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Communication overload management through social interactions clustering

Lossio-Ventura Juan Antonio, Hacid Hakim, Roche Mathieu, Poncelet Denis. 2016. Communication overload management through social interactions clustering. In : Proceedings of the 31st Annual ACM Symposium on Applied Computing. Ossowski Sascha (ed.). New-York : ACM, pp. 1166-1169. ISBN 978-1-4503-3739-7 ACM Symposium on Applied Computing (SAC 2016). 31, Pisa, Italie, 4 April 2016/8 April 2016.

Paper with proceedings
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Url - éditeur : http://dl.acm.org/citation.cfm?id=2851984&dl=ACM&coll=DL&CFID=808170115&CFTOKEN=73638767

Abstract : We propose in this paper to handle the problem of overload in social interactions by grouping messages according to three important dimensions: (i) content (textual and hashtags), (ii) users, and (iii) time difference. We evaluated our approach on a Twitter data set and we compared it to other existing approaches and the results are promising and encouraging.

Mots-clés libres : Text mining, Clustering, Terminology extraction, Social network

Classification Agris : C30 - Documentation and information
U10 - Computer science, mathematics and statistics
000 - Other themes
E50 - Rural sociology

Auteurs et affiliations

  • Lossio-Ventura Juan Antonio, LIRMM (FRA)
  • Hacid Hakim, Zayed University (ARE)
  • Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
  • Poncelet Denis, LIRMM (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/580892/)

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