Contagious Information Spread Patterns in Social Networks: A Dynamic Model

• In Event: Media, Protest and Violence

Abstract

Human communication on social media is a dynamic social process. However, traditional models on human communication only allows for random interaction built on a uniform population, but cannot simulate real people interacting in real social circles. Using a dynamic network structure model, this study investigates the dynamics and critical transitions of opinions and spillover effect within social networks. It explores how individuals' information spread patterns and information dissemination can influence and spread across network connections. We simulate a large dataset of online social interactions to examine the extent to which communication behaviors and information sharing propagate through social ties. The findings reveal significant spillover effects, with individuals being influenced by their immediate network contacts and, in turn, influencing their contacts' behavior. We identify key structural properties of the network that facilitate or hinder the spread of information, such as centrality measures, network density, and tie strength. Furthermore, we investigate the role of influential individuals or "opinion leaders" in driving communication and spillover effects within the network. The results shed light on the mechanisms of information diffusion and social influence within social networks, offering insights into the design of effective communication strategies and interventions. This research contributes to the broader understanding of social network dynamics and has important implications for understanding political polarization, aggression and cyberbullying on social media.

Authors

• Meixi Macy Zhuang

• Dan Liu