González-Bailón, Sandra
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Publication Presentation on "Measuring Social Phenomena"(2016-06-01) González-Bailón, Sandra; Strohmaier, MarkusPublication Presentation on "Network Science and the Study of Political Protests"(2015-05-01) González-Bailón, SandraPublication Seminar on "The Emergence of Roles in Large-Scale Networks of Communication"(2014-09-01) González-Bailón, SandraCommunication through social media is becoming more prevalent in dynamics of coordination and information diffusion. However, online networks are so large and complex that we require new methods to summarize their structure and identify nodes holding relevant positions. We propose a method that generalizes the sociological theory of brokerage, originally devised on the basis of local transitivity and paths of length two, to make it applicable to larger, more complex structures. Our method makes use of the modular structure of networks to define brokerage at the local and global levels. The findings show that the method is better able to capture differences in communication dynamics than alternative approaches that only consider local or global network features.Publication Online Social Networks and Bottom-Up Politics(2014-01-01) González-Bailón, SandraPublication Semantic Networks and Applications in Public Opinion Research(2018-01-01) Yang, Sijia; González-Bailón, SandraPublication Automated Content Analysis of Online Political Communication(2015-01-01) Gonález-Bailón, Sandra; Petchler, RossPublication The Dynamics of Information-Driven Coordination Phenomena: A Transfer Entropy Analysis(2016-04-01) Borge-Holthoefer, Javier; Perra, Nicola; Goncalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, AlessandroData from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.Publication ¿SOCIEDADES ARTIFICIALES? Una introducción a la simulación social(2004-09-01) Gonález-Bailón, SandraThese lines have a twofold objective: to introduce social simulation as a technique of analysis, and to offer a view of its uses and possibilities. The article starts with a couple of examples aimed to illustrate the practice of simulation. The methodological aspects associated to it are then exposed to, finally, highlight the pros (and cons) of its use. The aim of social simulation is the same as that of statistical equations and ideal types: to generate models of the social reality that help us to build or validate theories about its regularities. What turns social simulation into an innovative analytical strategy is not its objectives but rather its means: programming algorithms that contain the behavioural rules of agents that interact among them and with their environment. The advantages of social simulation are not only experimental but also, and specially, theoretic: it allows the search of the mechanisms that statistical models cannot provide.Publication Cascading Behaviour in Complex Soci-Technical Networks(2013-04-22) Borge-Holthoefer, Javier; Baños, Raquel A.; González-Bailón, Sandra; Moreno, YamirMost human interactions today take place with the mediation of information and communications technology. This is extending the boundaries of interdependence: the group of reference, ideas and behaviour to which people are exposed is larger and less restricted to old geographical and cultural boundaries; but it is also providing more and better data with which to build more informative models on the effects of social interactions, amongst them, the way in which contagion and cascades diffuse in social networks. Online data are not only helping us gain deeper insights into the structural complexity of social systems, they are also illuminating the consequences of that complexity, especially around collective and temporal dynamics. This paper offers an overview of the models and applications that have been developed in what is still a nascent area of research, as well as an outline of immediate lines of work that promise to open new vistas in our understanding of cascading behaviour in social networks.Publication The Role of Dynamic Networks in Social Capital: A Simulation Experiment(2006-01-01) Gonález-Bailón, SandraThis paper has two basic aims: the first is to understand why networks matter in the creation and maintenance of social capital; the second is to explore many of the (unproved) assumptions that arise when social capital is applied to the field of political participation. A simulation- based experiment is used to achieve both aims. The paper starts by delimiting the scope of the theoretical problem. It then reviews the assumptions made in the literature about the role networks play for social capital, and integrates them with what is known about dynamic networks. The third section provides a brief introduction to the methodological nature of simulation. It justifies the appropriateness of this technique to tackle the questions posed by the existing theory. A description of the simulation model and its results follows. The first set of experiments explores the structural properties of different networks in respect of information diffusion. The second set analyses a principle of action that might be responsible for the formation of social capital networks. The implications that these results have for the theory are assessed in the conclusion. Their links to future research are also discussed.