Structural and behavioral analysis and modeling of the society

Abstract

The recently introduced term, coined as New Network Science, facilitates the understanding of many emergent phenomena in nature and society, and is a major trend on the modern scientific scale. One branch of Network Science that has attracted much attention in the last decade is Social Networks Analysis. The benefit of understanding the complex processes behind how people adopt and form their own opinions about surrounding problems is an important concern for research fields like Psychology, Philosophy, Politics, Marketing, Finances, and even Warfare, and it can be alleviated using network analysis. Social media is constantly modifying the way we create, share and consume information, and has become a powerful tool for understanding social trends, and society as a whole. The goal of this thesis is to help in the understanding and better prediction of diffusion phenomena, by using the computer as a tool for social networks analysis. Relying on computer science as a means for modeling and analysis of the underlying social topologies and individual interaction models, I focus on understanding systems of people and when they become stable, as well as the connections that cause events in social networks As such, I make use of the emerging interdisciplinary field of Social Networks Analysis, which sheds new light into the modeling of social opinion dynamics and personal opinion fluctuations, of how people influence each other and how they can be influenced. The presented work begins with the analysis at the topological level of human relationship establishment, then explains and models network growth and interaction based on original and validated socio-psychological assumptions, and reaches the meta-level of human interaction models. These models are a current scientific (and also socio-political) barrier in predicting social emergence and being able to design more stable and safe social systems in the future. I achieve the goals to model social interaction, network structure and network growth more accurately, and ultimately discuss how decision factors can be influenced at the macroscopic level of the society we live in. Like most of the sciences studying opinion and influence, this work models the decision process by combining elements from Psychology, Sociology, Anthropology, and Computer Science.