Web event evolution trend prediction based on its computational social context

Xuan, J; Luo, X; Lu, J; Zhang, G

Web event evolution trend prediction based on its computational social context

Xuan, J

Luo, X Lu, J

Zhang, G

Permalink

Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: World Wide Web, 2020, 23, (3), pp. 1861-1886
Issue Date:: 2020-05-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted manuscriptAdobe PDF (2.39 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908
dc.contributor.author	Luo, X
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.date.accessioned	2021-01-29T04:24:15Z
dc.date.available	2021-01-29T04:24:15Z
dc.date.issued	2020-05-01
dc.identifier.citation	World Wide Web, 2020, 23, (3), pp. 1861-1886
dc.identifier.issn	1386-145X
dc.identifier.issn	1573-1413
dc.identifier.uri	http://hdl.handle.net/10453/145654
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces the performance of temporal patterns mining. Furthermore, these approaches overlook the influence of the social context surrounding the Web events. In this paper, we propose a novel method to predict future trends of Web events, based on their social contexts rather than temporal patterns. More specially, in the proposed method, a computational model for the social context is first built as a two-layer Association Linked Network considering its properties, such as the associative network property and the small world property. Then, the interaction between a Web event and the social context is simulated, based on the anchoring theory. Finally, an external force is defined and evaluated to quantify the influence of the social context on the evolution of Web events, which is used to predict future trends of Web events. Experiments show that the performance of the proposed method is better than that of the traditional time series-based approaches.
dc.language	English
dc.publisher	SPRINGER
dc.relation	http://purl.org/au-research/grants/arc/DP190101733
dc.relation.ispartof	World Wide Web
dc.relation.isbasedon	10.1007/s11280-019-00753-2
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Web event evolution trend prediction based on its computational social context
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-29T04:24:10Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	3

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces the performance of temporal patterns mining. Furthermore, these approaches overlook the influence of the social context surrounding the Web events. In this paper, we propose a novel method to predict future trends of Web events, based on their social contexts rather than temporal patterns. More specially, in the proposed method, a computational model for the social context is first built as a two-layer Association Linked Network considering its properties, such as the associative network property and the small world property. Then, the interaction between a Web event and the social context is simulated, based on the anchoring theory. Finally, an external force is defined and evaluated to quantify the influence of the social context on the evolution of Web events, which is used to predict future trends of Web events. Experiments show that the performance of the proposed method is better than that of the traditional time series-based approaches.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145654