1. Classifying Twitter Users
David Horn
Cal Poly

2. How can you classify social
network users?
What kinds of traits do they have?
How does information spread on
twitter?

3. Power graph analysis kinda fails
Twitter is no more a social network than
IRC
Following is cheap

4. This just in: following spammers gets
you spammed
“When I unfollowed 106,000 people all
my spam just stopped. Dead. “

5. Mavens
Discover new information
Connectors
Spread information to new communities

6. Ineffectual
Heavily engaged, but do not get a strong
response from the community
Consumer
Less engaged with the service

7. Engagement Metrics
Engaged - How often others @mention
a user
Engaging - How often a user
@mentions others

8. Twitter User Engagement
80
70
60
50
Engaged by
40
30
20
10
0
0 10 20 30 40 50 60 70 80
Engaging

9. Twitter User Engagement
80
70
60
50
Engaged by
40
30
20
10
0
0 10 20 30 40 50 60 70 80
Engaging

10. Twitter User Engagement
80
70
60
Mavens
Number of users engaging
50
Connectors
40
30
20
10
0
0 10 20 30 40 50 60 70 80
Number of users engaged

11. Twitter User Engagement
80
70
60
Number of users engaging
50
40
30
Ineffectual
20
10
0
0 10 20 30 40 50 60 70 80
Number of users engaged
Community-at-large

12. Convert that to polar coordinates
(x,y) => (r,θ)
r
engaged
θ
engaging

13. Magnitude
r  engaged 2  engaging 2
How engaged is this user with the
service?

14. Angle
 engaged 
  sin 
1

 r 
What kind of a user this is

15. 90°
Maven
60°
Connector
If magnitude > 15
40°
Ineffectual
0°

16. Meme flow:
Larger
community
Connector
Maven
Source
(anywhere)
Time

17. For any particular meme, you can
expect a traffic breakdown:
• 10% from trend leaders
• 20% from community leaders
• 30% from the overall community
• 40% from ineffectual users

18. Reach
Reach = r/engaging*followers

19. Chance a meme will spread
P(spread) = θ/90

20. http://engagerank.com
Try it out!

21. T