Understanding Collective Human Behavior in Social Media Networks Via the Dynamical Hypothesis: Applications to Radicalization and Conspiratorial Beliefs

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

Automated summary

This article introduces the research on the dynamical hypothesis, which plays an important role in understanding collective behavior of cognitive agents and online social networks. The article explores the feasibility of the dynamical hypothesis in describing the evolving nature of complex systems and how online social networks can be understood as dynamical systems. Additionally, a dynamical modeling approach is introduced to reveal the emergence of radicalized conspiratorial beliefs in social media, and the dynamical hypothesis is contrasted with alternative approaches in explaining collective human behavior in social networks.