Relative neighborhood graphs uncover the dynamics of social media engagement

de Vries, NJ; Arefin, AS; Mathieson, L; Lucas, B; Moscato, P

Relative neighborhood graphs uncover the dynamics of social media engagement

de Vries, NJ Arefin, AS Mathieson, L

Lucas, B Moscato, P

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 10086 LNAI pp. 283 - 297
Issue Date:: 2016-01-01

Closed Access

	Filename	Description	Size
	deVriesetal-RNGs-for-FB.pdf	Accepted Manuscript version	496.75 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	de Vries, NJ	en_US
dc.contributor.author	Arefin, AS	en_US
dc.contributor.author	Mathieson, L https://orcid.org/0000-0001-6470-2296	en_US
dc.contributor.author	Lucas, B	en_US
dc.contributor.author	Moscato, P	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 10086 LNAI pp. 283 - 297	en_US
dc.identifier.isbn	9783319495859	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122838
dc.description.abstract	© Springer International Publishing AG 2016. In this paper, we examine if the Relative Neighborhood Graph (RNG) can reveal related dynamics of page-level social media metrics. A statistical analysis is also provided to illustrate the application of the method in two other datasets (the Indo-European Language dataset and the Shakespearean Era Text dataset). Using social media metrics on the world’s ‘top check-in locations’ Facebook pages dataset, the statistical analysis reveals coherent dynamical patterns. In the largest cluster, the categories ‘Gym’, ‘Fitness Center’, and ‘Sports and Recreation’ appear closely linked together in the RNG. Taken together, our study validates our expectation that RNGs can provide a “parameterfree” mathematical formalization of proximity. Our approach gives useful insights on user behaviour in social media page-level metrics as well as other applications.	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	10.1007/978-3-319-49586-6_19	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Relative neighborhood graphs uncover the dynamics of social media engagement	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10086 LNAI	en_US
utslib.for	0802 Computation Theory and Mathematics	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10086 LNAI	en_US

Abstract:

© Springer International Publishing AG 2016. In this paper, we examine if the Relative Neighborhood Graph (RNG) can reveal related dynamics of page-level social media metrics. A statistical analysis is also provided to illustrate the application of the method in two other datasets (the Indo-European Language dataset and the Shakespearean Era Text dataset). Using social media metrics on the world’s ‘top check-in locations’ Facebook pages dataset, the statistical analysis reveals coherent dynamical patterns. In the largest cluster, the categories ‘Gym’, ‘Fitness Center’, and ‘Sports and Recreation’ appear closely linked together in the RNG. Taken together, our study validates our expectation that RNGs can provide a “parameterfree” mathematical formalization of proximity. Our approach gives useful insights on user behaviour in social media page-level metrics as well as other applications.

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

http://hdl.handle.net/10453/122838