This document presents a method for visualizing co-retweeting behavior during political debates to recommend relevant real-time content on Twitter. It analyzes co-retweeting patterns to create a graph that reveals relationships between influential accounts. This graph can be used to recommend additional accounts to follow during debates based on ones a user already follows or retweets. The approach focuses on accounts generating original content rather than just aggregating content. It aims to surface less popular but interesting voices discussing the debates.

1. S A M A N T H A F I N N A N D E N I M U S T A F A R A J
W E L L E S L E Y C O L L E G E
P R E S E N T E D A T M S M 2 0 1 3
C O - L O C A T E D W I T H A C M H Y P E R T E X T 2 0 1 3
P A R I S , F R A N C E
Visualizing Co-Retweeting
Behavior for Recommending
Relevant Real-Time Content

2. “Thomas Jefferson used newspapers to win the
presidency, F.D.R. used radio to change the way he
governed, J.F.K. was the first president to understand
television, and Howard Dean saw the value of the
Web for raising money…
But Senator Barack Obama understood that you
could use the Web to lower the cost of building a
political brand, create a sense of connection and
engagement, and dispense with the command and
control method of governing to allow people to self-
organize to do the work.”
- New York Times Article, How Obama Tapped Into Social Networks’ Power
How to become the US President

3. The US Presidential Debates
 Debates important events in the election
 Second only to Super Bowl in TV watch
 Since the first televised debates in 1960, many
defining moments people remember from the
debates
 Al Gore rolling his eyes and sighing
 Reagan “There you go again”
 Bush Sr. looking at his watch

4. Memes During the 2012 Debates

5. Pew: 1/10 of viewers are dual-screeners

6. Source: Dispatch From the Denver Debate (blog.twitter.com)
Watching on Twitter: First Debate

7. Watching on Twitter: Second Debate
Source: Twitter at the Town Hall Debate (blog.twitter.com)

8. Watching on Twitter: Third Debate
Source: The Final 2012 Presidential Debate (blog.twitter.com)

9. Follow the Debate Through Hashtags (Or try to)
Interactive Chart
There are too many hashtags being created, and rising and falling in popularity
during the debate to follow them all.

10. User Number Followers Tweets during the
3rd debate
ladygaga 30,572,024 2
BarackObama 21,206,234 9
YouTube 18,484,946 1
twitter 13,993,216 1
JimCarrey 9,256,715 1
cnnbrk 9,051,349 4
nytimes 6,350,936 8
CNN 6,287,257 5
PerezHilton 5,742,938 8
MTV 5,725,897 2
Or by Following Famous Twitter Accounts
Popular, celebrity Twitter accounts are
NOT the ones generating content during the debates.

11. Does More Followers Mean More Retweets?

12. Single person has a limited view
 It would be impossible for a single person to discover
and consume all the content about the debates
(especially while trying to watch them)
 Solution?
 Human computation

13. Human Computation
 Using humans as computers to improve intelligent
algorithms
 Retweeting as recommendation
 Already existing Twitter construct
 Utilizing it for a new purpose

14. uA uB uC uD
u1 u2 u3 u4 u5
. . . . .
. . . . .
. . . . .
(tweeters)
(tweets)
(retweeters)
Going from a tweeting model…

15. To the Retweet Matrix…
uA uB uC uD
u1 2 1 0 0
u2 0 1 0 0
u3 1 0 1 0
u4 0 1 1 1
u5 0 0 1 2
Tweeting Users (items)
RetweetingUsers(users)

16. uA uB uC uD
uA 2 1 1 0
uB 3 1 1
uC 3 2
uD 2
To the Co-Retweet Matrix
Tweeting Users
TweetingUsers

17. Co-Retweet Visualization
 Nodes represent top
retweeted accounts
 uA – uD
 Edges mean that the
connected nodes have
been co-retweeted
 Weighted by how many
users have co-retweeted
the two nodes
 Created using Gephi

18. Step 1: Layout
 Force Atlas
 Algorithm developed by
Gephi
 Force Directed layout
 Attraction between
connected nodes
 Repulsion between
unconnected nodes
 Nodes with stronger
connections (more edges,
heavier weight) are
attracted to each other
Source: ForceAtlas2, A Graph Layout Algorithm for
Handy Network Visualization

19. Step 2: Community
 Modularity Algorithm to
assign groups
 Detects communities
within a network
 Each community has a
different color
Source Fast unfolding of communities
in large networks

20. Step 3: Node Rank
 Eigenvector centrality
 Measures the influence of
a node in the network
based off the connections
with that node
 Similar to Google’s
PageRank algorithm
 Nodes are made larger
and darker based on
higher centrality values

21. Co-Retweet Graph of 2nd Debate
Interactive Graph

22. Co-Retweet Graph of 3rd Debate
Interactive Graph

23. Why a Co-Retweeting Model?
 Co-retweeting focuses only on the content creators
 Reveals the perceived
relationships between
accounts
 Unbiased media accounts

24. Co-Retweet Network vs. Retweet Network
 Retweet network
contains three
types of accounts
 Users who generate
original content
 Users who
aggregate content
by retweeting other
sources
 Users who do both
 Authorities vs.
Hubs
Retweeted
Only
48.0%
Wrote
Original
Tweets
Only
41.2%
Both Wrote
and
Retweeted
10.8%

25. Authorities Hubs
 Accounts writing
tweets
 Creating original
content
 Accounts retweeting
content
 Aggregating data from
lots of sources
 Sifting through and
picking out content
they find worth
recommending
Comparing Content Creators to Retweeters

26. Applications of Recommender System
 Given the set of users a person follows or has retweeted
 Recommend additional users to follow during the
debates
 Useful for people who are not savvy with Twitter
 Don’t know the users to follow
 Or don’t generally focus on political content, only interested during
election season
 Focus on users who are creating interesting content
during the debates
 Might not be the users with millions of followers
 Or they don’t regularly tweet about politics
 Have a record of the most interesting content of the
debate, to browse based on your own interests

27. Are these accounts still tweeting about politics?
Politically
Active
34.6%
Nonpolitical
65.4%
Of the top 1,500 retweeted accounts for the 16th and the 22nd, how many are
still generating relevant political content?
Source: Finn, S., and Mustafaraj, E. 2012. Learning to Discover Political Activism in the Twitterverse.
In KI-Künstliche Intelligenz 27 (1), 17-24

28. Conclusion
 Massive amount of data on Twitter during the
debates
 Human computation to create recommender system
 Co-retweeting connections reveal perceived
relationships between accounts
 Recommender system allows for easier and more
meaningful consumption of data in real time

29. Future Work

30. Highlight the Accounts You Follow

31. Highlight the Accounts You Follow

32. Highlight the Accounts You Follow

33. Highlight the Accounts You Follow

34. Highlight the Accounts You Follow