Alejandro Rivero

The Dynamics of Protest Recruitment through an Online Network

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.

Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study

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.

Assessing the Bias in Samples of Large Online Networks

We consider the sampling bias introduced in the study of online networks when collecting data through publicly available APIs (application programming interfaces). We assess differences between three samples of Twitter activity; the empirical context is given by political protests taking place in May 2012. We track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the search and the streaming APIs, and to different filtering parameters. We find that smaller samples do not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions, partly because of the higher influence of snowballing in identifying relevant nodes. We discuss the implications of this bias for the study of diffusion dynamics and political communication through social media, and advocate the need for more uniform sampling procedures to study online communication.

Locating privileged spreaders on an online social network.

Social media have provided plentiful evidence of their capacity for information diffusion. Fads and rumors but also social unrest and riots travel fast and affect large fractions of the population participating in online social networks (OSNs). This has spurred much research regarding the mechanisms that underlie social contagion, and also who (if any) can unleash system-wide information dissemination. Access to real data, both regarding topology--the network of friendships--and dynamics--the actual way in which OSNs users interact, is crucial to decipher how the former facilitates the latters success, understood as efficiency in information spreading. With the quantitative analysis that stems from complex network theory, we discuss who (and why) has privileged spreading capabilities when it comes to information diffusion. This is done considering the evolution of an episode of political protest which took place in Spain, spanning one month in 2011.

The Spanish “Indignados” Movement: Time Dynamics, Geographical Distribution, and Recruitment Mechanisms

Online social networks have an enormous impact on opinions and cultural trends. Also, these platforms have been revealed as a fundamental organizing mechanism in country-wide social movements. Recent events in the Middle East and North Africa (the wave of protests in the Arab world), across Europe (in the form of anti-cuts demonstrations or riots) and in the United States have generated much discussion on how digital media is connected to the diffusion of protests. In this chapter, we investigate, from a complex network perspective, the mechanisms driving the emergence, development and stabilization of the “Indignados” movement in Spain, analyzing data from the period between April 25 and May 26, 2011. Using 70 keywords related to the movement, we analyze 581,749 Twitter messages coming from 87,569 users. The online trace of the 15M protests provides a unique opportunity to tackle central issues in the social network literature like recruitment patterns or information cascades. These findings shed light on the connection between online networks and social movements and offer an empirical test to elusive sociological questions about collective action.

Analysis of academic productivity based on Complex Networks

We present a new tool, Kampal (http://kampal.unizar.es), developed to help to analyze the academic productivity of a research institution from the point of view of Complex Networks. We will focus on two main aspects: paper production and funding by research grants. Thus, we define a network of researchers and define suitable ways of describing their interaction, either by co-publication, project-collaboration, or a combination of both. From the corresponding complex networks, we extract maps which encode in graphical terms the relevant information and numerical parameters which encode the topological properties of the network. Thousands of these maps have been created and allow us to study the similarities and differences of the co-publications and the project-collaboration networks.