González-Bailón, Sandra
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Publication Presentation on "Network Science and the Study of Political Protests"(2015-05-01) González-Bailón, SandraPublication The Advantage of the Right in Social Media News Sharing(Oxford University Press, 2022) González-Bailón, SandraWe analyze social media activity during one of the largest protest mobilizations in US history to examine ideological asymmetries in the posting of news content. Using an unprecedented combination of four datasets (tracking offline protests, social media activity, web browsing, and the reliability of news sources), we show that there is no evidence of unreliable sources having any prominent visibility during the protest period, but we do identify asymmetries in the ideological slant of the sources shared on social media, with a clear bias towards right-leaning domains. These results support the “amplification of the right” thesis, which points to the structural conditions (social and technological) that lead to higher visibility of content with a partisan bent towards the right. Our findings provide evidence that right-leaning sources gain more visibility on social media and reveal that ideological asymmetries manifest themselves even in the context of movements with progressive goals.Publication Semantic Networks and Applications in Public Opinion Research(2018-01-01) Yang, Sijia; González-Bailón, SandraPublication Presentation on "Measuring Social Phenomena"(2016-06-01) González-Bailón, Sandra; Strohmaier, MarkusPublication 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 Online Social Networks and Bottom-Up Politics(2014-01-01) 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 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.Publication Diffusion Dynamics With Changing Network Composition(2013-01-01) Baños, Raquel A; Borge-Holthoefer, Javier; Wang, Nang; Moreno, Yamir; González-Bailón, SandraWe analyze information diffusion using empirical data that tracks online communication around two instances of mass political mobilization that took place in Spain in 2011 and 2012. We also analyze protest-related communications during the year that elapsed between those protests. We compare the global properties of the topological and dynamic networks through which communication took place, as well as local changes in network composition. We show that changes in network structure underlie aggregated differences on how information diffused: an increase in network hierarchy is accompanied by a reduction in the average size of cascades. The increasing hierarchy affects not only the underlying communication topology but also the more dynamic structure of information exchange; the increase is especially noticeable amongst certain categories of nodes (or users). Our findings suggest that the relationship between the structure of networks and their function in diffusing information is not as straightforward as some theoretical models of diffusion in networks imply.