public

1. FriendOrMate?
Where does your customer live?
Julia Tsitron
Insight Data Science

2. ????
“mate”
“friend”

3. location
Advertisers don’t want to waste money targeting people who don’t have
access to their product

4. ~ 1
million
80% / 20%
Training / Testing
or
Training::Cross Validation::Testing
Naive Bayes:
Prediction Accuracy: 79%
Logistic Regression:
Prediction Accuracy: 79%

5. soccer
roommate
math
color
sports
realize
mom
friend
colour
sport
maths
football
rubbish
realise
mum
mate
0 11
What did the model learn?

6. The Takeaway: We can learn user attributes from tweets!
location

7. The Takeaway: We can learn user attributes from tweets!
location
Free! Limitless!

8. location income
census data
Free! Limitless!
The Takeaway: We can learn user attributes from tweets!
“lightly supervised” learning

9. location income age, gender, ...
census data
Free! Limitless!
The Takeaway: We can learn user attributes from tweets!
“lightly supervised” learning

10. Julia Tsitron
Ph.D., Computational Biology and Molecular Biophysics
M.A., Physics
B.A., Physics

11. Feature Importance

12. A Few Case Studies
David Cameron Hillary Clinton

13. A Few Case Studies
Hilary Mason

14. A Few Case Studies, continued