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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
“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
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
Memes During the 2012 Debates
Pew: 1/10 of viewers are dual-screeners
Source: Dispatch From the Denver Debate (blog.twitter.com)
Watching on Twitter: First Debate
Watching on Twitter: Second Debate
Source: Twitter at the Town Hall Debate (blog.twitter.com)
Watching on Twitter: Third Debate
Source: The Final 2012 Presidential Debate (blog.twitter.com)
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.
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.
Does More Followers Mean More Retweets?
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
Human Computation
 Using humans as computers to improve intelligent
algorithms
 Retweeting as recommendation
 Already existing Twitter construct
 Utilizing it for a new purpose
uA uB uC uD
u1 u2 u3 u4 u5
. . . . .
. . . . .
. . . . .
(tweeters)
(tweets)
(retweeters)
Going from a tweeting model…
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)
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
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
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
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
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
Co-Retweet Graph of 2nd Debate
Interactive Graph
Co-Retweet Graph of 3rd Debate
Interactive Graph
Why a Co-Retweeting Model?
 Co-retweeting focuses only on the content creators
 Reveals the perceived
relationships between
accounts
 Unbiased media accounts
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%
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
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
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
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
Future Work
Highlight the Accounts You Follow
Highlight the Accounts You Follow
Highlight the Accounts You Follow
Highlight the Accounts You Follow
Highlight the Accounts You Follow

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Visualizing Co-Retweeting Behavior for Recommending Relevant Real-Time Content

  • 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