The nested assembly of collective attention in online social systems
Current state-of-the-art approaches to collective attention and information life-cycle are aimed to prove how topics arise and fade in well-defined dynamical classes, with either the accent placed on human temporal dynamics or on semiotic dynamics. Both frameworks, however, neglect agent-symbol (memes, hashtags) interactions: the originally rich bipartite representation of data is collapsed onto one of the dimensions of the problem, providing a partial understanding of how attention consensus builds up around a certain topic. On the other hand, recent works have highlighted the competitive nature of information systems, in which humans and memes strive for scarce resources --visibility and attention, respectively. Here, we apply an ecosystems approach to show that such a competitive environment drives the emergence of modular-to-nested architectures. Specifically, we consider information-driven systems, such as online social platforms, as mutualistic networks, in which users and memes (the nodes of the bipartite graph) compete for resources within their respective classes, and cooperate without. This setting yields longitudinal observation of initially modular, then nested structures for different types of topics from large, public collections of online time-stamped microblogging data. More importantly, intensive numerical simulations allow us to explain the driving force underlying the observed modular-to-nested transition, under the logic of a dynamical model of mutualistic systems.