Slides from ICWSM'17 workshop on Social Media for Demographic Research (https://sites.google.com/site/smdrworkshop/program). Data sets include Facebook's ad audience estimates, Google Correlate, online genealogy and much more. Contact Ingmar directly to learn more.

1. Not-So-Obvious Online
Data Sources for
Demographic Research
Ingmar Weber
@ingmarweber
https://sites.google.com/site/smdrworkshop/

2. Targeted Advertising as a Digital Census
All the Internet giants make money with targeted advertising
It’s in their commercial interest to “understand” their users
Rich data on both demographic and behavioral attributes
Usually not available for outside researchers, but …
Some aggregate “audience estimates” available for advertisers:
How many users/impressions match criteria X?
Supported by (at least) Facebook, Twitter, and Google

3. Facebook’s Advertising Reach Estimates
https://www.facebook.com/ads/manager/creation/creation/
https://developers.facebook.com/docs/marketing-api/buying-api/targeting/v2.8
Easy-to-Use Python code
https://github.com/maraujo/pySocialWatcher
Created by Matheus Araujo at QCRI
Contact me if you want to (i) know about important
details, and (ii) know what’s in the pipeline.

4. Sneak Preview: Estimating Stocks of Migrants
Joint work with Emilio Zagheni and Krishna Gummadi. Currently under review.

5. Twitter’s Advertising Reach Estimates
https://dev.twitter.com/ads/reference/1/get/
accounts/%3Aaccount_id/reach_estimate
https://ads.twitter.com/login

6. Google’s Advertising Reach Estimates
https://support.google.com/adwords/answer/2475441?hl=en
https://developers.google.com/adwords/api/docs/guides/traffic-
estimator-servicehttp://adwords.google.com/

7. Using Online Ads to Reach Migrants
Only described use as a passive data source. But can be used as an active
outreach channel. Examples below.
“Migrant Sampling Using Facebook Advertisements A Case Study of Polish:
Migrants in Four European Countries”; S. Pötzschke, M. Braun; 2016
“Using Internet to Recruit Immigrants with Language and Culture Barriers for
Tobacco and Alcohol Use Screening: A Study Among Brazilians”; B. H. Carlini, L.
Safioti, T. C. Rue, L. Miles; 2014
“Reaching and recruiting Turkish migrants for a clinical trial through Facebook: A
process evaluation”; B. Ü. Ince, P. Cuijpers, E. van 't Hof, H. Riper; 2014

8. Google Trends on Steroids
Google Trends does not provide demographic information
Get DMA-level demographic information (race, income, …)
Join with DMA-level Google Trends information
Can potentially give “average income of a web search query over time”
But often sparsity problems, with data only showing for bigger cities (=> bias)
See “The cost of racial animus on a black candidate: Evidence using Google
search data”, Seth Stephens-Davidowitz; Journal of Public Economics; 2014
Also: “Demographic information flows”, Ingmar Weber, Alejandro Jaimes; CIKM 2010

9. “Fertility and its Meaning: Evidence from Search Behavior”
Jussi Ojala, Emilio Zagheni, Francesco C. Billari, Ingmar Weber
ICWSM; 2017
https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15579
Example study using Google Correlate

10. Study Goals
(i) detect evidence for different contexts surrounding different types of fertility;
Teen, low/high income, (un-)married, …
(ii) model regional variation across states for different fertility levels;
What distinguishes Alabama from California from New York?
(iii) track temporal changes in fertility across time.
Train a model across space, predict across time.

11. Different Contexts of 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

12. 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

13. 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.

14. “Quantitative analysis of population-scale family trees using
millions of relatives”
Joanna Kaplanis, Assaf Gordon, Mary Wahl, Michael Gershovits, Barak Markus,
Mona Sheikh, Melissa Gymrek, Gaurav Bhatia, Daniel G MarArthur, Alkes Price,
Yaniv Erlich
bioRxiv; 2017
http://biorxiv.org/content/early/2017/02/07/106427
Example study using an online genealogy database

15. Online Genealogy Data - Again
13 million people, after
cleaning, in a single pedigree
Small sample of mitochondria
and Y-STR haplotypes (not
discussed)
Also location information.
Cleaned, de-identified data
available at:
http://familinx.org/

16. Geographical Distribution of Data (Place of Birth)
Pre 1800 Post 1800

17. Mortality and City Growth
Their model (red) validated against
previous models (Oeppen & Vaupel, black)

18. Mobility Over Time
And a lot more! Check out the paper.
Median migration distance in North American
born individuals as a function of time.
Red: mother-offspring,
blue: father-offspring,
black: marital radius.
Dots represent the data before smoothing.

19. “A novel web informatics approach for automated
surveillance of cancer mortality trends”
Georgia Tourassi, Hong-Jun Yoon, Songhua Xu
Journal of Biomedical Informatics; 2016
http://www.sciencedirect.com/science/article/pii/S1532046416300181
Example study using online obituaries

20. Crawling Cancer-Related Obituaries
Use a web search engine to get seeds
for queries such as “breast cancer
obituary, New York”
Example
Then post-filter
Then lung vs. breast cancer
Then infer age and gender

21. Cancer Mortality Rates from Online Obituaries
Percent of lung cancer deaths per age
group based on SEER data and
obituaries for both genders.
Annual female breast cancer death rates based on
obituaries and on National Vital Statistics Report
(NVSR) for 2008–2012.

22. “From Migration Corridors to Clusters: The Value of Google+
Data for Migration Studies”
Johnnatan Messias, Fabricio Benevenuto, Ingmar Weber, Emilio Zagheni
ASONAM; 2016
http://ieeexplore.ieee.org/document/7752269/
Example study using public Google Plus profiles

23. 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

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

25. 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

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

27. 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

28. Enriching Your Data
Demographic Inference 101

29. Demographic Inference – Name Dictionaries
First name gender dictionaries:
https://ideas.repec.org/c/wip/eccode/10.html
http://gender.io/
Contact me for dictionary in “International Gender Differences and Gaps in Online
Social Networks”
Ethnicity Dictionary:
https://www.census.gov/topics/population/genealogy/data/2010_surnames.html
Also see “Inferring Nationalities of Twitter Users and Studying Inter-National Linking”

30. Demographic Inference – Image-Based Inference
Face++ Cognitive Services
https://www.faceplusplus.com/face-detection/
Microsoft Cognitive Services
https://www.microsoft.com/cognitive-services/en-us/computer-vision-api

31. Demographic Inference – Build Your Training Data
FollowerWonk by Moz
https://moz.com/followerwonk/bio
https://moz.com/followerwonk/bio/?q=(38-yr%7C38-yrs%7C38%20years)%20old%0A%0A

32. Questions, Comments, Thoughts?
https://sites.google.com/site/digitaldemography/