How Cascades Grow

Abstract:

In this talk I will discuss diffusion processes in social networks where slower growth can nevertheless result in large cascades and high long-term adoption. Information is expected to diffuse most readily when it can be broadcast by any node and adopted and reshared quickly with little effort. However, some cascades, such as the ice-bucket challenge, diffuse more slowly using a nomination as opposed to a broadcast sharing protocol. In a recent study we contrasted a wide-range of diffusion protocols that generated large cascades ranging from image reshares to the ice-bucket challenge. We identified two key counterbalancing factors in the construction of these protocols: the effort required to participate in the cascade, and the social cost of staying on the sidelines. Protocols requiring greater individual effort slow down a cascade’s propagation, while those imposing a greater social cost of not participating increase the cascade’s adoption likelihood. The way in which individuals communicate can itself be part of a diffusion process on a network. In  a simulation study, we show that initial adoption can be less important if long-term interaction is not sustained. When social ties are clustered, and one’s satisfaction depends on one’s friends continued use,  gradual adoption may lead to higher long-term engagement.

Bio:

Lada Adamic leads the Computational Social Science Team at Facebook. Prior to joining Facebook she was an associate professor at the University of Michigan's School of Information and Center for the Study of Complex Systems. Her research interests center on information dynamics in networks. She has received an NSF CAREER award, a University of Michigan Henry Russell award, the 2012 Lagrange Prize in Complex Systems.