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Understanding Social Media Users via Attributes and Links


Abstract With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opi... (more)
Created Date 2014
Contributor Abbasi, Mohammad Ali (Author) / Liu, Huan (Advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Subject Computer science / Attribute Prediction / Link Prediction / Machine Learning / Relational Learning / Scalable Learning / Social Network Analysis
Type Doctoral Dissertation
Extent 121 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis