This document proposes a method called TweetRank for ranking resources like web links by analyzing the Twitter graph. TweetRank is computed in 3 steps: 1) Calculating a UserRank score for each user based on their followers, 2) Determining the TweetRelevance of each tweet based on the author's UserRank, and 3) Computing the TweetRank of each resource based on tweets and retweets that mention the resource. The method was evaluated using YouTube videos and search results, showing TweetRank can help reflect an indirect form of user rating for resources from Twitter data.

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