Presentation given during panel on "Closing inequalities and gender divides" at UNESCO Mobile Learning Week on March 28. Program at https://en.unesco.org/sites/default/files/unesco-mlw2018-programme-en.pdf#page=11. References at https://ingmarweber.de/publications/. Coming soon: http://www.digitalgendergaps.org/. Joint work with Ridhi Kashyap and Masoomali Fatehkia.

1. Tracking Digital Gender Gaps
@ingmarweber
Research Director for Social Computing
Qatar Computing Research Institute
UNESCO Mobile Learning Week, March 28
Panel #3 “Closing inequalities and gender divides”

2. Gender Differences in Facebook Usage

3. Gender Differences in Facebook Usage

4. Gender Differences in Facebook Usage

5. Gender Differences in Facebook Usage

6. Estimating Internet Access Gender Gaps
%-age of women with internet access
%-age of men with internet access
%-age of women who are on Facebook
%-age of men who are on Facebook
ITU data 2015
84 countries
Our estimates 2017
152 countries
“Using Facebook Ad Data to Track the Global Digital Gender Gap”
Masoomali Fatehkia, Ridhi Kashyap, Ingmar Weber
World Development, Vol 107, pp 189-209, 2018

7. Gender Differences in Interests

8. Gender Differences in Interests

9. Gender Differences in Interests

10. Gender Differences in Interests

11. Gender Differences in Interests

12. Gender Differences in Interests

13. International Expats on Facebook

14. Sub-National Estimates

15. Sub-City Estimates

16. Audience Estimates on LinkedIn

21. Everything you saw you can do yourself at no cost
https://www.facebook.com/ads/
https://www.linkedin.com/ad/
* Follow the steps for creating an ad campaign
* Stop at the audience selection step
Homework

22. Summary: Advertising Audience Estimates
+ Global reach with over 2 billion users
+ FB, LinkedIn, Google, Twitter, Snapchat, Instagram, …
+ Real-time estimates
+ Uses anonymous and aggregate data, no personally identifiable information
+ Disaggregation by gender, age, location, nationality, …
+ Includes non-traditional attributes such as interests and skills
- Proprietary black box on how attributes are inferred
- Needs modeling for bias correction, don’t use at face value
- Needs periodic adjustments of the models as usage patterns change

23. Coming Soon
www.digitalgendergaps.org
Portal for monthly estimates of digital gender gaps
Thanks for support from Data2X!
Lead PI: Ridhi Kashyap, University of Oxford

24. Merci!
Contact me for references and data visualizations
@ingmarweber
iweber@hbku.edu.qa