Tag: viral rss

Posts

03 November 2011 / / Publications
Authors: José Luis Iribarren and Esteban Moro Journal: Physical Review E 84, 046116 (2011) LINK | arXiv Abstract: Despite its importance for rumors or innovations propagation, peer-to-peer collaboration, social networking, or marketing, the dynamics of information spreading is not well understood. Since the diffusion depends on the heterogeneous patterns of human behavior and is driven by the participants’ decisions, its propagation dynamics shows surprising properties not explained by traditional epidemic or contagion models.
08 February 2011 / / Publications
Authors: José Luis Iribarren and Esteban Moro Journal: Social Networks 33, 134-142 (2011). LINK | arXiv Abstract: Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different contents. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features.
04 August 2009 / / Publications
Authors: J. L. Iribarren and E. Moro Journal: Physical Review Letters 103, 038702 (2009) LINK arXiv Abstract: We study the impact of human activity patterns on information diffusion. To this end we ran a viral email experiment involving 31183 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.
06 June 2007 / / Publications
Authors: José Luis Iribarren, Esteban Moro Journal: Preprint 0706.0641 arXiv Abstract: 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.