Healthy Skepticism Library item: 19776

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Publication type: Journal Article

González-Bailón S, Borge-Holthoefer J, Rivero A, Moreno Y
The Dynamics of Protest Recruitment through an Online Network
Sci Rep. 2011 Dec 15; 1:197
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3240992/

Abstract:

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.
The last few years have seen an eruption of political protests aided by internet technologies. The phrase Twitter revolution was coined in 2009 to refer to the mass mobilizations that took place in Moldova1 and, a few months later, in Iran2, in both cases to protest against fraudulent elections. Since then, the number of events connecting social media with social unrest has multiplied, not only in the context of authoritarian regimes exemplified by the recent wave of upsurges across the Arab world but also in western liberal democracies, particularly in the aftermath of the financial crisis and changes to welfare policies. These protests respond to very different socio-economic circumstances and are driven by very different political agendas, but they all seem to share the same morphological feature: the use of social networking sites (SNSs) to help protesters self-organize and attain a critical mass of participants. There is, however, not much evidence on how exactly SNSs encourage recruitment. Empirical research on online activity around riots and protests is scarce, and the few studies that exist3,4,5 show no clear patterns of protest growth. Related research has shown that information cascades in online networks occur only rarely6,7,8, with the implication that even online it is difficult to reach and mobilize a high number of people. Revolutions, riots and mass mobilizations are also rare and, as such, difficult to predict; but when they happen, they unleash potentially dramatic consequences. The relevant question, which we set to answer here, is not when these protests take place but whether and how SNSs contribute to trigger their explosion.
Sociologists have long analyzed networks as the main recruitment channels through which social movements grow9,10. Empirical research has shown that networks were crucial to the organization of collective action long before the internet could act as an organizing tool, with historical examples that include the insurgency in the Paris commune of 187111, the 60’s civil right struggles in the U.S.12, and the demonstrations that took place in East Germany prior to the fall of the Berlin wall13,14. These studies provide evidence that recruits to a movement tend to be connected to others already involved and that networks open channels through which influence on behavior spreads, but they are limited by the quality of the network data analyzed, particularly around time dynamics. Analytical models have tried to overcome these data limitations by recreating the formal features of interpersonal influence, and analyzing how they are related to diffusion15,16,17,18 and to examples of social contagion like collective action or the growth of social movements19,20,21,22,23. Four main findings arise from these models. First, the shape of the threshold distribution, i.e. the variance in the propensity to join intrinsic to people, determines the global reach of cascades. Second, individual thresholds interact with the size of local networks: two actors with the same propensity might be recruited at different times if one is connected to a larger number of people. Third, attaining a critical mass depends on being able to activate a sufficiently large number of low threshold actors that are also well connected in the overall network structure. And fourth, the exposure to multiple sources can be more important than multiple exposures: unlike epidemics, the social contagion of behavior often requires reinforcement from multiple people. Recent experiments have confirmed the relevance of complex contagions to explain behavior in online contexts24, and large-scale analyses have validated its effects on information diffusion on Twitter25.
Models of collective action have identified important network mechanisms behind the decision to join a protest, but they suffer from lack of empirical calibration and external validity. Online networks, and the role that SNSs play in articulating the growth of protests, offer a great opportunity to explore recruitment mechanisms in an empirical setting. We analyze one such setting by studying the protests that took place in Spain in May 2011. The mobilization emerged as a reaction to the political response to the financial crisis and it organized around broad demands for new forms of democratic representation. The main target of the campaign was to organize a protest on May 15, which brought tens of thousands of people to the streets of 59 cities all over the country. After the march, hundreds of participants decided to camp in the city squares until May 22, the date for local and regional elections; crowded demonstrations took place daily during that week. After the elections, the movement remained active but the protests gradually lost strength and its media visibility waned (more background information in SI).
We analyze Twitter activity around those protests for the period April 25 (20 days before the first mass mobilizations) to May 25 (10 days after the first mass mobilizations, and 3 days after the elections). The data set follows the posting behavior of 87,569 users and tracks a total of 581,750 protest messages (see Methods). We know, for each user, who they follow and who is following them. In addition to this asymmetric network, we also consider a version of the network that only retains reciprocated and therefore stronger connections. Previous research has suggested that Twitter is closer to a news media platform than to a social network7; this research suggests that the properties of the online network cannot be directly compared to other social networks because of the prominence of broadcasters. The symmetric (reciprocated) network mitigates the relatively higher influence of these hubs of activity and retains only connections that reflect mutual acknowledgement between users, which is arguably a stronger proxy to offline relationships. Contrasting recruitment patterns in both the asymmetric and symmetric networks allows us to test whether the dynamics of mobilization depend on weak, broadcasting links or on stronger connections, based on mutual recognition. Our analysis of recruitment is based on the assumption that users joined the movement the moment they started sending Tweets about it. We also assume that once they are activated, they remain so for the rest of the period we consider.