Document Type
Thesis
First Faculty Advisor
Brian Blais
Second Faculty Advisor
Suhong Li
Keywords
disease modeling; Twitter dynamics; dynamic model; stochastic model
Publisher
Bryant University
Rights Management
CC-BY-NC-ND
Abstract
This study aims to use compartmental disease-models to explore Twitter dynamics. Applying an epidemiology model to Twitter tweets can give deeper insights into the factors that make a tweet go viral. In addition, this study explored the differences between a stochastic and a dynamic compartmental model. This research connects the world of diseases with the internet and explored if a disease model will accurately model Twitter dynamics. We found that stochastic models were better at fitting to smaller populations of data than dynamic models were. Dynamic models ended up predicting larger populations better. Furthermore, we found that although a topic is popular does not mean that it is infectious. This study was able to show that disease modeling is able to accurately predict Twitter dynamics.
