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IISS: A Framework to Influence Indiv...

Arizona State University.

IISS: A Framework to Influence Individuals through Social Signals on a Social Network.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	IISS: A Framework to Influence Individuals through Social Signals on a Social Network.
作者:	Le, Tien D.
面頁冊數:	72 p.
附註:	Source: Masters Abstracts International, Volume: 52-06.
附註:	Advisers: Hari Sundaram; Hasan Davulcu.
Contained By:	Masters Abstracts International52-06(E).
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1555503
ISBN:	9781303881152

IISS: A Framework to Influence Individuals through Social Signals on a Social Network.
Le, Tien D.

IISS: A Framework to Influence Individuals through Social Signals on a Social Network. - 72 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.S.)--Arizona State University, 2014.

This item must not be sold to any third party vendors.

Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question.

ISBN: 9781303881152Subjects--Topical Terms:

199325
Computer science.

IISS: A Framework to Influence Individuals through Social Signals on a Social Network.
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245 1 0 $a IISS: A Framework to Influence Individuals through Social Signals on a Social Network.
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500 $a Source: Masters Abstracts International, Volume: 52-06.
500 $a Advisers: Hari Sundaram; Hasan Davulcu.
502 $a Thesis (M.S.)--Arizona State University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question.
520 $a In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising.
520 $a In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user.
520 $a I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
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