Social Networks and Collective Action in Large Populations: An application to the Egyptian Arab Spring

We study a dynamic model of collective action in which agents are connected by a social network. Our approach highlights the importance of communication in this problem and conceives that network &- which is continuously evolving &- as providing the channel through which agents not only interact but also communicate. We consider two alternative scenarios that differ only on how agents form their expectations: while in the "benchmark" context agents are completely informed, in the alternative one their expectations are formed through a combination of local observation and sociallearning à la DeGroot. We completely characterize the long-run behavior of the system in both cases and show that only in the latter scenario (arguably the most realistic) there is a significant long-run probability that agents eventually achieve collective action within a meaningful time scale. We suggest that this sheds light on the puzzle of how large populations can coordinate on globallydesired outcomes. Finally, we illustrate the empirical potential of the model by showing that it can be efficiently estimated for the Egyptian Arab Spring using large-scale cross-sectional data from Twitter. This estimation exercise also suggests that, in this instance, network-based social learning played a leading role in the process underlying collective action.