Abstract The viral spread of digital misinformation has become so severe that the World Economic Forum considers it among the main threats to human society. This spread have been suggested to be related to the similarly problematized phenomenon of “echo chambers”, but the causal nature of this relationship has proven difficult to disentangle due to the connected nature of social media, whose causality is characterized by complexity, non-linearity and emergence. This paper uses a network simulation model to study a possible relationship between echo chambers and the viral spread of misinformation. It finds an “echo chamber effect”: the presence of an opinion and network polarized cluster of nodes in a network contributes to the diffusion of complex contagions, and there is a synergetic effect between opinion and network polarization on the virality of misinformation. The echo chambers effect likely comes from that they form the initial bandwagon for diffusion. These findings have implication for the study of the media logic of new social media.

Citation: Törnberg P (2018) Echo chambers and viral misinformation: Modeling fake news as complex contagion. PLoS ONE 13(9): e0203958. https://doi.org/10.1371/journal.pone.0203958 Editor: Chris T. Bauch, University of Waterloo, CANADA Received: March 28, 2018; Accepted: August 30, 2018; Published: September 20, 2018 Copyright: © 2018 Petter Törnberg. 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: Data for empirical simulations are available from: Törnberg, Petter (2018): Empirical simulation graphs. figshare. Fileset. https://doi.org/10.6084/m9.figshare.7011635.v1. Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme project ODYCCEUS (grant agreement n° 732942). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The author has declared that no competing interests exist.

Introduction The way we become informed, debate, and form our opinions have changed profoundly with the advent of online media [1–5]. Today’s media is less organized through centralized decision-making, and more through complex cascade processes, where news items spread like wild-fire over social networks through direct connections between news producers and consumers—categories between which it is becoming increasingly hard to distinguish. Disintermediation has changed the way we as a society form narratives about our common world with promises of more egalitarian ways of meeting and discussing. But despite early optimism about this ostensibly decentralized and democratic meeting-place, the online world seems less and less like a common “table” that “gathers us together” [6](p.52) to freely discuss and identify societal problems in a new type of “public sphere” [7]. Instead, it seems to bring forth the worst of human instincts: we cluster together into tribes that comfort us with reaffirmation and protect us from disagreement; “echo chambers” that reinforce existing perspective and foster confirmation biases [8–11]. Technology that was purported to help weave tighter the bonds between humans instead seem to has led to a fraying of our social fabric, as we are torn into social groups with separate world-views. Simultaneous with this development is an on-going reduction in the quality and credibility of available information: while the Internet was initially hailed as an unprecedented source of easily accessible knowledge, it increasingly appears to instead have brought an information climate characterized by biased narratives, “fake news”, conspiracy theories, mistrust and paranoia. The digital world seems to provide fertile soil for the growth of misinformation, as studies show that false news diffuse faster, farther and deeper than true news in social networks [12]. This changing online climate is relevant not merely within the realm of social media, but may influence opinions and behavior also in other areas of human life [13–18]. This rapid cultural shift has quickly become an onerous threat, with viral misinformation now being seen as a major risk to human society [19]. There are certain signs that point to a link between these two phenomena—echo chambers and the spread of misinformation—since homogeneous clusters of users with a preference for self-confirmation seem to provide capable green-houses for the seedling of rumors and misinformation. A polarized digital space where users tend to promote their favorite narratives, form polarized groups and resist information that does not conform to their beliefs may be the fertilizer that makes the Internet so fertile for the growth of misinformation [20]. Armies of supporters for products, brands or presidents are quickly rallied, spreading their views in an environment where trust in traditional knowledge authorities is increasingly frail [21–25]. But the potential link between echo chambers and misinformation has proven difficult to evaluate due to the complexity underlying social media dynamics. The transition in information dynamics that digital media has brought can be usefully seen through the notion of media logic, i.e. the “particular institutionally structured features of a medium […] that will tend to structure particular perceptual and cognitive biases” [26] (p.63-64). This notion thus points to structural factors of a medium, which in the case of social media implies an entwined causal of distributed actors [27, 28]. Contemporary media logic has taken a distinctly postmodern turn with digital technology, as networks and identities now carry more weight than the truths of traditional authorities [29]. It has gone from the structured top-down logic of the machine—with centralized institutions and trained journalists making decisions from editorial chairs—to the decentralized and dynamic complexity of what resembles a swarm or herd—millions of dopamine-driven smartphone-users swiping through click-bait news on porcelain thrones [30, 31]. Such swarm logic often departs from the terrain accessible by human intuition, and travels into the realm of emergence and complexity where neither un-aided cognition or traditional scientific methods are able to disentangle the complex chains of causality [32]. This paper suggests an emergent causal link that could serve to connect the spread of misinformation and online echo chambers. The nature of this link is perhaps most clear through the lens of a metaphor: if we think of the viral spread of misinformation in a social network as akin to a wildfire [19], an echo chamber has the same effect as a dry pile of tinder in the forest; it provides the fuel for an initial small flame, that can spread to larger sticks, branches, trees, to finally engulf the forest (it should be noted that this metaphor has a specific and technical meaning: forest fires received significant early interest within complexity theory, see e.g. [33, 34], the metaphor also recalls Schelling’s [35] threshold model). Just as how segregation has been, famously, shown to involve emergent feedback processes [36], it is in this paper suggested that an emergent network effect results in that misinformation and rumors spread easier in networks where there is a presence of an echo chamber. The model implies that news originating in a segregated cluster of users tends to spread further than in a network without clusters. It furthermore shows that the mere coming together of users with similar views may be enough to increase the prevalence of misinformation, as virality increases with network homophily. It finally suggests that the combination between these two factors, arguably what defines an echo chambers, is synergetically more prone to induce global cascades than either taken in isolation. This could mean that echo chambers make fake news more viral, since information that resonates with biased clusters of users has a higher likelihood to spread through a network. This paper thus uses a simple network model to study whether the network structure of echo chambers can in itself have an effect for the spread of information. Online echo chambers are modelled as set of users characterized by opinion and network polarization, i.e. they are clusters of like-minded users, that are (i) more separated from the rest of the network, and (ii) have a lower threshold for being convinced by a given narrative. This approach may provide direction for further study in online media dynamics in general, and the spread of misinformation in particular.

1 Modeling spread of misinformation on social media The model presented here aims to be simple as this both increases generality and makes the results of the model easier to interpret and bring into a sociological narrative [37, 38]. Observing that there is a tendency for echo chambers to form around conspiracies and mistrust of traditional authorities, the question that this model asks is: if a diffusant is associated to an echo chamber, will this affect its virality in a social network? The model thus does not deal with the content of the diffusant, meaning that its dynamics applies to any type of information spreading from an echo chamber. The model describes the spread of a news item on social media as a complex diffusion in a social network. This means that we understand a social network as a number of connected nodes—representing users—that each are assigned a threshold that describes how difficult they are to convince about a given narrative. If a large-enough fraction of their neighbors spread a given narrative, the node will be convinced, and will continue the spread of the diffusant. Hence, the threshold can be seen as representing to what extent a given news item or idea resonates with a certain user. In this network, we understand an echo-chamber as a set of users characterized by two properties: opinion and network polarization. Opinion polarization means that they, in relation to a given question, are more inclined to share similar views. Network polarization means that they are more densely connected with each other than with the outside network. In other words, an echo chamber is a tightly connected set of nodes more inclined to share a common view on a given narrative. For the sake of simplicity, this model focuses on the existence of a single echo-chamber in a larger network. This type of clusters in networks have generally been understood to constitute impediments to the spread of diffusion, as they reduce the number of the weak ties that have in turn been found to be central enablers of diffusion (see e.g. [39–43]). Modeling results have shown that things like information, diseases, and so on, have a harder time of spreading into and out of clusters, since these are less connected to the surrounding network, which means that they will also reduce the probability for network-wide cascades. However, these results specifically concern diffusion of entities that require only single contact to spread. This makes sense in many cases, for example disease, but it has been argued to be a problematic assumption when studying behavioral phenomena (see e.g. [44, 45]). In these cases, complex contagion, is often a more plausible view: that multiple contact increases the likelihood for a contagion to spread. In these cases, weak ties have been shown to be less relevant, or even have a negative effect on diffusion, and there are situations where clusters may in fact be beneficial for the diffusion. But not always: even for complex contagions, clusters may have negative impact on diffusion. For example, Easley and Kleinberg [46] show using mathematical reasoning that given that cascades start outside of the cluster, that they are seen as successful only if every node in the network is activated, and that every node has the same number of neighbors, clusters will have negative effects for cascades. We argue that online news and rumors are better characterized as a complex rather than simple contagions, since a news item or idea becomes more convincing with the number of people that argue for it [44]. For example, a single user in your network claiming that vaccines cause autism or that Russia is an important ally to the US may easily be disregarded as a nut-job. But if a large fraction of your social network argues for the same thing, it not only makes the argument seem more authoritative, but you may also feel the need to conform to your social group [47]. This majority effect becomes especially clear if we think of political discussions and online interaction not as much as a spread of information, but as having elements of group identity and belonging, an understanding that is becoming increasingly prevalent throughout the social sciences [48, 49]. Echo chambers are generally understood as also playing a role in the formation of interpretive frames and collective identities, rather than simply constituting a hub for information diffusion [50]. The second aspect of the echo chambers, opinion polarization, implying that their thresholds are lower than that of the surrounding network, is related to what in network terms is referred to as homophily: the probability that neighboring nodes have similar thresholds for activation. Moderate levels of homophily is generally understood to increase the network’s capacity for diffusion [51–54]. This can also be connected to the suggestion in critical mass theory that an initial group can solve the large group problem by creating a “bandwagon effect” [55–58]. This model differs from studies looking only at homophily (e.g. [51]) as it combines homophily with network clustering, to thereby capture the essence of a social network echo chamber. The model here presented primarily differs from previous approaches in the literature by: (i) looking at the dynamics of complex rather than simple contagions, which may have important effects for the results; (ii) loosening the assumptions that every node in the network needs to be activated for a cascade to be understood as complete, and that each node has the same number of neighbors; (iii) exploring the systemic interaction between network and opinion polarization, i.e. between lower activation thresholds and level of clustering, as these are both characteristic of echo chambers. In other aspects, the model generally follows the established design of existing network cascade models that focus on how different factors affect the probability for activation to diffuse in a network [44, 51, 59]. 1.1 Model design We refer to the probability that activation will spread to a majority of the network’s nodes, given a certain distribution of network structures, as the “virality” of the diffusion. Since the connections between the nodes represent the existence of mutual social contact, the model uses undirected ties. The distribution of these ties follows the so-called Erdős-Rényi structure, where each tie is assigned with uniform probability, which is commonly used in this type of models [43]. The distribution of ties in an Erdős-Rényi network follows a Poisson distribution. To form the clusters, which here represent free social spaces, a fraction of the ties that span from inside the cluster to outside the cluster are removed and replaced with ties inside the clusters, resulting in more internal cluster ties than external. The fraction between internal and external ties represents the previously introduced notion of network polarization. For example, with a polarization value of 0.85, 85% of the cluster’s external connections are relocated, while a polarization value of 0 represents a standard Erdős-Rényi structure. This definition of network polarization captures the idea that echo chambers are clusters of more densely connected users. A cascade is initiated by the activation of a randomly selected node and its neighbors. This could for example be in the form of an actor posting a blog post or tweet with misinformation. In each following time step, nodes that have more than a certain fraction—called their threshold—of their neighborhood activated themselves become activated—what has been referred to as a complex contested contagion [44]. This continues until a steady state is reached. If at this point, a majority of the network has become activated, the cascade is classified as successful. Since the focus is on echo chambers, we assume that the cascade is initiated inside the cluster. Furthermore, we implement the previously introduced notion of opinion polarization as a parameter lowering the activation threshold for the cluster nodes relative to the average threshold, meaning that the nodes inside the cluster are more easily activated. This of course trivially has an effect of increasing the virality, and we hence compare to a control case, in which the reduction of threshold is assigned to random nodes in the entire network. The primary question that we aim to investigate with this model is whether echo chambers have any effect on the virality of cascades, and to furthermore look at the interaction between opinion and network polarization in terms of virality. 1.2 Model implementation We define: P n as the network polarization parameter, P o as opinion polarization parameter, k as the average degree of the nodes, θ is the activation threshold, c is the fraction of nodes belonging to the echo chamber. E is the total number of edges, N are number of nodes. For each combination of steps of parameter values, the model is run 1,000 times, with a new network structure generated for each run to compensate for network structure heterogeneity and allow for higher generality and robustness. Parameters P o , P n , and θ are systematically varied over an interval, to find how these parameters affect the model (additionally, other parameters where tested for sensitivity in separate runs.) With 100 steps in P o and 200 steps in θ and 12 steps in P n , the model is run a total of 2.4 * 108 times, which is arguably a reasonably thorough exploration of parameter space, thereby allowing an in-depth assessment of the model dynamics. Furthermore, these runs were evaluated for a number of network sizes, to further validate their robustness and relevance. In each such run, an Erdős-Rényi network structure is constructed in the following way. N nodes are created, cN of which are specified as belonging to the echo chamber. The specified mean degree k is divided by two (since the network is undirected and any edge has two sides) and multiplied with the number of node edges, i.e. . From this set, P n kE edges are selected where exactly one of the connected nodes belong to the cluster. These are removed, and replaced by edges where both nodes belong to the cluster. Following this, nodes outside the cluster are set to have activation threshold θ, and nodes in the cluster are set to have threshold θ − P o . A random node in the cluster is then selected, and set as activated, to act as activation seed. All nodes connected to this node are also set as activated. After this, the following is repeated until no more changes occur: for each node, if the fraction of activated connected nodes is larger than the node threshold, the node is set as active in the next step. If the fraction of active nodes at the end of such a run is larger than 0.5, the cascade is considered global (this definition prevents the mere activation of the echo chamber to affect the measured level of virality, and follows previous research, see e.g. [45]). This procedure is repeated 1,000 times with new random network structure for each parameter step. Virality, V(θ, P n , P o ), is defined as the fraction of times in these runs that a majority of the nodes are activated.

3 Conclusion The model presented in this paper indicates that there may be general structural effects of echo chambers that contribute to the viral spread of misinformation. The model adds to the existing literature on network diffusion (see e.g. [44, 51, 59]) by showing that a combination between clustering and homophily has disproportionate effects on the capacity for complex diffusion, as it contributes to initiating a band-wagon effect. This stands in sharp contrast to both intuition and existing research on the spread of simple contagions, where clusters have instead been found to reduce virality [43]. This link between the presence of a polarized cluster and the spread of a complex contagion indicates a possible connection between echo chambers and the spread of misinformation. In other words, the result of this model suggests that echo chambers may be linked to the spread of misinformation through an emergent network effect. When misinformation resonates with the views of an echo chamber, the chamber can function as an initial platform from which the diffusion can occur globally through weak ties. The model furthermore suggested that the combination between opinion and network polarization, quintessential of echo chambers, results in a synergetic effect that increases the virality of narratives that resonate with the echo chamber. This means that the simple clustering together of users with a deviant world-view is enough to affect the virality of information items that resonate with their perspective. More broadly, this suggests that not only algorithmic “filter bubbles” [63] affect what news and perspectives we are exposed to online, but that the mere fact of social media permitting a dynamic of social clustering can change the dynamics of online virality. The possibility of self-segregation [64] can therefore affect not only what the segregated users see, but also what perspectives non-segregated users are exposed to. This can occur as subtle and complex network dynamics of the interaction structure of social media can play into the diffusion dynamics, in ways that are not necessarily even understood by the developers of the media platforms. While simulations offer an unparalleled possibility to study specific causal mechanisms, it should however be noted that the result of a computational model is not enough to draw definitive conclusions about real world dynamics, since the observed mechanism may be overshadowed by other factors. The simplicity of the model described in this paper, while having the benefit of providing generality and permitting a thorough exploration of parameter space, also means that there are many factors that are not taken into account in the model. Additional research is therefore necessary to investigate whether the described mechanism is in fact in play in real world social media. By engaging in this exploration, this paper constitutes a step toward the study of the “media logic” [27] of social media, which, as has been noted by multiple scholars, differ in important ways from the more institutionalized logic of traditional media [26, 28]. Since social media logic is the result of complex cascades and emergent effects, they require the use of computational models to study and discern their often-surprising dynamics [32]. Although clearly of central relevance for the understanding of the contemporary media landscape, these questions remain under-researched and poorly understood among both scholars and the lay public. A fascinating self-similarity can be noted here, as the type of unintuitive complex dynamics characteristic of social media has been observed to be precisely what misinformation and conspiracy theories tend to seize upon: scholars have noted that conspiracy theorists often construct narratives that attempt to “restore a sense of agency, causality and responsibility” [65] (p.1). This seems related to a type of modernist thinking of society—what Andersson and Törnberg [38] refer to as the “complicated” system perspective—which would follow from approaching social media through the lens of traditional media. In this way, conspiracism is not only a symptom of the breakdown of knowledge authority, but of the breakdown of intuitive causality itself, as society becomes an increasingly complex system [66, 67]. In line with this, Gambetta and Hertog [68] show that engineers are over-represented among extremists, and argue that this comes from seeking the order and hierarchy of machines also in the social world. Conspiracy theories hence tend to describe a society under the conscious control of a global elite, seeking order and agency in a world which is becoming more and more like a leaf in the wind of social processes that are emergent and intangible, contingent and plural. This is part of the larger question of how social media has affected our social and political processes. While online platforms have been thought of as equalizers and social levelers, which through disintermediation could provide equal opportunities for all, they in practice seem rather to result in the perpetuation and reinforcement of processes of stratification and inequality, as well as in self-segregation and tribalization [69]. The disappearance of media intermediation seems not to have, as was believed, fostered a space for direct meetings in a sort of online Habermasian public sphere, but rather to have implied that the “world between them has lost its power to gather them together, to relate and to separate them” [6] (p.52). As we find ourselves in this new condition of “worldlessness”, we go in search of new communities, new worlds in which we may again feel a sense of togetherness. These communities become places “where we see all people suddenly behave as though they were members of one family, each multiplying and prolonging the perspective of his neighbor. […] The end of the common world has come when it is seen only under one aspect and is permitted to present itself in only one perspective.” [6] (p.57-58). The question of how to resolve the issues of misinformation and polarization, now considered among the main threats to human society, hence concerns how to design social media technology as to again constitute the foundation for a common world; how to shape our digital spaces as to help weave, rather than fray, our social fabric. For this, studies of the emergent externalities of the interaction structures of social networks will likely need to play a central role.

Acknowledgments This project has received funding from the European Union’s Horizon 2020 research and innovation programme project ODYCCEUS (grant agreement n° 732942).