How to Extract Relevant Knowledge from Tweets?

Flavien Bouillot, Phan Nhat Hai, Nicolas Béchet, Sandra Bringay, Dino Ienco, Stan Matwin, Pascal Poncelet, Mathieu Roche, Maguelonne Teisseire

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

2 Scopus citations

Abstract

Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available. More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets.

Original languageEnglish (US)
Title of host publicationInformation Search, Integration and Personalization - International Workshop, ISIP 2012, Revised Selected Papers
PublisherSpringer Verlag
Pages111-120
Number of pages10
ISBN (Print)9783642401398
DOIs
StatePublished - 2013
Externally publishedYes
Event7th International Workshop on Information Search, Integration and Personalization, ISIP 2012 - Sapporo, Japan
Duration: Oct 11 2012Oct 13 2012

Publication series

NameCommunications in Computer and Information Science
Volume146
ISSN (Print)1865-0929

Other

Other7th International Workshop on Information Search, Integration and Personalization, ISIP 2012
Country/TerritoryJapan
CitySapporo
Period10/11/1210/13/12

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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