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

• Published in: Topics in cognitive science 2023-10
• Main Authors: Necaise, Aaron, Han, Jingjing, Vrzáková, Hana, Amon, Mary Jean
• Format: Article
• Language: English
• ISSN: 1756-8757; 1756-8765
• DOI: 10.1111/tops.12702

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. This article discusses how the dynamical hypothesis can be applied to improve our understanding of pressing modern day issues arising from collective human behaviors in online social networks. We introduce one relevant dynamical modeling technique to investigate radicalization in social media forums, and we describe how the dynamical hypothesis can be extended to explore collective online behaviors more broadly.