José Luis Iribarren, Esteban Moro
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.
This research has been awarded by IBM with a “Shared University Research” 2007 grant.
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