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

391 views
314 views

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
391
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

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

  1. 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 JW E L L E S L E Y C O L L E G EP R E S E N T E D A T M S M 2 0 1 3C 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 3P A R I S , F R A N C EVisualizing Co-RetweetingBehavior for RecommendingRelevant Real-Time Content
  2. 2. “Thomas Jefferson used newspapers to win thepresidency, F.D.R. used radio to change the way hegoverned, J.F.K. was the first president to understandtelevision, and Howard Dean saw the value of theWeb for raising money…But Senator Barack Obama understood that youcould use the Web to lower the cost of building apolitical brand, create a sense of connection andengagement, and dispense with the command andcontrol method of governing to allow people to self-organize to do the work.”- New York Times Article, How Obama Tapped Into Social Networks’ PowerHow to become the US President
  3. 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, manydefining moments people remember from thedebates Al Gore rolling his eyes and sighing Reagan “There you go again” Bush Sr. looking at his watch
  4. 4. Memes During the 2012 Debates
  5. 5. Pew: 1/10 of viewers are dual-screeners
  6. 6. Source: Dispatch From the Denver Debate (blog.twitter.com)Watching on Twitter: First Debate
  7. 7. Watching on Twitter: Second DebateSource: Twitter at the Town Hall Debate (blog.twitter.com)
  8. 8. Watching on Twitter: Third DebateSource: The Final 2012 Presidential Debate (blog.twitter.com)
  9. 9. Follow the Debate Through Hashtags (Or try to)Interactive ChartThere are too many hashtags being created, and rising and falling in popularityduring the debate to follow them all.
  10. 10. User Number Followers Tweets during the3rd debateladygaga 30,572,024 2BarackObama 21,206,234 9YouTube 18,484,946 1twitter 13,993,216 1JimCarrey 9,256,715 1cnnbrk 9,051,349 4nytimes 6,350,936 8CNN 6,287,257 5PerezHilton 5,742,938 8MTV 5,725,897 2Or by Following Famous Twitter AccountsPopular, celebrity Twitter accounts areNOT the ones generating content during the debates.
  11. 11. Does More Followers Mean More Retweets?
  12. 12. Single person has a limited view It would be impossible for a single person to discoverand consume all the content about the debates(especially while trying to watch them) Solution? Human computation
  13. 13. Human Computation Using humans as computers to improve intelligentalgorithms Retweeting as recommendation Already existing Twitter construct Utilizing it for a new purpose
  14. 14. uA uB uC uDu1 u2 u3 u4 u5. . . . .. . . . .. . . . .(tweeters)(tweets)(retweeters)Going from a tweeting model…
  15. 15. To the Retweet Matrix…uA uB uC uDu1 2 1 0 0u2 0 1 0 0u3 1 0 1 0u4 0 1 1 1u5 0 0 1 2Tweeting Users (items)RetweetingUsers(users)
  16. 16. uA uB uC uDuA 2 1 1 0uB 3 1 1uC 3 2uD 2To the Co-Retweet MatrixTweeting UsersTweetingUsers
  17. 17. Co-Retweet Visualization Nodes represent topretweeted accounts uA – uD Edges mean that theconnected nodes havebeen co-retweeted Weighted by how manyusers have co-retweetedthe two nodes Created using Gephi
  18. 18. Step 1: Layout Force Atlas Algorithm developed byGephi Force Directed layout Attraction betweenconnected nodes Repulsion betweenunconnected nodes Nodes with strongerconnections (more edges,heavier weight) areattracted to each otherSource: ForceAtlas2, A Graph Layout Algorithm forHandy Network Visualization
  19. 19. Step 2: Community Modularity Algorithm toassign groups Detects communitieswithin a network Each community has adifferent colorSource Fast unfolding of communitiesin large networks
  20. 20. Step 3: Node Rank Eigenvector centrality Measures the influence ofa node in the networkbased off the connectionswith that node Similar to Google’sPageRank algorithm Nodes are made largerand darker based onhigher centrality values
  21. 21. Co-Retweet Graph of 2nd DebateInteractive Graph
  22. 22. Co-Retweet Graph of 3rd DebateInteractive Graph
  23. 23. Why a Co-Retweeting Model? Co-retweeting focuses only on the content creators Reveals the perceivedrelationships betweenaccounts Unbiased media accounts
  24. 24. Co-Retweet Network vs. Retweet Network Retweet networkcontains threetypes of accounts Users who generateoriginal content Users whoaggregate contentby retweeting othersources Users who do both Authorities vs.HubsRetweetedOnly48.0%WroteOriginalTweetsOnly41.2%Both WroteandRetweeted10.8%
  25. 25. Authorities Hubs Accounts writingtweets Creating originalcontent Accounts retweetingcontent Aggregating data fromlots of sources Sifting through andpicking out contentthey find worthrecommendingComparing Content Creators to Retweeters
  26. 26. Applications of Recommender System Given the set of users a person follows or has retweeted Recommend additional users to follow during thedebates 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 duringelection season Focus on users who are creating interesting contentduring 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 thedebate, to browse based on your own interests
  27. 27. Are these accounts still tweeting about politics?PoliticallyActive34.6%Nonpolitical65.4%Of the top 1,500 retweeted accounts for the 16th and the 22nd, how many arestill 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. 28. Conclusion Massive amount of data on Twitter during thedebates Human computation to create recommender system Co-retweeting connections reveal perceivedrelationships between accounts Recommender system allows for easier and moremeaningful consumption of data in real time
  29. 29. Future Work
  30. 30. Highlight the Accounts You Follow
  31. 31. Highlight the Accounts You Follow
  32. 32. Highlight the Accounts You Follow
  33. 33. Highlight the Accounts You Follow
  34. 34. Highlight the Accounts You Follow

×