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Application of Association Rule Mining Theory in Sina Weibo

Cui, X, Shi, Hao and Yi, X (2014) Application of Association Rule Mining Theory in Sina Weibo. Journal of Computer and Communications, 02 (01). 19 - 26. ISSN 2327-5219

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Abstract

A user profile contains information about a user. A substantial effort has been made so as to understand users’ behavior through analyzing their profile data. Online social networks provide an enormous amount of such information for researchers. Sina Weibo, a Twitter-like microblogging platform, has achieved a great success in China although studies on it are still in an initial state. This paper aims to explore the relationships among different profile attributes in Sina Weibo. We use the techniques of association rule mining to identify the dependency among the attributes and we found that if a user’s posts are welcomed, he or she is more likely to have a large number of followers. Our results demonstrate how the relationships among the profile attributes are affected by a user’s verified type. We also put some efforts on data transformation and analyze the influence of the statistical properties of the data distribution on data discretization.

Item Type: Article
Uncontrolled Keywords: social network; user profile; data discretization
Subjects: FOR Classification > 0806 Information Systems
Faculty/School/Research Centre/Department > College of Science and Engineering
Depositing User: Symplectic Elements
Date Deposited: 19 Feb 2016 02:18
Last Modified: 08 Dec 2019 23:29
URI: http://vuir.vu.edu.au/id/eprint/29994
DOI: https://doi.org/10.4236/jcc.2014.21004
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