Information Diffusion Epidemics in Social Networks

The dynamics of information dissemination in social networks is of paramount importance in processes such as rumors or fads propagation [1], spread of product innovations [2] or ”word-of-mouth” communications [3, 4]. 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 [5] or indirect measurements [6]. 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 [7] 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. Specifically we observe large heterogeneity in both the number of recommendations made by individuals and of the time they take to transmit the information. Both 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 [8, 9, 10] 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 [11] which corroborates the importance of heterogeneity. The fact that both the intensity and frequency of human responses show also large degrees of heterogeneity in many other activities [12, 13, 14] suggests that 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. Each day, millions of conversations, e-mails, SMS, blog comments, instant messages or web pages containing various types of information are exchanged between people. Humans behave in a viral fashion, having a natural inclination to share the information so as to gain reputation, trustworthiness or money. This “word-of-mouth” (WOM) dissemination of information through social networks is of paramount importance in our every day life. For example, WOM is known to influence purchasing decisions to the extent that 2/3 of the economy of the United States is driven by WOM recommendations [4]. But also WOM is important to understand communication inside organizations, opinion formation in societies or rumor spreading. Despite its importance, detailed empirical data about how humans disseminate information are scarce or indirect [5, 15]. Most understanding comes from implementing models and ideas borrowed from epidemiology on empirical or synthetic social networks [1, 6]. However, unlike virus spreading, information diffusion depends on the voluntary nature of humans, has a perceived transmission cost and is only passed by its host to individuals who may be interested on it [16, 17]. Here we present a large scale experiment designed to measure and understand the influence of human behavior on the diffusion of information. We analyzed a series of controlled viral marketing [7] campaigns in which subscribers to an on-line newsletter were offered incentives for promoting new subscriptions among friends and colleagues. This offering was virally spread through recommendation e-mails sent by participants. This “recommend-a-friend” mechanism was fully conducted electronically and thus could be monitored at every step. Spurred by exogenous online advertising, a total of 7,153 individuals started recommendation cascades subsequently fueled through viral propagation carried out by 2,112 secondary spreaders. This resulted in another 21,918 individuals touched by the message which they did not pass along further. All in all, 31,183 individuals were “infected” by the viral message. Of those, 9,265 were spreaders. Thus, 77% of the participants were reached by the endogenous WOM viral mechanism. We call seed nodes the individuals spontaneously initiating recommendation cascades and viral nodes the individuals who pass e-mail invitations along after having received them from other participants. The topology of the resulting viral recommendations graph (designated as the Viral Network) is a directed network formed by 7,188 isolated components, or viral cascades, where nodes representing participants are connected by arcs representing recommendation e-mails (see Fig. 1).