Space Pattern of Facebook User Prediction Based on User Behavior
Date Issued
2015
Date
2015
Author(s)
Wang, Tzu-An
Abstract
As the rapid development of social networks services (SNSs), SNSs like Face-book are getting more popular and have played a critical role in our daily lives. This is because we are able to interact with our friends or acquire latest information (or news) on Facebook in anytime at anyplace. That is, such new SNSs have changed the way of how to communicate or connect our friends and how to learn new knowledge or information. Among all social network sites, Facebook is currently the biggest social networking service based on global reach and total active users. For the large number of users, Facebook contains a lot of user behavior data. It attracts many researchers to start studying user behavior in Facebook. Hence, the focus of this work will study how people behavior within Facebook from the perspective of environmental psychology. We believe that learning user’s behavior within Facebook and what they feel like will be a key to successfully design better functionalities for Facebook. To deal with this issue, we design a user behavior model by crawling user data in Facebook to predict user’s space type. Results show that our approach is able to do well on prediction accuracy of user’s space type.
Subjects
Social networks
Facebook
Space pattern
Clustering
Type
thesis
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