Abstract
Twitter, a popular social networking service, enables its users to not only send messages but re-broadcast or retweet a message from another Twitter user to their own followers. Considering the number of times that a message is retweeted across Twitter is a straightforward way to estimate how interesting it is. However, a considerable number of messages in Twitter with high retweet counts are actually mundane posts by celebrities that are of interest to themselves and possibly their followers. In this paper, we leverage retweets as implicit relationships between Twitter users and messages and address the problem of automatically finding messages in Twitter that may be of potential interest to a wide audience by using link analysis methods that look at more than just the sheer number of retweets. Experimental results on real world data demonstrate that the proposed method can achieve better performance than several baseline methods.
| Original language | English |
|---|---|
| Title of host publication | ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings |
| Pages | 15-19 |
| Number of pages | 5 |
| Publication status | Published - 2012 Dec 1 |
| Event | 7th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2012 - Jeju, Korea, Republic of Duration: 2012 Jul 13 → 2012 Jul 13 |
Other
| Other | 7th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2012 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju |
| Period | 12/7/13 → 12/7/13 |
ASJC Scopus subject areas
- Software
Cite this
Using link analysis to discover interesting messages spread across twitter. / Yang, Min Chul; Lee, Jung Tae; Rim, Hae-Chang.
ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings. 2012. p. 15-19.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Using link analysis to discover interesting messages spread across twitter
AU - Yang, Min Chul
AU - Lee, Jung Tae
AU - Rim, Hae-Chang
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Twitter, a popular social networking service, enables its users to not only send messages but re-broadcast or retweet a message from another Twitter user to their own followers. Considering the number of times that a message is retweeted across Twitter is a straightforward way to estimate how interesting it is. However, a considerable number of messages in Twitter with high retweet counts are actually mundane posts by celebrities that are of interest to themselves and possibly their followers. In this paper, we leverage retweets as implicit relationships between Twitter users and messages and address the problem of automatically finding messages in Twitter that may be of potential interest to a wide audience by using link analysis methods that look at more than just the sheer number of retweets. Experimental results on real world data demonstrate that the proposed method can achieve better performance than several baseline methods.
AB - Twitter, a popular social networking service, enables its users to not only send messages but re-broadcast or retweet a message from another Twitter user to their own followers. Considering the number of times that a message is retweeted across Twitter is a straightforward way to estimate how interesting it is. However, a considerable number of messages in Twitter with high retweet counts are actually mundane posts by celebrities that are of interest to themselves and possibly their followers. In this paper, we leverage retweets as implicit relationships between Twitter users and messages and address the problem of automatically finding messages in Twitter that may be of potential interest to a wide audience by using link analysis methods that look at more than just the sheer number of retweets. Experimental results on real world data demonstrate that the proposed method can achieve better performance than several baseline methods.
UR - http://www.scopus.com/inward/record.url?scp=84883298909&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883298909&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84883298909
SN - 9781937284374
SP - 15
EP - 19
BT - ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings
ER -