Abstract 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.

Citation: Barberá P, Wang N, Bonneau R, Jost JT, Nagler J, Tucker J, et al. (2015) The Critical Periphery in the Growth of Social Protests. PLoS ONE 10(11): e0143611. https://doi.org/10.1371/journal.pone.0143611 Editor: Stephen C. Pratt, Arizona State University, UNITED STATES Received: May 12, 2015; Accepted: November 6, 2015; Published: November 30, 2015 Copyright: © 2015 Barberá et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: All relevant data are available from the Dataverse database: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WCXK3Z. Funding: The authors gratefully acknowledge financial support from the INSPIRE program of the National Science Foundation (Award # SES-1248077), the New York University Research Investment Fund sponsored by Dean Thomas Carew, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Do social media help raise awareness of political causes or do they simply encourage “feel-good” politics with hollow consequences? The wave of international protests that began in 2011 spurred controversy about whether online networks serve or impede the goals of mobilization [1–3]. The question is revived every time mass mobilizations ripple through social media, most recently during the umbrella revolution staged by protesters in Hong Kong in 2014–2015 [4]. The debate often arises when the daring minority of highly committed protesters is compared with the less heroic majority of followers who may risk relatively little, posting messages comfortably from a distance. This depiction, however, fails to acknowledge the complex forces that are at play in the current media environment, including the synergies that both core and peripheral participants create in the process of starting and scaling up visibility of the protest movement. In this article we characterize these synergies and devise a strategy to measure the relevance of the immense periphery, gauging their role in contributing an important mobilization resource–the ability to disseminate information about the protest events. Research on collective action has long emphasized the importance of resource mobilization to understand the success of social movements [5]. Generalized discontent is a precondition for social protests to arise, but the ability to reach, recruit, and organize participants is what allows initial sparks to spread like ‘wildfires’, to use one common analogy in historical accounts of contentious politics [6]. Resource mobilization theories often measure success by the number of participants mobilized, which is assumed to increase as a function of the number of participants who are already active [7, 8]. Critical mass theories aim to identify the factors that cause participation to become self-sustaining [9, 10]; that is, they focus on how and why the number of participants reaches a tipping point—the moment when the spark becomes a wildfire. Social networks are central to the analytical treatment of these questions: they provide the structure of interdependence that shapes individual decision-making (i.e., whether to join a given protest), and they channel the propagation of activation signals (i.e., how many other people are already participating). In other words, networks hold the key to decoding the social logic of protests. Most of the insights gained so far on how networks mediate collective action derive from simulations and mathematical models that consider a short list of network properties; in particular, prior research has emphasized the effects of density, degree distribution, degree centralization, network size, and the prevalence of weak ties [10–14]. In addition, these formal models often assume stylized networks that are convenient analytically but not very useful for accurately depicting real-world social networks, which are far too complex to be fully characterized in terms of the properties just listed. For instance, the core-periphery structure seen in many empirically observed networks [15] has been neglected in work aimed at testing critical mass theories. Fortunately, the increasing availability of data logs tracking large-scale communication provides new research opportunities to analyze dynamics that are obscured by more traditional data sources [16–18]; in particular, communication dynamics in social media help to reveal the contribution that peripheral actors make to collective action efforts.

Data and Methods To address these questions, we make use of three datasets tracking Twitter communication concerning three protest events spanning different languages and political contexts. The first dataset tracks communication about Twitter activity around the protests that emerged in Istanbul’s Taksim Gezi Park in May 2013. The protests started as a sit-in against urban development plans but soon escalated into massive antigovernment demonstrations. We sampled activity for the period May 31 to June 29, 2013 using Twitter’s streaming API, querying for messages that contained hashtags related to the protests. We selected these terms by observing the trending topics in Turkey and in consultation with local experts (see [19] for the full list of terms). Our selection of keywords is consistent with other studies of the Turkish protests [20]. We collected a total of 30,019,710 messages sent by 2,908,926 unique users. Although this dataset is likely to exclude many tweets related to the protest that did not mention one of the main protest hashtags, we believe this is decision should not affect our results (see S1 Text for a discussion of potential biases related to our data collection process). We obtained the number of followers associated with each sampled user. We also have information about the location of some users, which we obtained from tweets that contained the geographic coordinates from which they were sent. This information will allow us to identify users who sent at least one tweet in the area around Taksim Gezi Park in Istanbul, which we consider an indication that they participated in the protest. Finally, we parsed the messages to identify retweets, which allowed us to reconstruct the network of information flow around the protest. The second and third datasets track Twitter activity around the international call for action “United for Global Change,” a demonstration planned for May 12, 2012. This was the second call for action under the same slogan, the first having been organized in October 15, 2011 as part of the wave of protests that started with the Arab Spring, and continued with the Spanish Indignados and the Occupy movement. This second call for action was less successful at mobilizing people and raising public awareness. We sampled activity for the period April 30 to May 30, 2012 using the search API, querying for messages that contained hashtags related to the Indignados movement (identified in previous work [21, 22]) and variations of the word Occupy*. A total of 606,625 messages sent by 125,219 unique users were collected. As with the Turkish dataset, we also obtained the number of followers associated with each sampled user and parsed the messages to identify retweets, which allowed us to reconstruct the networks we will examine in our analysis. To determine whether the core-periphery dynamics we identify are characteristic of protest communication or they are, instead, simply widespread in communication around all types of conversations on Twitter, including those unrelated to protests, we replicated our analyses with two additional datasets. The first contained messages related to the 2014 Academy Awards. We sampled activity for the period March 2, 2014 at 2pm ET to Wednesday March 5 at 2pm ET using the streaming API, querying for the keywords “oscars”, “#oscars2014”, and “academy awards”. We collected a total of 7,527,157 tweets sent by 3,910,627 unique users. The second dataset is formed by messages related to the debate about raising the minimum wage in the United States. We sampled activity for the period February 3, 2014 to February 2rd, 2015 using the streaming API, querying for the keywords “minimum wage”, “minimum hourly wage”, “raise the wage”, “#giveamericaaraise” (and other less frequent hashtags). In total, we collected 2,957,847 tweets sent by 1,199,414 unique users. We analyzed the network of retweets among participants, which we defined as users who employed relevant hashtags. We treat retweets as the directed ties that link users in a network of information exchange, where each edge originates in the user who retweets and ends in the user whose message is retweeted. Table 1 shows high-level statistics for the five networks analyzed. The indegree distributions, reciprocity levels, and degree correlation all suggest a hierarchical, core-periphery structure with a minority of participants at the core. For example, the maximum indegree (number of times retweeted) is 181,387, whereas the maximum outdegree (number of retweets sent) is 2,397. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Table 1. Summary statistics for the three retweet networks analyzed (largest weakly connected component). https://doi.org/10.1371/journal.pone.0143611.t001 In addition to the network of RTs, we use the total number of messages containing any of the relevant hashtags for each collection (regardless of whether they are RTs) as node attribute information. We also count, for each node, the total number of followers. These two attributes are the basis for our measurement of activity and reach. We measure reach as the fraction of followers that every participant has over the total number of followers in the network. As Fig 1 illustrates, this means that some followers (like node l) are counted more than once. Because of the clustering in the Twitter network [23], our count probably overestimates the number of unique users who are exposed to protest information; however, prior research on complex contagion and the psychological effects of repeated exposure suggests that the adoption of a given behavior (such participation in a protest) is more likely after individuals become familiar with the stimuli [13, 14, 24]. Psychological research also shows that familiarity with statements increases the likelihood that those statements will be judged to be valid and true [25, 26]. By counting the same followers more than once, we tap into the mobilization potential of repeated exposure–all else equal, node l will be more likely to join the flow of protest communication than other followers who are only exposed to protest information from a single source. For the same reason, we assume that a similar feedback mechanism exists among participants who are following other participants: the more information they are exposed to, the more engaged they are likely to become. Although we cannot disentangle any causal relationships given the observational nature of our data, this positive feedback is likely to drive (at least in part) the higher density of ties we identify at the core of the network, where participants are on average more active in posting messages and retweets. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Fig 1. Reach measured as a fraction of all followers. In this schematic representation, there are three protest participants that accumulate six unique followers. The relative reach of each participiant (nodes in red) is the fraction of their direct followers over the total available in the system (nodes in orange). We normalize these counts to fall in the interval [0,1] for the three networks. https://doi.org/10.1371/journal.pone.0143611.g001 Overall, our measure of reach is roughly comparable to more standard measures of audience share in media market studies (e.g., Nielsen ratings). We measure how many Twitter users are exposed to protest-related information through at least one of the users they decided to follow. This is similar to measuring the share of households with their televisions or radios on that are tuned to a particular channel. We cannot be certain that followers are reading the protest messages that appear in their feeds, much as rating measurements provide no guarantee that members of a household tuning into a particular program are actually paying attention. We identify core and peripheral participants using the k-core decomposition technique, which partitions a network in nested shells of connectivity [27, 28]. The k-core of a graph is the maximal subgraph in which every vertex has at least degree k. In our case, degree relates to the number of retweets made or received. The k-core decomposition is a recursive approach that progressively trims the least connected nodes in a network (i.e. those with lower degree) in order to identify the most central ones. Fig 2 illustrates the k-core decomposition of a random graph with 19 vertices and 24 edges. Node degree is in the range of 1 to 5, but there are only four cores. Since the method is recursive, some of the nodes with degree 5 end up being classified in lower k-shells. Nodes classified in higher k-shells not only have higher degree: they are also connected to nodes that are central as well. Low engagement participants are classified in lower k-shells, and they form the periphery of the network. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Fig 2. Schematic representation of the k-core decomposition for a random network with N = 16 vertices and E = 24 edges. This technique recursively prunes the network to remove nodes with the lowest degree. The coreness of a vertex is k if it belongs to the k-core but not to the (k+1)-core. https://doi.org/10.1371/journal.pone.0143611.g002 We use the network of retweets to distinguish between core and peripheral participants. However, not all messages relevant in the dissemination of protest-related information are retweets to other users. This is clearly the case for individuals at the protests themselves, who are perfectly capable of producing original tweets with relevant information; this even holds for those not at the protests, who may write original tweets summarizing what they have learned from friends, colleagues, and other sources of new and traditional media. Consider someone watching a Twitter feed and seeing five tweets in a row related to the use of teargas. This person could choose to retweet one of those five tweets, but could just as easily tweet something along the lines of “multiple tweets reporting teargas at #gezipark”. We therefore measure protest activity as the total number of messages that contained at least one protest-related hashtag, regardless of whether that message was a retweet. For the non-protest-related network, we also measure activity as the total number of messages containing at least one hashtag or keyword related to the event. We produce three metrics: one at the level of individual participants, one at the level of k-cores, and one at the level of the entire network, which we use to assess the share that each participant and k-core contributes to overall activity volume. Our analyses assess the impact of peripheral users by simulating how removing them from the network would affect the two outcome variables, audience and reach, in comparison to a random benchmark. This benchmark is based on a random assignment of k-core values (sampled without replacement, averaged over 10,000 permutations). The benchmark can, in fact, be interpreted as a line of perfect equality, that is, it plots what would happen if all k-cores contributed the same amount to overall activity and reach. The farther the reach and activity curves fall from this line, the more unequally distributed these resources are in the network.

Discussion Our findings suggest that peripheral users in online protest networks may be as important in expanding the reach of messages as the highly committed minority at the core. We do not find equivalent patterns in datasets that concern topics unrelated to political protests. Peripheral users possess potentially valuable mobilization resources that greatly increase the number of online individuals who are exposed to protest messages initiated by core participants. Given the power of the Turkish mainstream media to censor information, Gezi Park protest organizers needed to spread news through online networks, and they did so successfully: the protests became international news in a matter of hours [31]. Protestors at the core of this network–a large proportion of whom were actual protesters on the ground, as evidenced by the location of the messages they shared on Twitter (see S1 Text)–would have had a much harder time reaching such a large online audience without the critical periphery of low-activity users. Our analysis suggests that, because of their numbers, peripheral users managed to generate a great deal of activity in Twitter related to this cause. This does not imply that social media can always be used to activate the critical periphery to the same degree. In some cases, the periphery might not be large enough to increase the audience of protest messages and, as a result, fail to raise sufficient awareness of the actions taken by the committed minority. Indeed, the global demonstrations planned for May of 2012 were not as successful in terms of their media impact as the first wave of Indignados and Occupy protests in 2011 [32]. Decades of research on resource mobilization shows that there are many factors involved in the success of collective action [33]. Social media is just one instrument that needs to be played effectively. Independent of the social and material constraints that might restrict mobilization, our findings demonstrate that relatively low commitment participants–who are often derided as feel-good activists or “slacktivists” [2, 3]–are potentially very important as a collective. By expanding the audience of messages sent by the committed minority, the periphery can amplify the core voices and actions, and thus provide a way for larger numbers of online citizens to be exposed to news and information about the protest, even (or especially) in the absence of mass media coverage. The availability of information about protest events is important because it can increase support for opposition parties [34] and lower the effective cost of participating, potentially leading to broader anti-regime action [35], as evidenced by the close correspondence between online protest activity and offline collective action [20]. A question that requires further exploration is whether the specific characteristics of the events we considered (such as their geographical location, the nature of the protests, the disruptive force of the actions taken) made them uniquely effective at activating a large periphery. Ultimately, core-periphery dynamics in online networks are the result of exogenous factors shaping the process; and yet, much in the same way as indexes of inequality like the Gini coefficient make it possible to compare countries that are in many respects incomparable, we believe that quantifying core-periphery dynamics can help us to compare episodes of large-scale social coordination, even if they arise in very different circumstances. Future work should consider alternative data sets and investigate whether the logic of our analysis can provide a solid basis for developing a taxonomy of social networks, which in turn would improve our theoretical understanding of the role that network structure plays in the growth of protest movements. Prior research, for instance, demonstrates that cascading behavior in social networks depends greatly on the number of early adopters or first movers, but also (if more subtly) on how they are connected to one another and to the much larger community of potential participants [11]. This is the dimension that we try to capture with our core-periphery analyses. The distributions of influential and susceptible people in social networks, including the ways in which they cluster, is also important to explain propagation behavior [36]. We are still far from understanding how to best characterize these connections to reveal the association of network structure with cascading phenomena in collective action settings [22, 37]. Nevertheless, our findings indicate that we should give more theoretical prominence to the large periphery of people who generate most of the activity and help to increase the reach of messages. This is also important for more general studies of diffusion [38], which have focused on locating central or core individuals and have overlooked the significance of activating the critical periphery.

Supporting Information S1 Text. Supplementary Materials. This document contains additional information about the estimation techniques, empirical analysis, and robustness tests. https://doi.org/10.1371/journal.pone.0143611.s001 (DOCX)

Acknowledgments The authors gratefully acknowledge financial support from the INSPIRE program of the National Science Foundation (Award # SES-1248077), the New York University Research Investment Fund sponsored by Dean Thomas Carew, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation. We also thank members of the DiMeNet research group at Annenberg, members of the NYU Social Media and Political Participation (SMaPP) laboratory, and several anonymous reviewers for insightful comments.

Author Contributions Conceived and designed the experiments: PB SGB. Performed the experiments: PB SGB. Analyzed the data: PB SGB. Contributed reagents/materials/analysis tools: PB NW SGB. Wrote the paper: PB NW RB JJ JN JT SGB.