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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 me more central to 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.

Significance Online networks carry benefits and risks with high-stakes consequences during contentious political events: They can be tools for organization and awareness, or tools for disinformation and conflict. We combine social media and web-tracking data to measure differences on the visibility of news sources during two events that involved massive political mobilizations in two different countries and time periods. We contextualize the role of social media as an entry point to news, and we cast doubts on the impact that bot activity had on the coverage of those mobilizations. We show that verified, blue-badge accounts were significantly more visible and central. Our findings provide evidence to evaluate the role of social media in facilitating information campaigns and eroding traditional gatekeeping roles.

Social life is full of paradoxes. Our intentional actions often trigger outcomes that we did not intend or even envision. How do we explain those unintended effects and what can we do to regulate them? In Decoding the Social World, Sandra González-Bailón explains how data science and digital traces help us solve the puzzle of unintended consequences -- offering the solution to a social paradox that has intrigued thinkers for centuries. Communication has always been the force that makes a collection of people more than the sum of individuals, but only now can we explain why: digital technologies have made it possible to parse the information we generate by being social in new, imaginative ways. And yet we must look at that data, González-Bailón argues, through the lens of theories that capture the nature of social life. The technologies we use, in the end, are also a manifestation of the social world we inhabit. González-Bailón discusses how the unpredictability of social life relates to communication networks, social influence, and the unintended effects that derive from individual decisions. She describes how communication generates social dynamics in aggregate (leading to episodes of'collective effervescence') and discusses the mechanisms that underlie large-scale diffusion, when information and behavior spread'like wildfire.'She applies the theory of networks to illuminate why collective outcomes can differ drastically even when they arise from the same individual actions. By opening the black box of unintended effects, González-Bailón identifies strategies for social intervention and discusses the policy implications -- and how data science and evidence-based research embolden critical thinking in a world that is constantly changing.

AbstractDigital 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 policymaking, with a focus on how it can help us improve communication and governance in policy‐relevant domains.

This 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. ; El objetivo de este artículo es doble: por un lado, entender por qué importan las redes en la creación y mantenimiento de capital social y, por otro, explorar muchas de las asunciones (no probadas) que surgen cuando el concepto de capital social se aplica al campo de la participación política. Ambos objetivos se llevan a cabo con la ayuda de un experimento de simulación. El artículo empieza exponiendo los términos del problema teórico. Prosigue con un resumen de las asunciones que aparecen en la literatura sobre el rol que las redes juegan en el funcionamiento del capital social y las contrasta con lo que se sabe acerca del funcionamiento de redes dinámicas. La tercera sección proporciona una breve introducción a la naturaleza metodológica de la simulación multi-agente. Le sigue una descripción del modelo de simulación y de sus resultados. El primer conjunto de experimentos explora las propiedades estructurales de distintas redes respecto a la difusión de información. El segundo conjunto analiza un principio de acción responsable de la formación de redes de capital social. Las implicaciones teóricas de estos resultados son valoradas en la conclusión. También se discuten futuras líneas de investigación.

Significance Access to diverse news strengthens democratic citizenship. Whether digital technologies have narrowed or widened news diets fosters contentious debates. Previous research shows the abundance of digital news sources might be leading to more fragmented audiences, ideological segregation, and echo chambers. Our study resorts to an unprecedented combination of data to show that the increase in mobile access to news actually leads to higher exposure to diverse content and that ideological self-selection explains only a small percentage of co-exposure to news. We also find that more than half of Internet users in the United States do not use online news. Future research should avoid generalizations from desktop-only data and pay attention to the increasing divide between informed citizens and news avoiders.

AbstractWe evaluate the empirical evidence interrogating the question of whether social media erodes social cohesion. We look at how networks, information exchange, and norms operate on these platforms. We also evaluate the conditions under which social media can be conducive to forming social capital and encouraging prosocial behavior. We discuss the psychological mechanisms that operate at the individual level and assess whether social media can create the environment and incentives to sustain cooperation and constructive exchange. Our discussion of the literature centers on how attitudes, perceptions, and beliefs are formed during the type of online interactions encouraged by platforms, their design, and affordances. We consider the policy implications of existing research, focusing on how empirical studies may inform regulatory efforts and platform interventions.

This study offers a systematic comparison of automated content analysis tools. The ability of different lexicons to correctly identify affective tone (e.g., positive vs. negative) is assessed in different social media environments. Our comparisons examine the reliability and validity of publicly available, off-the-shelf classifiers. We use datasets from a range of online sources that vary in the diversity and formality of the language used, and we apply different classifiers to extract information about the affective tone in these datasets. We first measure agreement (reliability test) and then compare their classifications with the benchmark of human coding (validity test). Our analyses show that validity and reliability vary with the formality and diversity of the text; we also show that ready-to-use methods leave much space for improvement when analyzing domain-specific content and that a machine-learning approach offers more accurate predictions across communication domains.