  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 detectable warning signs
may precede some of these drastic events. This view is also corroborated by
abstract mathematical theory for generic bifurcations in stochastic multi-scale
systems. Whether the stochastic scaling laws used as warning signs are also
present in social networks that anticipate a-priori {\it unknown} events in
society is an exciting open problem, to which at present only highly
speculative answers can be given. Here, we instead provide a first step towards
tackling this formidable question by focusing on a-priori {\it known} events
and analyzing a social network data set with a focus on classical variance and
autocorrelation warning signs. Our results thus pertain to one absolutely
fundamental question: Can the stochastic warning signs known from other areas
also be detected in large-scale social network data? We answer this question
affirmatively as we find that several a-priori known events are preceded by
variance and autocorrelation growth. Our findings thus clearly establish the
necessary starting point to further investigate the relation between abstract
mathematical theory and various classes of critical transitions in social
networks.
