Detecting informative messages based on user history in twitter

Chang Woo Chun, Jung Tae Lee, Seung Wook Lee, Hae Chang Rim

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


Since more and more users participate in various social networking services, the volume of streaming data is considerably increasing. It is necessary to find out valuable messages from huge data archived every moment. This paper investigates the problem of detecting informative messages in Twitter, and proposes effective methods to solve the problem based on User History. Most of the sheer information in tweets has a common defect which is the fact that it is affected by influence of User level within the Twitter network. Our key idea is to leverage each user's history observed from a large scale dataset as features to determine whether a new message is informative or not, compared to their previous messages. This allows us to normalize influence of individual user on tweets and to estimate the probability of informativeness. Experimental results on a real Twitter data show that our method can effectively improve the performance on identifying informative tweets.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
Number of pages12
Publication statusPublished - 2012
Event8th Asia Information Retrieval Societies Conference, AIRS 2012 - Tianjin, China
Duration: 2012 Dec 172012 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th Asia Information Retrieval Societies Conference, AIRS 2012


  • Information filtering
  • Recommendation system
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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