Using link analysis to discover interesting messages spread across twitter

Min Chul Yang, Jung Tae Lee, Hae-Chang Rim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publicationACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings
Pages15-19
Number of pages5
Publication statusPublished - 2012 Dec 1
Event7th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2012 - Jeju, Korea, Republic of
Duration: 2012 Jul 132012 Jul 13

Other

Other7th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2012
CountryKorea, Republic of
CityJeju
Period12/7/1312/7/13

ASJC Scopus subject areas

  • Software

Cite this

Yang, M. C., Lee, J. T., & Rim, H-C. (2012). Using link analysis to discover interesting messages spread across twitter. In ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings (pp. 15-19)

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 proceedingConference contribution

Yang, MC, Lee, JT & Rim, H-C 2012, Using link analysis to discover interesting messages spread across twitter. in ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings. pp. 15-19, 7th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2012, Jeju, Korea, Republic of, 12/7/13.
Yang MC, Lee JT, Rim H-C. Using link analysis to discover interesting messages spread across twitter. In ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings. 2012. p. 15-19
Yang, Min Chul ; Lee, Jung Tae ; Rim, Hae-Chang. / Using link analysis to discover interesting messages spread across twitter. ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods for Natural Language Processing, Workshop Proceedings. 2012. pp. 15-19
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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.",
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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.

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