Impact of human activity patterns on the dynamics of information diffusion.

By José Luis Iribarren, E. Moro. Physical Review Letters, 2007

405 citations recorded by the source corpus.

We study the impact of human activity patterns on information diffusion. To this end we ran a viral email experiment involving 31,183 individuals in which we were able to track a specific piece of information through the social network. We found that, contrary to traditional models, information travels at an unexpectedly slow pace. By using a branching model which accurately describes the experiment, we show that the large heterogeneity found in the response time is responsible for the slow dynamics of information at the collective level. Given the generality of our result, we discuss the important implications of this finding while modeling human dynamical collective phenomena.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2aa97697206ec5c0a4f13bcbaabd5207d033224e

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Citations in the corpus

Dynamics of information diffusion and its applications on complex networks (2016, background): “And human activity patterns, such as the large heterogeneity in response time [2], weight-topology correlations and the burst activity patterns on individuals [3], would slow down information spreading significantly.”
What Stops Social Epidemics? (2011, methodology): “…epidemics on the Internet (Castellano and PastorSatorras 2010), word-of-mouth recommendations (Goldenberg, Libai, and Muller 2001), viral marketing campaigns (Kempe, Kleinberg, and Tardos 2003; Iribarren and Moro 2009), and information cascades in online social networks (Lerman and Ghosh 2010).”
Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process (2012, result): “First, the propagation of influence from one person to another may incur a certain amount of time delay, which is evident from recent studies in statistical physics (Iribarren and Moro 2009; Karsai et al. 2011).”
Analytically Solvable Model of Spreading Dynamics with Non-Poissonian Processes (2013, background): “The spreading dynamics on this temporal network can be related to a class of Bellman-Harris branching processes [16,20,24].”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “…the type of information being transmitted [29], its novelty [27], inﬂuence of the initial spreader [38] and activity levels of subsequent spreaders [39], how people respond to repeated exposure to information by their network neighbors [29], [26], [40], [41], as well as network structure [40],…”