Credibility-based twitter social network analysis

Al-Sharawneh, J; Sinnappan, S; Williams, MA

Credibility-based twitter social network analysis

Al-Sharawneh, J Sinnappan, S Williams, MA

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7808 LNCS pp. 323 - 331
Issue Date:: 2013-04-10

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Field	Value	Language
dc.contributor.author	Al-Sharawneh, J	en_US
dc.contributor.author	Sinnappan, S	en_US
dc.contributor.author	Williams, MA https://orcid.org/0000-0002-1047-0503	en_US
dc.date.issued	2013-04-10	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7808 LNCS pp. 323 - 331	en_US
dc.identifier.isbn	9783642374005	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26587
dc.description.abstract	Social Network (SN) members in Twitter communicate in varied contexts such as crisis. Formations within social networks are unique as some members have more influence over other members; members with more influence are known as leaders or pioneers. Finding leaders in a social network is the central factor in information dissemination; therefore, the main objective of this paper is to utilize social network information to identify leaders in Twitter during crisis situations. We propose a new approach to identify social network leaders based on their credibility which is drawn from their trustworthiness and expertise utilizing the "Follow the Leader" model based on the content quality generated by SN members. Our approach is highly innovative and the empirical results demonstrate that our approach can identify SN community leaders in a specific context (crisis) and has significant practical benefits. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-37401-2_33	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Credibility-based twitter social network analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7808 LNCS	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7808 LNCS	en_US

Abstract:

Social Network (SN) members in Twitter communicate in varied contexts such as crisis. Formations within social networks are unique as some members have more influence over other members; members with more influence are known as leaders or pioneers. Finding leaders in a social network is the central factor in information dissemination; therefore, the main objective of this paper is to utilize social network information to identify leaders in Twitter during crisis situations. We propose a new approach to identify social network leaders based on their credibility which is drawn from their trustworthiness and expertise utilizing the "Follow the Leader" model based on the content quality generated by SN members. Our approach is highly innovative and the empirical results demonstrate that our approach can identify SN community leaders in a specific context (crisis) and has significant practical benefits. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/26587