Webinar hosted by Georgetown Global Human Development Program. Recording of the session is available at https://georgetown.zoom.us/rec/play/vMElIrir-mg3HYWWtwSDVP4rW461Jqis2iAZ8qUEyErnBiQGNFqlb7tEM-ofDv5GgHLYljjfYoBR0852?continueMode=true&_x_zm_rtaid=8UtYz48MSICS36P_gsSTDg.1588250263912.a1e6098b19d99104d94a3a1063c22f70&_x_zm_rhtaid=503 Original eventbrite at https://www.eventbrite.com/e/monitoring-migration-using-social-media-data-an-introduction-tickets-102687830064

1. Monitoring Migration Using Social
Media Data: An Introduction
Hosted by Georgetown Global Human Development Program
Ingmar Weber
April 29, 2020

2. Amazing Collaborators! (Alphab.)
• Natalie Adler, Musa Al-Asad, Carlos Callejo,
Masoomali Fatehkia, Manuel Garcia, Kiran
Garimella, Krishna Gummadi, Alfredo Morales,
Fabrizio Natale, Joao Palotti, Francesco
Rampazzo, Marzia Rango, Vedran Sekara, Elise
Sonne, Spyridon Spyratos, Bogdan State,
Michele Vespe, Jeffrey Villaveces, Agnese Vitali,
Emilio Zagheni, …

3. Experience Reports
• Geo-tagged tweets
• Google+ and “places lived”
• Facebook advertising data
• LinkedIn advertising data
• Urban mobility (re covid-19)

4. GEO-TAGGED TWEETS

5. Show tweet map

6. Data Collection
• Used Twitter streaming API filter for geo-tagged tweets
from OECD countries
• Pick 3,000 users per country, get their tweets
• Estimate out-migration and oversample countries
where migration is rare
• Get data for ~500K users
• Activity thresholding: 3+ tweets in four-months
windows, May 2011->April 2013
• Left with ~15K users -> Small!

7. Estimated Out-Migration Rates

8. Difference-in-Differences
• Out-migration rates clearly an overestimate
• Non-representative user set
• Selection bias is changing over time
• Focus on between-country differences
D D

9. Results
(Soft) Validation: Ireland out-migration rate grew by 2.2% 2011 -> 2012, more than most
countries (Irish Central Statistics Office)
Mexico also sees a reduction in out-migration (Pew Research Center)

10. Another Data Collection
• Same seed user set as before
• Collected more recent tweets
• Dropped conditions on coverage
• ~62k users with at least some tweets in the US

11. Duration and Interval
duration interval
How long have you been in X? Where were you […] years ago?

12. The Duration-Interval Interplay
Plot of estimated migration rate as a function of
interval and duration length. Rates were estimated
fixing July 1st 2012 as the starting point.

13. GOOGLE+ PLACES LIVED

14. Beyond Origin-Destination Migration Analysis
• I’m a German citizen living in Qatar. So did I
migrate from Germany to Qatar?
• Yes, according to Qatari border control.
• But: Germany (78->99), United Kingdom (99->03),
• Germany (03->07), Switzerland (07->09),
• Spain (09->12), Qatar (12->now)
• Use the “places lived” on Google+
• In 2012, no “currently”, just set of places
• Get tuples of co-lived countries

15. Flows/Corridors vs. Tuples/Clusters
This is what border
control can obtain
(with directionality)
This is what the Google+ “places lived” provides

16. Expected Cluster Frequencies
• Lots of migrant flows on (A,B), (A,C) and (B,C) =>
expect lots on (A,B,C)
• “Expect” = rank clusters according to:
• min(freqAB; freqAC; freqBC) * mean(freqAB;
freqAC; freqBC)
• Best performing ranking approximation (Kendall
.565, Spearman .754)
• Look at outliers and try to explain those

17. Outlier Frequencies
• Look at “expected
rank – actual rank”
• Middle 20%: “close to
expected”
• Top 20%: “higher
than expected”
• Low 20%: “lower than
expected”

18. Feature Analysis
More than expected:
• (Spain, France, Italy)
• (UAE, India, Singapore)
Less than expected:
• (Brazil, Mexico, USA)
• (Canada, China, UK)
Most discriminative features for 3-class distinction

19. FACEBOOK ADVERTISING DATA

21. Expats Across US States
2014
2017

22. Expats Across Countries
2015
2017
regression line

23. Age-Specific Selection Biases

24. 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

25. Venezuelan Exodus: Motivation
• Large outpouring of migrants and refugees
– Mostly into Colombia and neighboring countries
• Lack of reliable survey data on the crisis
– Irregular migrants
– Fear of persecution
• Goal: Help improve humanitarian response
– Detect temporal trends with low latency
– Insights into spatial distribution
– Insights into socio-economic status

26. Increasing Trends from 2016

27. Validation w/ (Few) Available Data
Registro de Administrativo de
Migrantes Venezolanos (RAMV)
- Jun, 2018
Facebook - Jun, 2018
Kendall's τ = .71 (n=31)

28. Previously Unavailable Estimates
Brazil - Facebook. Feb 2019 Peru - Facebook. Feb 2019 Ecuador - Facebook. Feb 2019

29. Sub-City Distribution
Boa Vista in Roraima, Brazil

30. Predicted Income Based on OS

31. Changes to Facebook’s Backend

32. Syrian Refugees in Lebanon
For 6 governorates and 770 cities
720k FB users “lives abroad” + AR
3.5M FB users overall
Only 157 cities with > 1000 FB users
Strong gender bias

33. OS-Type Predictive of Poverty?
Predicting: % below poverty line
Model variable CV performance (LOOCV)
R^2
% iOS device users 0.895
% high-end phones
(iphones/galaxy)
users
0.678

34. 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:
?

35. 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
• Importantly: should people assimilate?

36. LINKEDIN ADVERTISING DATA

37. 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, …

38. All age groups

39. 55+

40. 18-24

41. RECAP

42. Advertising Audience Estimates
+ 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 change over time
- No historic data available
- Risk of misuse

43. Selected Ongoing Work
• Using Twitter to Predict Social Network
Integration for International Migrants
– w/ Elise Wang Sonne at UN University and others
• Studying Inter-Generational Integration of
Hispanics in the US Through FB Surveys
– w/ Andre Grow at MPI Demographics and others

44. Covid-19 and mobility …

45. Thanks!
iweber@hbku.edu.qa
References and more at:
https://ingmarweber.de/publications/
https://www.slideshare.net/IngmarWeber
https://scholar.google.com/citations?user=3YDU
bP0AAAAJ