  The dynamics of information dissemination in social networks is of paramount
importance in processes such as rumors or fads propagation, spread of product
innovations or "word-of-mouth" communications. Due to the difficulty in
tracking a specific information when it is transmitted by people, most
understanding of information spreading in social networks comes from models or
indirect measurements. Here we present an integrated experimental and
theoretical framework to understand and quantitatively predict how and when
information spreads over social networks. Using data collected in Viral
Marketing campaigns that reached over 31,000 individuals in eleven European
markets, we show the large degree of variability of the participants' actions,
despite them being confronted with the common task of receiving and forwarding
the same piece of information. This have a profound effect on information
diffusion: Firstly, most of the transmission takes place due to super-spreading
events which would be considered extraordinary in population-average models.
Secondly, due to the different way individuals schedule information
transmission we observe a slowing down of the spreading of information in
social networks that happens in logarithmic time. Quantitative description of
the experiments is possible through an stochastic branching process which
corroborates the importance of heterogeneity. Since high variability of both
the intensity and frequency of human responses are found in many other
activities, our findings are pertinent to many other human driven diffusion
processes like rumors, fads, innovations or news which has important
consequences for organizations management, communications, marketing or
electronic social communities.
