Social networks are ubiquitous in everyday life. Although commonly analyzed from a perspective of individual interactions, social networks can provide insights about the collective behavior of a community. It has been shown that changes in the mood of social networks can be correlated to economic trends, public demonstrations, and political reactions, among others. In this work, we study community resilience in terms of the mood variations of the community. We have developed a method to characterize the mood steady-state of online social networks and to analyze how this steady-state is affected under certain perturbations or events that affect a community. We applied this method to study community behavior for three real social network situations, with promising results.