Topic-based social influence measurement for social networks

Hamzehei, A; Jiang, S; Koutra, D; Wong, R; Chen, F

Topic-based social influence measurement for social networks

Hamzehei, A Jiang, S Koutra, D Wong, R Chen, F

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Publisher:: Australasian Association for Information Systems and Australian Computer Society
Publication Type:: Journal Article
Citation:: Australasian Journal of Information Systems, 2017, 21, (0), pp. 1-14
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Hamzehei, A
dc.contributor.author	Jiang, S
dc.contributor.author	Koutra, D
dc.contributor.author	Wong, R
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2022-08-22T03:47:59Z
dc.date.available	2022-08-22T03:47:59Z
dc.date.issued	2017-01-01
dc.identifier.citation	Australasian Journal of Information Systems, 2017, 21, (0), pp. 1-14
dc.identifier.issn	1326-2238
dc.identifier.issn	1449-8618
dc.identifier.uri	http://hdl.handle.net/10453/160672
dc.description.abstract	Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, user-generated content for topic-based influence measurement, and user's interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.
dc.language	en
dc.publisher	Australasian Association for Information Systems and Australian Computer Society
dc.relation.ispartof	Australasian Journal of Information Systems
dc.relation.isbasedon	10.3127/ajis.v21i0.1552
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems, 1503 Business and Management
dc.title	Topic-based social influence measurement for social networks
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0806 Information Systems
utslib.for	1503 Business and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-22T03:47:58Z
pubs.issue	0
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	0

Abstract:

Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, user-generated content for topic-based influence measurement, and user's interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.

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http://hdl.handle.net/10453/160672