Social networks are often described as complex systems, but what does that mean? Drawing from chaos and complexity theory, we can identify several key characteristics that help explain their unpredictable and emergent behavior.
First, social networks have internal structure. They are composed of many interacting components (users) and exhibit a network structure that describes who interacts with whom. This structure is often hierarchical, with subsystems and communities forming at multiple scales.
Second, they exhibit emergent behaviors. These are behaviors that arise from the interactions of the subsystems and are not evident from analyzing individual users in isolation. A viral meme or a sudden shift in public opinion are examples of emergent phenomena.
Third, social networks adapt and evolve. They are not static systems. They respond to inputs from users and the wider environment, leading to continuous change and evolution.
Finally, uncertainty is pervasive. The sheer number of components, the variety of their relationships, and the rapid pace of change make it impossible to predict the behavior of a social network with certainty.
By understanding these principles, we can move beyond simplistic models of online interaction and begin to appreciate the true complexity of these dynamic systems.