Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor's Name

Tao Li

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Sundaraja Sitharama Iyengar

Third Advisor's Name

Shu-Ching Chen

Fourth Advisor's Name

Bogdan Carbunar

Fifth Advisor's Name

Debra VanderMeer

Keywords

Data Mining, Information Retrieval, Social Network, Social Media

Date of Defense

3-26-2014

Abstract

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it.

This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign?

The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Identifier

FI14040816

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