  Data from social media are providing unprecedented opportunities to
investigate the processes that rule the dynamics of collective social
phenomena. Here, we consider an information theoretical approach to define and
measure the temporal and structural signatures typical of collective social
events as they arise and gain prominence. We use the symbolic transfer entropy
analysis of micro-blogging time series to extract directed networks of
influence among geolocalized sub-units in social systems. This methodology
captures the emergence of system-level dynamics close to the onset of socially
relevant collective phenomena. The framework is validated against a detailed
empirical analysis of five case studies. In particular, we identify a change in
the characteristic time-scale of the information transfer that flags the onset
of information-driven collective phenomena. Furthermore, our approach
identifies an order-disorder transition in the directed network of influence
between social sub-units. In the absence of a clear exogenous driving, social
collective phenomena can be represented as endogenously-driven structural
transitions of the information transfer network. This study provides results
that can help define models and predictive algorithms for the analysis of
societal events based on open source data.
