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Information diffusion and influence ...

Cheng, Jiesi.

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Information diffusion and influence propagation on social networks with marketing applications.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Information diffusion and influence propagation on social networks with marketing applications./
作者:	Cheng, Jiesi.
面頁冊數:	124 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: A.
Contained By:	Dissertation Abstracts International75-02A(E).
標題:	Business Administration, Management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597824
ISBN:	9781303461712

Information diffusion and influence propagation on social networks with marketing applications.
Cheng, Jiesi.

Information diffusion and influence propagation on social networks with marketing applications. - 124 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: A.

Thesis (Ph.D.)--The University of Arizona, 2013.

Web and mobile technologies have had such profound impact that we have witnessed significant evolutionary changes in our social, economic and cultural activities. In recent years, online social networking sites such as Twitter, Facebook, Google+, and LinkedIn have gained immense popularity. Such social networks have led to an enormous explosion of network-centric data in a wide variety scenarios, posing unprecedented analytical and computational challenges to MIS researchers. At the same time, the availability of such data offers major research opportunities in various social computing and analytics areas to tackle interesting questions such as - From a business and marketing perspective, how to mine the novel datasets of online user activities, interpersonal communications and interactions, for developing more successful marketing strategies? - From a system development perspective, how to incorporate massive amounts of available data to assist online users to find relevant, efficient, and timely information?

ISBN: 9781303461712Subjects--Topical Terms:

626628
Business Administration, Management.

Information diffusion and influence propagation on social networks with marketing applications.
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500 $a Adviser: Daniel Zeng.
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520 $a Web and mobile technologies have had such profound impact that we have witnessed significant evolutionary changes in our social, economic and cultural activities. In recent years, online social networking sites such as Twitter, Facebook, Google+, and LinkedIn have gained immense popularity. Such social networks have led to an enormous explosion of network-centric data in a wide variety scenarios, posing unprecedented analytical and computational challenges to MIS researchers. At the same time, the availability of such data offers major research opportunities in various social computing and analytics areas to tackle interesting questions such as - From a business and marketing perspective, how to mine the novel datasets of online user activities, interpersonal communications and interactions, for developing more successful marketing strategies? - From a system development perspective, how to incorporate massive amounts of available data to assist online users to find relevant, efficient, and timely information?
520 $a In this dissertation, I explored these research opportunities by studying multiple analytics problems arose from the design and use of social networking services. The first two chapters (Chapter 2 and 3) are intended to study how social network can help to derive a better estimation of customer lifetime value (CLV), in the social gaming context. In Chapter 2, I first conducted an empirical study to demonstrate that friends' activities can serve as significant indicators of a player's CLV. Based on this observation, I proposed a perceptron-based online CLV prediction model considering both individual and friendship information. Preliminary results have shown that the model can be effectively used in online CLV prediction, by evaluating against other commonly-used benchmark methods. In Chapter 3, I further extended the metric of traditional CLV, by incorporating the personal influences on other customers' purchase as an integral part of the lifetime value. The proposed metric was illustrated and tested on seven social games of different genres. The results showed that the new metric can help marketing managers to achieve more successful marketing decisions in user acquisition, user retention, and cross promotion. Chapter 4 is devoted to the design of a recommendation system for micro-blogging. I studied the information diffusion pattern in a micro-blogging site (Twitter.com) and proposed diffusion-based metrics to assess the quality of micro-blogs, and leverage the new metric to implement a novel recommendation framework to help micro-blogging users to efficiently identify quality news feeds. Chapter 5 concludes this dissertation by highlighting major research contributions and future directions.
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