The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.

Many epidemic processes in networks spread by stochastic contacts among their connected vertices. There are two limiting cases widely analyzed in the physics literature, the so-called contact process (CP) where the contagion is expanded at a certain rate from an infected vertex to one neighbor at a time, and the reactive process (RP) in which an infected individual effectively contacts all its neighbors to expand the epidemics. However, a more realistic scenario is obtained from the interpolation between these two cases, considering a certain number of stochastic contacts per unit time. Here we propose a discrete-time formulation of the problem of contact-based epidemic spreading. We resolve a family of models, parameterized by the number of stochastic contact trials per unit time, that range from the CP to the RP. In contrast to the common heterogeneous mean-field approach, we focus on the probability of infection of individual nodes. Using this formulation, we can construct the whole phase diagram of the different infection models and determine their critical properties.

During the last ten years several studies have appeared regarding language complexity. Research on this issue began soon after the burst of a new movement of interest and research in the study of complex networks, i.e., networks whose structure is irregular, complex and dynamically evolving in time. In the first years, network approach to language mostly focused on a very abstract and general overview of language complexity, and few of them studied how this complexity is actually embodied in humans or how it affects cognition. However research has slowly shifted from the language-oriented towards a more cognitive-oriented point of view. This review first offers a brief summary on the methodological and formal foundations of complex networks, then it attempts a general vision of research activity on language from a complex networks perspective, and specially highlights those efforts with cognitive-inspired aim.Keywords: complex network; language; universality; cognition Language is surely a strikingly complex object to study. It is built upon many thousands of items (WordNet database [1], for instance, currently gathers over 200,000 words in English), which in turn are often ambiguous. Also, they hold manifold relationships with each other, sometimes for the sake of structure (syntax), sometimes for the sake of their meaning (semantics). Words, and the relationships they hold, evolve across time, new words appear, some of them die, some simply change. As difficult as it may be to study or model these facts, complexity is even greater when language is placed in a cognitive context: linguistic production and comprehension processes occur proficiently in the mind while many other processes are concurrently interacting. Consider, among them, actions from the Entropy 2010, 12 1265 sensorimotor system (vocalization), the perceptual system (listening, reading) or memory (retrieval, recall and recognition). Finally, a last step to complexity is to consider linguistic performance as a result of neural activity. Language, thus, is a complex object efficiently managed in a complex mental context, which in turn is embodied in the most complex known system, the brain.Linguistics and psycholinguistics devote much efforts to disentangle the details of the aforementioned facts. However, some fundamental questions can not be addressed from this fine-grained perspective: what is the general structure of language? Is such structure common to every language? Can we describe the general trends of the mechanisms that provide for linguistic efficient performance? Is it possible to describe the principles of language growth (from a child to an adult)? Such questions demand a complementary point of view from that of linguistics and psycholinguistics, one that abstracts and simplifies as much as possible the intricate nature of language. This general view makes the minimum assumptions, in the end language is reduced to a set of entities which are related with each other. Following this line, cognitive processes are characterized...

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.

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