Exploring the meaning behind Twitter hashtags through clustering. (Contributo in volume (capitolo o saggio))

Type
Label
  • Exploring the meaning behind Twitter hashtags through clustering. (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-34228-8_22 (literal)
Alternative label
  • Muntean C. I., Morar G. A., Moldovan D. (2012)
    Exploring the meaning behind Twitter hashtags through clustering.
    Springer, London (Regno Unito) in BIS 2012 - Business Information Systems Workshops. Revised papers, 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Muntean C. I., Morar G. A., Moldovan D. (literal)
Pagina inizio
  • 231 (literal)
Pagina fine
  • 242 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/chapter/10.1007%2F978-3-642-34228-8_22?LI=true (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • BIS 2012 - Business Information Systems Workshops. Revised papers (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 127 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 12 (literal)
Note
  • Scopu (literal)
  • PuMa (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; Babes-Bolyai University, Cluj-Napoca, Romania; Babes-Bolyai University, Cluj-Napoca, Romania; (literal)
Titolo
  • Exploring the meaning behind Twitter hashtags through clustering. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-34227-1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Witold Abramowicz, John Domingue, Krzysztof W?cel (literal)
Abstract
  • Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with. (literal)
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