  Online social media have greatly affected the way in which we communicate
with each other. However, little is known about what are the fundamental
mechanisms driving dynamical information flow in online social systems. Here,
we introduce a generative model for online sharing behavior that is
analytically tractable and which can reproduce several characteristics of
empirical micro-blogging data on hashtag usage, such as (time-dependent)
heavy-tailed distributions of meme popularity. The presented framework
constitutes a null model for social spreading phenomena which, in contrast to
purely empirical studies or simulation-based models, clearly distinguishes the
roles of two distinct factors affecting meme popularity: the memory time of
users and the connectivity structure of the social network.
