Abstract

As social media continues to influence our daily life, much research has focused on analyzing characteristics of social networks and tracking how information flows in social media. Information cascade originated from the study of information diffusion which focused on how decision making is affected by others depending on the network structure. An example of such study is the SIR (Susceptible, Infected, Removed) model. The current research on information cascade mainly focuses on three open questions: diffusion model, network inference, and influence maximization. Different from these studies, this dissertation aims at deriving a better understanding to the problem of who will transfer information to whom. Particularly, we want to investigate how knowledge is transferred in social media.

The process of transferring knowledge is similar to the information cascade observed in other social networks in the way that both processes transfer particular information from information container to users who do not have the information. The study first works on understanding information cascade in term of detecting information outbreak in Twitter and the factors affecting the cascades. Then we analyze how knowledge is transferred in the sense of adopting research topic among scholars in the DBLP network. However, the knowledge transfer is not able to be well modeled by scholars’ publications since a “publication” action is a result of many complicated factors which is not controlled by the knowledge transfer only.

So, we turn to Q&A forum, a different type of social media that explicitly contain the process of transferring knowledge, where knowledge transfer is embodied by the question and answering process. This dissertation further investigates Stack-Overflow, a popular Q&A forum, and models how knowledge is transferred among StackOverflow users. The knowledge transfer includes two parts: whether a question will receive answers, and whether an answer will be accepted. By investigating these two problems, it turns out that the knowledge transfer process is affected by the temporal factor and the knowledge level, defined as the combination of the user reputation and posted text. Take these factors into consideration, this work proposes TKTM (Time based Knowledge Transfer Modeling) where the likelihood of a user transfers knowledge to another is modeled as a continuous function of time and the knowledge level being transferred. TKTM is applied to solve several predictive problems: how many user accounts will be involved in the thread to provide answers and comments over time; who will provide the answer; and who will provide the accepted answer. The result is compared to NetRate, QLI, and regression methods such as RandomForest, linear regression. In all experiments, TKTM outperforms other methods significantly.

Library of Congress Subject Headings

Online social networks; Information society; Communication of technical information

Publication Date

7-2016

Document Type

Dissertation

Student Type

Graduate

Degree Name

Computing and Information Sciences (Ph.D.)

Advisor

Shanchieh Jay Yang

Advisor/Committee Member

Andres Kwasinski

Advisor/Committee Member

Christopher Homan

Comments

Physical copy available from RIT's Wallace Library at HM742 .C84 2016

Campus

RIT – Main Campus

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