Communication overload management through social interactions clustering
Source of Publication
Proceedings of the ACM Symposium on Applied Computing
© 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.
Association for Computing Machinery
Clustering, Social networks, Twitter
Lossio-Ventura, Juan Antonio; Hacid, Hakim; Roche, Mathieu; and Poncelet, Pascal, "Communication overload management through social interactions clustering" (2016). All Works. 984.
Indexed in Scopus
Open Access Type
Green: A manuscript of this publication is openly available in a repository