Publication Date

2019

Document Type

Dissertation/Thesis

First Advisor

Polansky, Alan M.

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Statistics and Actuarial Science

Abstract

The foundation of social media is conversation. Social media allows people to share ideas and opinions, as well as discuss those opinions. A point of intrigue for many social scientists is how those opinions change through interaction with others. What influences someone’s opinion? When is a person willing to adapt their opinion, and when does it remain the same? Is it possible to measure these opinion dynamics? Our overall goal is to develop a more comprehensive model for opinion dynamics. The first step of this process is to simulate data that can then be analyzed and used to develop a model. When attempting to build a model for opinion dynamics in social media interactions, it is important to start small, and make sure the core of the function executes properly. Thus, we started with a small, manageable model with no error terms and only two actors. After testing this, we generalized the model and added our error terms, testing this within three different potential Ω environments. Several simulations later, we used our final model function to simulate 1000 iterations of each factor combination and return the Median Absolute deviation, proceeding to perform analysis on this data. There were no consistent factors that gave the maximum or minimum mean or standard deviation of the data. However, this does not mean there is no value in looking at these summaries. With some tweaking to the function, it would be very possible to perform regression analysis or time series analysis to indicate not only which factors are most important, but to be able to effectively estimate the values of these factors with real world data. Additionally, there is value in experimenting with dynamic Ω matrices. In real life, various comments will affect the respect one user feels for another and will therefore change the level of influence on their opinions.

Extent

114 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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