Let’s CoRank: trust of users and tweets on social networks

Li, P; Zhao, W; Yang, J; Sheng, QZ; Wu, J

Let’s CoRank: trust of users and tweets on social networks

Li, P Zhao, W Yang, J Sheng, QZ Wu, J

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: World Wide Web, 2020, 23, (5), pp. 2877-2901
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Li, P
dc.contributor.author	Zhao, W
dc.contributor.author	Yang, J
dc.contributor.author	Sheng, QZ
dc.contributor.author	Wu, J
dc.date.accessioned	2021-02-26T00:42:15Z
dc.date.available	2021-02-26T00:42:15Z
dc.date.issued	2020-09-01
dc.identifier.citation	World Wide Web, 2020, 23, (5), pp. 2877-2901
dc.identifier.issn	1386-145X
dc.identifier.issn	1573-1413
dc.identifier.uri	http://hdl.handle.net/10453/146476
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Twitter, as one of the largest Online Social Network (OSN) platforms, is a popular media for online social communication and information dissemination. The trustworthy evaluation of information and people becomes crucial for maintaining an open and healthy OSN for our society. In this work, we develop a Coupled Dual Networks Trust Ranking (CoRank) method to evaluate the trustworthiness of users and tweets by analysing user/tweet behaviours on Twitter. We propose a model to capture the complex characteristics and relations of both users and tweets and calculate their trust values. Our approach goes beyond the existing solutions that use a single network to link both users and tweets. A set of experiments have been conducted against the real data collected from Twitter. The experimental results show the effectiveness, robustness, and time complexity of the proposed method. We also compare our solution with three baseline methods PageRank, TURank, and Weighted PageRank, to show how our approach outperforms the existing ones.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	World Wide Web
dc.relation.isbasedon	10.1007/s11280-020-00829-4
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Let’s CoRank: trust of users and tweets on social networks
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-26T00:42:13Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	5

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Twitter, as one of the largest Online Social Network (OSN) platforms, is a popular media for online social communication and information dissemination. The trustworthy evaluation of information and people becomes crucial for maintaining an open and healthy OSN for our society. In this work, we develop a Coupled Dual Networks Trust Ranking (CoRank) method to evaluate the trustworthiness of users and tweets by analysing user/tweet behaviours on Twitter. We propose a model to capture the complex characteristics and relations of both users and tweets and calculate their trust values. Our approach goes beyond the existing solutions that use a single network to link both users and tweets. A set of experiments have been conducted against the real data collected from Twitter. The experimental results show the effectiveness, robustness, and time complexity of the proposed method. We also compare our solution with three baseline methods PageRank, TURank, and Weighted PageRank, to show how our approach outperforms the existing ones.

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http://hdl.handle.net/10453/146476