Communication overload management through social interactions clustering

Document Type

Conference Proceeding

Source of Publication

Proceedings of the ACM Symposium on Applied Computing

Publication Date

4-4-2016

Abstract

© 2016 ACM. 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.

ISBN

9781450337397

Publisher

Association for Computing Machinery

Volume

04-08-April-2016

First Page

1166

Last Page

1169

Disciplines

Computer Sciences

Keywords

Clustering, Social networks, Twitter

Scopus ID

84975819733

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Green: A manuscript of this publication is openly available in a repository

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