Affinity Paths and Information Diffusion in Social Networks

TL;DR

This paper investigates how information spreads in social networks, revealing unique cascade patterns driven by local content affinity, supported by empirical data and an agent-based model.

Contribution

It introduces the concept of Affinity Paths, explaining novel diffusion patterns through local neighbor affinity, supported by empirical analysis and modeling.

Findings

Cascade patterns are disconnected, low transitivity, and tree-like.

Participants' involvement increases with path length.

Diffusion driven by local affinity rather than network topology.

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 content. 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. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the \emph{Affinity Paths} observed in real information diffusion cascades.