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Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Peer Review Status: Refereed
Title: Clustering social networks using interaction semantics and sentics
Author(s): Chandra, Praphul
Cambria, Erik
Hussain, Amir
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Editor(s): Wang, J
Yen, GG
Polycarpou, MM
Citation: Chandra P, Cambria E & Hussain A (2012) Clustering social networks using interaction semantics and sentics. In: Wang J, Yen G & Polycarpou M (eds.) Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I. Lecture Notes in Computer Science, 7367. Berlin Heidelberg: Springer, pp. 379-385.
Keywords: Social Network Analysis
Sentic Computing
Issue Date: 2012
Date Deposited: 12-Aug-2013
Series/Report no.: Lecture Notes in Computer Science, 7367
Abstract: The passage from a static read-only Web to a dynamic read-write Web gave birth to a huge amount of online social networks with the ultimate goal of making communication easier between people with common interests. Unlike real world social networks, however, online social groups tend to form for extremely varied and multi-faceted reasons. This makes very difficult to group members of the same social network in subsets in a way that certain types of contents are shared with just certain types of friends. Moreover, such a task is usually too tedious to be performed manually and too complex to be performed automatically. In this work, we propose a new approach for automatically clustering social networks, which exploits interaction semantics and sentics, that is, the conceptual and affective information associated with the interactive behavior of online social network members.
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