Discovering Social Behaviour Variances of Younger and Older Users through Social Interaction Analysis

Research output: Contribution to journalArticle

Abstract

The popularity of Social Networking has risen considerably in recent years, increasing opportunities for social interaction. As an approach, it has the potential to reduce the burden of social isolation for older users. However, the current state of older user engagement requires investigation. In a study exploring the possibilities of Interaction Analysis to undercover user behaviours / characteristics, five aspects of younger and older online engagement were investigated comprising; connectivity, length of engagement, application usage, engagement frequency classification and profile maintenance frequency. Results derived from user generated content, enabled direct comparisons on the engagement levels of both cohorts. Results established Interaction Analysis as an approach to detect user behaviour(s), observing the degree older users fail to return and maintain activity as significant; however users who do engage, maintain activity with a broad range of functions. Results quantified the interactions and behaviours of two disparate cohorts, determining key user characteristics.
LanguageEnglish
Pages44-65
JournalInternational Journal of Web Science
Volume2
Issue number1/2
DOIs
Publication statusPublished - Sep 2013

Cite this

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title = "Discovering Social Behaviour Variances of Younger and Older Users through Social Interaction Analysis",
abstract = "The popularity of Social Networking has risen considerably in recent years, increasing opportunities for social interaction. As an approach, it has the potential to reduce the burden of social isolation for older users. However, the current state of older user engagement requires investigation. In a study exploring the possibilities of Interaction Analysis to undercover user behaviours / characteristics, five aspects of younger and older online engagement were investigated comprising; connectivity, length of engagement, application usage, engagement frequency classification and profile maintenance frequency. Results derived from user generated content, enabled direct comparisons on the engagement levels of both cohorts. Results established Interaction Analysis as an approach to detect user behaviour(s), observing the degree older users fail to return and maintain activity as significant; however users who do engage, maintain activity with a broad range of functions. Results quantified the interactions and behaviours of two disparate cohorts, determining key user characteristics.",
author = "Darren Quinn and Liming Chen and Maurice Mulvenna",
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