information is available for less than 30% of
the estimated 50.5 million deaths that occur each year
worldwide.”
• Migration
- “The size of the irregular migrant stock of the EU-27 in 2008
was measured to be between 1.9 and 3.8 million, a decline from
between 2.4 and 5.4 million in the EU-25 in 2005” (Kovacheva
and Vogel, 2009).

6. Qual: Why? How?
• Births
- Effect of religiosity, available childcare, …
• Deaths
- Ikigai: “reason to get up in the morning”
• Migration
- Push/pull factors, assimilation, …

7. Opportunities for New methods
• Filling data gaps
– New data on migration, fertility, employment, …
• Explaining behavior
– Richer data, including networks and long-term history
• Predictive modeling
– Multi-modal forecasting
• Take a global perspective on things
– Facebook, Google, satellites know (almost) no borders
Goal is to augment, not replace, traditional approaches
Big Data is not a cure-all panacea

8. Rest of the Talk: Data-Centric
• Online advertising audience estimates
- Migration stocks, migrant assimilation
- Male mean-age-at-childbirth
- Ethics, limitations and challenges
• More non-obvious data sources
- Google Correlate, Followerwonk
- Even more non-obvious data sources
• Thoughts on interdisciplinary work

9. Facebook’s Audience Estimates

10. LinkedIn’s Audience Estimates
Female-to-male ratio LI users:
Female-to-male ratio LI users w/ AI:
0.94
0.27

11. Twitter’s Audience Estimates

12. Snapchat’s Audience Estimates
Female-to-male ratio SC users:
Female-to-male ratio SC users w/ STEM:
1.25
0.45

13. Google’s Impression Estimates
What they are actively researching or planning:
Baby and children’s products

14. Low-Cost Urban Census
http://fb-doha.qcri.org/

15. MIGRATION MONITORING

16. Expats Across US States
2014
2017

17. Expats Across Countries
2015
2017
regression line

18. Age-Specific Selection Biases

19. 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

20. QUANTIFYING MIGRANT
ASSIMILATION

21. 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:
?

22. 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

23. REAL-TIME MIGRATION MONITOR

24. Visualization of Venezuelan Exodus

25. Trends across time
Trends across space
Socio-economic insights
Report Hosted on “R4V: Operational Portal Refugee Situations”

26. STUDYING MALE FERTILITY RATES

27. Parenthood on Facebook

28. Mean Age at Child-Bearing
• Goal: fill data gaps on “mean age at child-bearing”

29. Out-of-Sample Predictions
Male MAC predictions for countries w/o ground truth

30. LIMITATIONS AND CHALLENGES

31. Ethical Challenges
• Privacy
– Was possible to obtain PII until early 2018 [Venkatadri
et al., 2018]
– Audience estimates for “custom audiences” no longer
supported
– The k in k-anonymity has been increased
• Vulnerable populations
– Was possible to exclude minorities from ads
– Was possible to target based on likely diseases
– Still targetable through proxy interests
We only use aggregate, anonymous data without
interacting with any user

32. Limitations: Selection Bias
Aren’t you just studying FB/LI/… vs. the “real
world”?
• If we understand the selection bias, we can
model it and de-bias the estimates
– Non-response biases in surveys
– Usual signal in a prediction model
– Non-random fake/duplicate accounts could
become problematic depending on domain
• Even if “only” LI, still real world implications
– LI used for hiring and to find keynote speakers

33. Limitations: Black Box
Who knows how FB’s classifier labels “expats” or
SC’s classifier labels “math enthusiasts”?
• Use as signal, not as ground truth
– Empirically, highly predictive of “proper” definition
– Unified definition can be a plus
• Incentives are in the right place
– Companies try to provide values to advertisers and,
hence, are incentivized to have correct labels
• Inconsistencies over time problematic
– In March 2019 FB changed its “expat” classifier

34. Limitations: No Longitudinal Data
None of the services provide information on
running a hypothetical ad campaign in the past
• No historical data sets of audience estimates
exist
– Hard to do causal inference (natural experiments)
• Similar to Twitter streaming API
– The best time to start collecting data is 20 years
ago. The second best time is today.

35. Limitations: What about Myspace?
Services come and go and FB et al. might
become obsolete
• Only useful for understanding and modeling
processes with current relevance
Usage patterns change over time
• FB of 2009 unlike FB of 2019.
• Users might become more privacy concerned.
• Re-validate and re-train your model over time.

36. MORE NON-OBVIOUS DATA
SOURCES

37. Google Correlate and Fertility
Discover search terms correlated with different fertility rates across US
states
https://www.google.com/trends/correlate/search?e=id:f7PU4mFDWV-
&t=all
Remove terms with no conceivable link to sex, pregnancy or maternity

38. Predicting Spatial Variability
• Performance of the regression models using
leave-one-out cross-validation. SMAPE is in [%], RMSE
values are multiplied by 1,000.
Use the previous terms to build
models predicting state-level fertility
rates
All these models make predictions
based on linear combinations of
search intensity
Goal: apply these spatial models
across time

39. Learning Across Space, Predicting Across
Time
• Temporal trend when applying the “teen” model
across time. Values are rescaled to a maximum of 1.0.
Pearson r correlation across 2010-2015 when using
the spatial model to predict trends across time.

40. Followerwonk and Gender Roles
(mother|mom) of … (father|dad) of …
… (girls|daughters) 1,257 303 1,560
… (sons|boys) 941 545 1,486
2,198 848
Location: (us|usa|united states)
https://followerwonk.com/bio/?q=(father|dad)%20of%20(sons|boys)&l=(us|usa|united%20states)

41. More Creative Data Sources
Online genealogy
- see how marriage mobility has changed
Online obituaries
- monitor patients discharged from hospital
Google Street View
- parked cars tell income and political orientation
https://sites.google.com/site/digitaldemography/

42. Creative Offline Data

43. Closing Thoughts
• Receptive collaborators (re selection bias)
• Publication venue (re career considerations)
• Data for Development (re practical use)

44. Again, amazing collaborators!
• Natalie Adler, Musa Al-Asad, Francesco Billari,
Antoine Dubois, Masoomali Fatehkia, Harsh
Gandhi, Manuel Garcia, Kiran Garimella, Krishna
Gummadi, Karri Haranko, Ridhi Kashyap, Yelena
Mejova, Alfredo Morales, Fabrizio Natale, Joao
Palotti, Tejas Rafaliya, Francesco Rampazzo, Marzia
Rango, Vedran Sekara, Vatsala Singh, Spyridon
Spyratos, Bogdan State, Reham Tamime, Michele
Vespe, Jeffrey Villaveces, Agnese Vitali, Emilio
Zagheni, …

45. Thanks!
Interested in a collaboration? Get in touch:
iweber@hbku.edu.qa
References and full texts: https://ingmarweber.de/publications/