1. Twitter Community Discovery & Analysis Using Topologies
Andrew McClain
Karen Aguar

2. Outline Introduction Motivation Project Description
Community Discovery
Data Collection
Analysis & Application
Results
Why, What, How?

3. Introduction
Many people use services like twitter to stay in contact with groups in which they are members or to interact with other people with similar interests
These groups are considered “communities”

4. Community?
A network or group of nodes with greater ties internally than to the rest of the network
There are various derivations of a community:
Some communities are tightly bound together
Others are loose associations of people

5. Motivation
To classify these communities & find real world implications of their digital associations
Project Description:
Discovering communities & examining the properties of the graph to give us insight into the community itself.
Ex: Find the organizers of a hobby group by the twitter activity

6. Our Project Our project is composed of 2 main sections
Twitter community discovery
Analysis of the community graphs & its correlation to the real world community structure

7. Community Discovery
Collected data from a diverse number of individuals from known real-world communities
Generated graphs of the communities
Partitioned graphs based on in/out degrees to isolate the community

8. Community Discovery Communities: @CNN @AthensGroupRide @AthensChurch
@UniversityOfGA
@ChickFilA

9. Data Collection Relationships Modeled: Parameters
Followed By/ Following
Replies to
Mentions
Parameters
1.5 Levels
Limit # of people included in network
Most limited ~ 300

10. Analysis & Application
Manually reconstructed the hierarchy of the real-world known communities
Use Gephi to detect behavior patterns and structures in twitter communities
Shape, interconnectivity, how the information flows through it
Analyzed the relationships in the graphs against known community structures

11. Analysis via Gephi Gephi -- open source graph visualization platform
We used Gephi to isolate the community from the noisy background

12. Analysis via Gephi
After isolating the communities, labels were sized based on in-degree
The assumption is that the people who are listened to are followed most in the community
The spline on the right shows the scale of the labels
At this time, the analysis of importance is done visually

13. Results
What we found:
An interesting dichotomy between primarily online & primarily offline communities
“Celebrity” Noise Effect
Once a celebrity is introduced to a community, everyone follows them and they become a center individual in the community structure

14. Results Online Community: Offline Community
Athens Group ride --- Make predictions about who is / is not important (by looking at in-degree)
Athens Church – Most significant members are represented in the graph
A mega-church pastor introduces celebrity noise into the community
Offline Community
ChickFilA’s information distribution is largely a uni-directional relationship. It doesn’t receive much information.
Semi-Online Communities (in between)
CNN, UniversityofGa
Their graphs reveal information about the community structure such as large organizations involved, but not much about the individuals in the network

15. Results Athens Group Ride
Ty_Magner, Philgaimon, Joeyrosskopf are determined to be most influential

16. Results
University of GA

17. Results
Athens Church
Andy Stanley -celebrity effect

18. Results
Chick-Fil-A
Offline Community

19. Results
CNN
Extremely small community once filters are applied

20. Thank You! Questions? References:
Community Discovery in Social Networks: Applications, Methods and Emerging Trends
S. Parthasarathy, Y. Ruan, V. Satuluri [2011]
gephi.com
nodexl.codeplex.com
twitter.com