Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Christine Reilly

Second Advisor

Dr. Xiang Lian

Third Advisor

Dr. Andres Figueroa


The use of online social networks empowers its users to efficiently disseminate information across traditional social networks. Typically, the weight and value of messages are relative to its readers’ culture and interests. Nevertheless, in some instances, messages take the form of viral phenomenon, which circulates around the world in very short periods of time. Therefore, despite the actual content of the message spread over the network, the determination of the effectiveness of message dissemination across the social network becomes an attractive opportunity for scientific study. Since a meticulous analysis of a complete online social network would require the acquisition of security permissions from its providers, a vast quantity of computer resources and a substantial amount of time for collection of online content in the network, the aim of this thesis is to address these challenges by building a system capable to simulate an online social network. More precisely, we focus our investigation on the Twitter social network, which is one of the most prominent micro-blogging social network providers nowadays. The main contributions of this work include the following: (i) the provision of a tool, for investigations in the area of social networks, to bypass the common challenges observed during data gathering, (ii) the design of a extensible system that minimizes the cost of implementation of newly resampling techniques, and (iii) the invention of a workbench that allows empirical analysis of properties of an online social network (in the context of this study, the dissemination of messages and the dynamics of influence on the Twitter social network, are the key properties under investigation). Production data from the Twitter network is used to present evidence to suffice the evaluation of different measurements of influence. The collection of live data in this investigation was performed for a period of three days (respecting the 15-minute window between GET requests- imposed by the Twitter API). The data collected served to build the grounds for modeling the social networks described in this thesis.


Copyright 2016 Jonatan Reyes. All Rights Reserved.