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The Dynamics of Content Popularity in Social Media

The Dynamics of Content Popularity in Social Media

Symeon Papadopoulos, Athena Vakali, Ioannis Kompatsiaris
Copyright: © 2010 |Volume: 6 |Issue: 1 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781616929206|EISSN: 1548-3924|DOI: 10.4018/jdwm.2010090802
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MLA

Papadopoulos, Symeon, et al. "The Dynamics of Content Popularity in Social Media." IJDWM vol.6, no.1 2010: pp.20-37. http://doi.org/10.4018/jdwm.2010090802

APA

Papadopoulos, S., Vakali, A., & Kompatsiaris, I. (2010). The Dynamics of Content Popularity in Social Media. International Journal of Data Warehousing and Mining (IJDWM), 6(1), 20-37. http://doi.org/10.4018/jdwm.2010090802

Chicago

Papadopoulos, Symeon, Athena Vakali, and Ioannis Kompatsiaris. "The Dynamics of Content Popularity in Social Media," International Journal of Data Warehousing and Mining (IJDWM) 6, no.1: 20-37. http://doi.org/10.4018/jdwm.2010090802

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Abstract

Social Bookmarking Systems (SBS) have been widely adopted in the last years, and thus they have had a significant impact on the way that online content is accessed, read and rated. Until recently, the decision on what content to display in a publisher’s web pages was made by one or at most few authorities. In contrast, modern SBS-based applications permit their users to submit their preferred content, to comment on and to rate the content of other users and establish social relations with each other. In that way, the vision of the social media is realized, i.e. the online users collectively decide upon the interestingness of the available bookmarked content. This article attempts to provide insights into the dynamics emerging from the process of content rating by the user community. To this end, the article proposes a framework for the study of the statistical properties of an SBS, the evolution of bookmarked content popularity and user activity in time, as well as the impact of online social networks on the content consumption behavior of individuals. The proposed analysis framework is applied to a large dataset collected from digg, a popular social media application.

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