Critical transitions in social network activity

A large variety of complex systems in ecology, climate science, biomedicine and engineering have been observed to exhibit tipping points, where the internal dynamical state of the system abruptly changes. For example, such critical transitions may result in the sudden change of ecological environments and climate conditions. Data and models suggest that some of these drastic events may be preceded by detectable early-warning signs. This view is also corroborated by abstract mathematical theory for generic bifurcations in stochastic multi-scale systems. Whether early-warnings are also present in social networks that anticipate a-priori unknown events in society is an open problem to which only highly speculative answers can be given at present. Here, we focus on a-priori known events and analyze a social network data set with a focus on classical variance and autocorrelation warning signs. We find that several a-priori known events are preceded by variance and autocorrelation growth as predicted by mathematical theory.