1. WHO ARE YOU?...HONESTLY!
A study on inferring missing attributes in social networks
Zeinab Mahdavifar
Advisor: Prof. Martine De Cock

2. We are living in social network era.
Each social network help us in one way. we are in touch with our old finds(Facebook), we find the company with closest work culture to us(LinkedIn), and can find help from sources we never could find otherwise(Mechanical Turk).

3. 3,035,749,340 online users 2,276,812,005 social network users
% of Internet users who use the following social media by year
74% of all online adult population use social networking sites.
one third of them are on facebook, and almost equal portions use other social networks such as Twitter, Instagram, Pinterest and LinkedIn. That is why 52% of online adults now use two or more social media sites.

4. 3,035,749,340 online users 2,276,812,005 social network users
74% of all online adult population use social networking sites.
one third of them are on facebook, and almost equal portions use other social networks such as Twitter, Instagram, Pinterest and LinkedIn. That is why 52% of online adults now use two or more social media sites.

5. … and we can use it for: Targeted Advertising Reputation Monitoring
Sexual Predation Detection

6. This is not final

7. This is not final

8. Predictive Models
Using like/ Comment/ Status
Using Friendship Links

9. Problem: Inferring age and gender in social network
Approach: Using friendship links between users
Input: friendship links of 3 million users in Netlog
Algorithm: Label Propagation (a community detection algorithm)
Output: All ages and genders of that 3 million users

10. Problem: Inferring age and gender in social network
Approach: Using friendship links between users
Input: friendship links of 3 million users in Netlog
Algorithm: Label Propagation (a community detection algorithm)
Output: All ages and genders of that 3 million users

11. We start from users with known attributes in the network = colored users
Lets have an example
Imagine we want to know the age of all our network. We know the age of two users in the network that are shown in color. We use an iterative approach and at each approach we propagate the age from known users to unknown ones.

12. At each iteration, known users pass their label to neighbors.

13. Till the whole network is labeled….
Till the whole network is labeled

14. F M
Andi F F
Suzie

15. F M M F F M F F

16. Results with info of 15% of users minimum error ~ 6 years
age
gender
with info of 15% of users
minimum error ~ 6 years
with info of 10% of users
accuracy ~ 80%

17. Results with info of 15% of users minimum error ~ 6 years
age
gender
with info of 15% of users
minimum error ~ 6 years
with info of 10% of users
accuracy ~ 80%

18. misrepresentation of age/gender Sexual Predation Detection

19. Public | Private
missing attributes vs. misrepresentation of attributes

20. Public | Private
publically available data to privately accurate information
Our model uses publicly available data to find privately accurate information about a user, and in case of suspicious behavior informs law enforcement authorities.
This helps supporting
communal safety and social well-being. 

21. Time’s Up!
About you:
Zeinab Mahdavifar
Masters of Computer Science
Institute of Technology, UW Tacoma
@ZeinabFar
I am a fan of:
Data Science Big Data CENTER FOR DATA SCIENCE WomenWhoCode New Technologies Bloomberg Cooking Cycling Puzzles Volunteering