Sandra González-Bailón

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.

Broadcasters and Hidden Influentials in Online Protest Diffusion

This article explores the growth of online mobilizations using data from the indignados (outraged) movement in Spain, which emerged under the influence of the revolution in Egypt and as a precursor to the global Occupy mobilizations. The data track Twitter activity around the protests that took place in May 2011, which led to the formation of camp sites in dozens of cities all over the country and massive daily demonstrations during the week prior to the elections of May 22. We reconstruct the network of tens of thousands of users and monitor their message activity for a month (April 25, 2011, to May 25, 2011). Using both the structure of the network and levels of activity in message exchange, we identify four types of users and analyze their role in the growth of the protest. Drawing from theories of online activism and research on information diffusion in networks, this article centers on the following two questions: How does protest information spread in online networks? And how do different actors contribute to the growth of activity? The article aims to inform the theoretical debate on whether digital technologies are changing the logic of collective action and to provide evidence of how new media facilitates the emergence of massive offline mobilizations.

The structure of political discussion networks: a model for the analysis of online deliberation

This paper shows that online political discussion networks are, on average, wider and deeper than the networks generated by other types of discussions: they engage a larger number of participants and cascade through more levels of nested comments. Using data collected from the Slashdot forum, this paper reconstructs the discussion threads as hierarchical networks and proposes a model for their comparison and classification. In addition to the substantive topic of discussion, which corresponds to the different sections of the forum (such as Developers, Games, or Politics), we classify the threads according to structural features like the maximum number of comments at any level of the network (i.e. the width) and the number of nested layers in the network (i.e. the depth). We find that political discussion networks display a tendency to cluster around the area that corresponds to wider and deeper structures, showing a significant departure from the structure exhibited by other types of discussions. We propose using this model to create a framework that allows the analysis and comparison of different internet technologies for the promotion of political deliberation.

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.

Cascading Behaviour in Complex Socio-Technical Networks

Most human interactions today take place with the mediation of ICTs. 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.

The Critical Periphery in the Growth of Social Protests.

Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as “slacktivists” because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.

Social Science in the Era of Big Data

Digital technologies keep track of everything we do and say while we are online, and we spend online an increasing portion of our time. Databases hidden behind web services and applications are constantly fed with information of our movements and communication patterns, and a significant dimension of our lives, quantified to unprecedented levels, gets stored in those vast online repositories. This article considers some of the implications of this torrent of data for social science research, and for the types of questions we can ask of the world we inhabit. The goal of the article is twofold: to explain why, in spite of all the data, theory still matters to build credible stories of what the data reveal; and to show how this allows social scientists to revisit old questions at the intersection of new technologies and disciplinary approaches. The article also considers how Big Data research can transform policy making, with a focus on how it can help us improve communication and governance in policy-relevant domains.

Opening the black box of link formation: Social factors underlying the structure of the web

Links play a twofold role on the web: they open the channels through which users access information, and they determine the centrality of sites and their visibility. This paper adds two factors to the analysis of links that aim to draw a parallel between the web and other offline interorganisational networks: the resources that the organisations publishing online are able to mobilise, and the status or public recognition of those organisations. Exponential random graph models (ERGMs) are used to analyse a sample of the web of about one thousand sites, showing that both the economic resources of the producers of the sites (a proxy to their wider pool of resources) and their presence in traditional news media (a proxy to their status) significantly increase their probability of receiving more links, and therefore, their centrality. This adds a sociologically relevant dimension to the analysis of the web that has been disregarded so far but that is crucial to understand the way it distributes visibility.

Networked Discontent: The Anatomy of Protest Campaigns in Social Media

We investigate how online networks mediate contentious politics by analysing communication around a global campaign launched in May of 2012. We analyse about 450,000 Twitter messages related to the Occupy and ‘indignados’ movements sent by about 125,000 users during the period of one month, which included the demonstrations to celebrate the first anniversary of the ‘indignados’ movement. We analyse the overall connectivity of the network (to test how well integrated the two movements are); the network position of brokers (to identify users posting content relevant to both movements); and the robustness of the network to node removal (to determine its resilience). We find that global connectivity depends on a small percentage of users, many – but not all of them – brokers, and that the two movements are mostly concerned with their local struggles: the bridges connecting them channel just a small percentage of all information exchanged. We use these findings to assess theoretical claims about political protests in the digital age.

The Dynamics of Information-Driven Coordination Phenomena: A Transfer Entropy Analysis

This work defines the framework to explore the spatiotemporal signature of emergent collective phenomena on social media. Data 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.