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Leveraging Microblogs
                                               for Resource Ranking

Tomáš Majer, Marián Šimko
tomasmajer@gmail.com, simko@fiit.stuba.sk


23.01.2012, SOFSEM 2012
Institute of Informatics and Software Engineering
Faculty of Informatics and Information Technologies
Slovak University of Technology in Bratislava
Microblog
• a brief form of a blog with limited size of a post,
  typically 140 characters of length
• sharing experiences, opinions, comments, links
• Twitter, Identi.ca, Jaiku,   (Google+, Facebook)


• Twitter:
  ▫ over 300 millions of users   (100 mil. at the time of writing paper)
  ▫ over 300 millions of tweets/day     (100 mil. --||--)
Microblog – why important?
• „Read-Write Web“ vision
• user-generated data
 ▫ unbiased
 ▫ not moderated


• huge amount of data – text, links
• valuable source of information
 ▫ data mining
Microblog – why important?
• „Read-Write Web“ vision
• user-generated data
 ▫ unbiased
 ▫ not moderated
                                      22 % of posts


• huge amount of data – text, links
• valuable source of information
 ▫ data mining
State-of-the-Art
• topic identification/keyword extraction             (Ramage et al., 2010)

• opinion mining    (Pendey, Iyer, 2009)

• twitter search, tweets ranking           (Teevan et al., 2011)

• user ranking   (Gayo-Avello, 2010)

• user modeling    (Abel et al., 2011)

•…
• challenge: resource ranking
Twitter Graph
                              T1
                                          R1
       U1
                         T2
                                    R2

               T3
       U2                                  R3
                               T4

                    T5
                                     R4

       U3
                          T6
                                          R5
                    T7


       Users         Tweets         Resources
Twitter Graph
                                  T1
                                              R1
 posts     U1
                             T2                     contains
                                        R2

                   T3
           U2                                  R3
                                   T4

                        T5
                                         R4

           U3                                       re-posts
 follows                      T6
                                              R5
                        T7


           Users         Tweets         Resources
Resource Ranking: Overview
• TweetRank
 ▫ ranking of a resource based on Twitter graph analysis


• Computation
 1. UserRank
 2. TweetRelevance
 3. TweetRank
Resource Ranking: Overview
• TweetRank
 ▫ ranking of a resource based on Twitter graph analysis


• Computation                                   T1
                                                           R1


 1. UserRank
                            U1
                                           T2
                                                      R2

                                 T3


 2. TweetRelevance
                            U2                              R3
                                                 T4

                                      T5
                                                      R4



 3. TweetRank
                            U3
                                            T6
                                                           R5
                                      T7
UserRank



                                1+γ (u)UserRank ( f )
 UserRank (u) = ∑
               f ∈ followers(u)   ∣ followers( f )∣
UserRank
                                        ∣ followers(u)∣
                                   γ(u)=
                                           ∣tweets(u)∣


                                1+γ (u)UserRank ( f )
 UserRank (u) = ∑
               f ∈ followers(u)   ∣ followers( f )∣
TweetRelevance, TweetRank


                      UserRank ( Author (t))
 TweetRelevance (t) =
                       ∣tweets( Author (t ))∣
TweetRelevance, TweetRank


                      UserRank ( Author (t))
 TweetRelevance (t) =                         = TR(t)
                       ∣tweets( Author (t ))∣


 TweetRank (r) =      ∑
                   t ∈tweets(r )   (   TR(t)+        ∑
                                                rt ∈retweets(t)
                                                                  TR(t)TR(rt)
                                                                                )
Evaluation
1. TweetRank ranking vs. explicit user ranking (YouTube )
2. Search results ranking study (Search)


• Data:
 ▫ 1,997,466 tweets from 367,824 users
    85 % in English
 ▫ 1,468,365,182 connections between 40,103,281 users
 ▫ 1,150,168 unique web links
    3 % of them: YouTube
Computing TweetRank
• TweetRank computed for each resource
 ▫ power-law distribution
   # of resources




                            TweetRank intervals
Experiment YouTube
• YouTube – explicit user rating (Y1Rank)
 ▫ positive/negative vote, normalized


• TweetRank vs. Y1Rank
                             TweetRank, Y1Rank
 ▫ correlation coefficient
   r = 0.02




                                                 YouTube videos
Experiment YouTube 2
• YouTube – application-collected user rating (Y2Rank)
 ▫ „how do you like the video“?
 ▫ 5 degree scale (1-best, 5-worst), 70 participants


• TweetRank vs. Y2Rank
 ▫ Kendall rank correlation coefficient τ= 0.125


• Relative video ranking
Experiment YouTube 2
 ▫ 5-tuplets
Experiment YouTube 2
 ▫ pairs
Experiment Search
• 20,000 resources
• indexing: SOLR (Apache Lucene)
• searching: resource ranking extended with TweetRank


• search results manual comparison
 ▫ 20 randomly selected queries (e.g.: „apple“)
 ▫ analyzed top-k results
Experiment Search
• findings:
  ▫ in general, „newer“ resources rank better
  ▫ ranking does not reflect chronological ordering of
    resources


• suitable for sorting search results within a predefined
  time window
Conclusions
• microblog
 ▫ perspective source of data, information, knowledge
• we proposed novel method for resource ranking
  leveraging microblog network
• an important additional knowledge from the crowd
 ▫ a form of indirect explicit user rating
• great potential for search improvement
 ▫ reflects temporal characteristics (not linearly)
 ▫ sorting results within a predefined time window

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Leveraging microblogs for resource ranking

  • 1. Leveraging Microblogs for Resource Ranking Tomáš Majer, Marián Šimko tomasmajer@gmail.com, simko@fiit.stuba.sk 23.01.2012, SOFSEM 2012 Institute of Informatics and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava
  • 2. Microblog • a brief form of a blog with limited size of a post, typically 140 characters of length • sharing experiences, opinions, comments, links • Twitter, Identi.ca, Jaiku, (Google+, Facebook) • Twitter: ▫ over 300 millions of users (100 mil. at the time of writing paper) ▫ over 300 millions of tweets/day (100 mil. --||--)
  • 3. Microblog – why important? • „Read-Write Web“ vision • user-generated data ▫ unbiased ▫ not moderated • huge amount of data – text, links • valuable source of information ▫ data mining
  • 4. Microblog – why important? • „Read-Write Web“ vision • user-generated data ▫ unbiased ▫ not moderated 22 % of posts • huge amount of data – text, links • valuable source of information ▫ data mining
  • 5. State-of-the-Art • topic identification/keyword extraction (Ramage et al., 2010) • opinion mining (Pendey, Iyer, 2009) • twitter search, tweets ranking (Teevan et al., 2011) • user ranking (Gayo-Avello, 2010) • user modeling (Abel et al., 2011) •… • challenge: resource ranking
  • 6. Twitter Graph T1 R1 U1 T2 R2 T3 U2 R3 T4 T5 R4 U3 T6 R5 T7 Users Tweets Resources
  • 7. Twitter Graph T1 R1 posts U1 T2 contains R2 T3 U2 R3 T4 T5 R4 U3 re-posts follows T6 R5 T7 Users Tweets Resources
  • 8. Resource Ranking: Overview • TweetRank ▫ ranking of a resource based on Twitter graph analysis • Computation 1. UserRank 2. TweetRelevance 3. TweetRank
  • 9. Resource Ranking: Overview • TweetRank ▫ ranking of a resource based on Twitter graph analysis • Computation T1 R1 1. UserRank U1 T2 R2 T3 2. TweetRelevance U2 R3 T4 T5 R4 3. TweetRank U3 T6 R5 T7
  • 10. UserRank 1+γ (u)UserRank ( f ) UserRank (u) = ∑ f ∈ followers(u) ∣ followers( f )∣
  • 11. UserRank ∣ followers(u)∣ γ(u)= ∣tweets(u)∣ 1+γ (u)UserRank ( f ) UserRank (u) = ∑ f ∈ followers(u) ∣ followers( f )∣
  • 12. TweetRelevance, TweetRank UserRank ( Author (t)) TweetRelevance (t) = ∣tweets( Author (t ))∣
  • 13. TweetRelevance, TweetRank UserRank ( Author (t)) TweetRelevance (t) = = TR(t) ∣tweets( Author (t ))∣ TweetRank (r) = ∑ t ∈tweets(r ) ( TR(t)+ ∑ rt ∈retweets(t) TR(t)TR(rt) )
  • 14. Evaluation 1. TweetRank ranking vs. explicit user ranking (YouTube ) 2. Search results ranking study (Search) • Data: ▫ 1,997,466 tweets from 367,824 users  85 % in English ▫ 1,468,365,182 connections between 40,103,281 users ▫ 1,150,168 unique web links  3 % of them: YouTube
  • 15. Computing TweetRank • TweetRank computed for each resource ▫ power-law distribution # of resources TweetRank intervals
  • 16. Experiment YouTube • YouTube – explicit user rating (Y1Rank) ▫ positive/negative vote, normalized • TweetRank vs. Y1Rank TweetRank, Y1Rank ▫ correlation coefficient r = 0.02 YouTube videos
  • 17. Experiment YouTube 2 • YouTube – application-collected user rating (Y2Rank) ▫ „how do you like the video“? ▫ 5 degree scale (1-best, 5-worst), 70 participants • TweetRank vs. Y2Rank ▫ Kendall rank correlation coefficient τ= 0.125 • Relative video ranking
  • 18. Experiment YouTube 2 ▫ 5-tuplets
  • 19. Experiment YouTube 2 ▫ pairs
  • 20. Experiment Search • 20,000 resources • indexing: SOLR (Apache Lucene) • searching: resource ranking extended with TweetRank • search results manual comparison ▫ 20 randomly selected queries (e.g.: „apple“) ▫ analyzed top-k results
  • 21. Experiment Search • findings: ▫ in general, „newer“ resources rank better ▫ ranking does not reflect chronological ordering of resources • suitable for sorting search results within a predefined time window
  • 22. Conclusions • microblog ▫ perspective source of data, information, knowledge • we proposed novel method for resource ranking leveraging microblog network • an important additional knowledge from the crowd ▫ a form of indirect explicit user rating • great potential for search improvement ▫ reflects temporal characteristics (not linearly) ▫ sorting results within a predefined time window