Presentation at the Annual Meeting of the Population Association of America (PAA 2019) in the Session Using Social Media in Population Research (http://paa2019.populationassociation.org/sessions/128). See https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211350 for the full paper.

1. Correlated Impulses
Using Facebook Interests to Improve
Predictions of Crime Rates in Urban Areas
Masoomali Fatehkia, Dan O’Brien, Ingmar Weber
@ingmarweber
PLOS ONE 14(2): e0211350, February 2019
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211350

2. The Team
Masoomali Fatehkia, QCRI
Daniel O’Brien, NEU

3. ADVERTISING AUDIENCE ESTIMATES

4. https://business.facebook.com/adsmanager/creation/

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

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

7. http://fb-nyc.qcri.org

8. Advertising Audience Estimates
+ Global reach with over 2 billion users
+ FB, LinkedIn, Google, Snapchat, IG, ...
+ Real-time estimates
+ Uses anonymous and aggregate data
+ Gender, age, location, country of origin, ….
+ Non-traditional attributes such as interests
+ Accessible through APIs

9. Advertising Audience Estimates
- Black box on how attributes are inferred
- Usage patterns change over time
- Black boxes changes over time
- Only includes people who are online
- Somewhat coarse (was 20, now 1,000)
- Possibility to locate vulnerable populations

10. MODELING CRIME RATES

11. Background for Exploration
• Ecological theories for community-level variation (social
norms, economic inequalities, …)
• Individual level characteristics (self-control, risk taking, …)
• Could the spatial distribution of individual-level processes
explain the variation in crime between neighborhoods?
• Can we pick up the traits of perpetrators?
• Can we pick up the traits of victims
Exploratory study

12. Data Sources
• 9 large cities (out of 25) with “usable” crime data
• Three data sources: (i) Facebook data, (ii) ACS
2015 data, and (iii) crime incident data
• ZIP codes ≈ ZCTA, except parks/office buildings
– ZCTA codes > 10k population in ACS 2015 (65x)
– FB population < 1.5 ACS population (41x)
• Resulted in 432 ZIP codes
• Bias in which ZIP codes remain (bigger/denser)

13. Facebook Interests Data
• Online dating and relationship status
– m/f in different relationship statuses
– Planned: “open relationship” – too sparse
• Online gaming
– Action games, card games, FPS, …
• Music genres
– Hip hop, blues, electronic, country, …
• Movie genre
– Action, horror, comedy, ..

14. ACS 2015 Demographic Data
% of population aged 15-19
% of population aged 18-24
Median age
% of population one race White
% of population one race Black or African-American
% of households on food stamp benefits
Median family income
% households with income > 150K
% households with income <= 25K
% of population 18-24 with bachelors or associates degree
% of population 18-24 with less than high school degree
% of population 25+ with less than high school degree
% of population 25+ with bachelors or higher degree
RaceAgeIncomeEducation

15. Crime Rate Data
• Use geo-coded incident data
• Aggregate by ZIP codes
• Standardized cities reporting to National
Incident Based Reporting System (NIBRS)
• Compute rates per 100k (using ACS’15 pop)
• Reported crime rates, not crime itself
• Policing strategies
https://www.gimletmedia.com/reply-all/127-the-crime-machine-part-i
https://www.gimletmedia.com/reply-all/128-the-crime-machine-part-ii

16. Data Availability
https://doi.org/10.7910/DVN/MGRADP

17. Predictive Performance Across Age/Gender
Performance is mean absolute error (MAE) for the crime rate per 100k population
All aged 18+ always gave best CV performance
All other experiments done using only this (broad) subset
Using regularized linear regression (LASSO) with FB-only
features to identify which age/gender group is most predictive

18. Factor Analysis
• Too many features are selected (up to 23),
even with LASSO
• Group features into “factors” for sparser, more
interpretable model
• Do a factor analysis for each of (i) relationship,
(ii) music, (iii) movie, and (iv) gaming features

19. Music-Related Factors
Factor analysis used ‘factanal’ R package on the feature correlation matrix.
Hip-hop,soulandrelated

20. Model Performance
Demographics only Facebook only Demogr. + Facebook
Assault .639 .488 .604 .437 .656 .511
Burglary .562 .083 .601 .163 .598 .157
Robbery .558 .411 .528 .371 .581 .441
“Facebook only” not bad
“Demogr. + Facebook” is best
Adjusted R^2Marginal gain over city dummies

21. Modeling Assault Crime

22. Spatial Variation in Assault Crime

23. Discussion
• Based on “18+ all”: FB’s predictive power lies
less in the behaviors of particular individuals
and more in the overall behavioral ecology
• Hip hop: the only factor that remained in all
models. Indicates culture of crime or of
increased policing?
• “… correcting for demographics”: or just
unmodeled variation (re interaction terms)?
• What else?
Rock, Rap, or Reggaeton? Assessing Mexican Immigrants’
Cultural Assimilation Using Facebook Data
Today, Session 168, 1:00-2:30 PM, Brazos/206

24. See you in Doha for SocInfo’19!
Speakers include:
Francesco Billari, Emre Kiciman, Katy Börner, Yelena Mejova, Luca
Maria Aiello, Aniko Hannak, and Giovanni Luca Ciampaglia
Submit papers/abstracts by April 15, 2019
Submit tutorials/workshops by April 30, 2019

25. Thanks!