Discovering Content-based Behavioral Roles in Social Networks
Date Issued
2012
Date
2012
Author(s)
Tsai, Hsin-Chieh
Abstract
Social networks such as Facebook, Google+, and Twitter have made a significant impact on the interactions among users. Role analysis helps us to characterize users’ interactions on a social network. However, previously proposed methods are mainly based on structural analysis of social networks rather than content-based behavior analysis. To the best of our knowledge, there is no method using content-based behavioral features extracted from user-generated content and behavior patterns to identify users’ roles and to explore role change patterns in social networks. Therefore, in this thesis, we propose a content-based method to identify users’ roles and find the role change patterns in a social network. The proposed method doesn’t need to define role types in advance and allow a user to play multiple roles on a social network. Our method provides a more general and flexible way to perform role analyses in social networks. The experimental results show that the proposed method can find various roles in a social network and additional roles that haven’t been previously aware of. It can also discover some interesting role change patterns in different groups. The results may help us better understand the trends and future growth of the social network, and formulate more effective management strategies.
Subjects
social network
data mining
content-based behavioral role
role change pattern
Type
thesis
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ntu-101-R99725005-1.pdf
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