Informal Networked Deliberation: How Mass Deliberative Democracy Really Works

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.

Echo chamber effects in social networks are generally attributed to the prevalence of interactions among like-minded peers. However, recent evidence has emphasized the role of hostile interactions between opposite-minded groups. We investigate the role of polarization, identified with structural balance, in the formation of echo chambers in signed networks. To do so, we generalize the independent cascade model and the linear threshold model to describe information propagation in presence of negative edges. Antagonistic connections do not disrupt the flow of information, but instead, alter the way information is framed. Our results show that echo chambers spontaneously emerge in balanced networks, but also in antibalanced ones for specific parameters. This highlights that structural polarization and echo chambers do not necessarily display a one-to-one correspondence, showing instead a complex and often counterintuitive interplay. The robustness of our results is confirmed with a complex contagion model and through simulations in different network topologies, including real-world datasets.

Following Cass Sunstein's popular treatment of the concept, echo chambers are often defined as environments which exclude contrary opinions through omission. C. Thi Nguyen contests the popular usage and defines echo chambers in terms of in-group trust and out-group distrust. In this paper, I argue for a more comprehensive treatment. While both exclusion by omission and out-group distrust help sustain echo chambers, neither defines the phenomenon. I develop a social network model of echo chambers which focuses on the role of belief-reinforcing echoes. First, I argue that the model allows us to incorporate Nguyen's main point about distrust without construing other commentators as deeply mistaken about the nature of echo chambers. Second, I use the model to develop an account ofcollaborative resistanceand use it to clarify the role echo chambers play in spreading misinformation.

The existence of echo chambers as a phenomenon in social media has been widely debated in recent years. Previous research has attempted to alternatively support or refute the claim that online users strongly favour interacting online with people of similar views. Much of this research has been on social networking sites such as Twitter where users directly connect with each other. A common difficulty faced by researchers of those platforms is how to define, and therefore identify, echo chambers. This research examines a different form of social media, the social news website Reddit, where users interact with each other indirectly as part of topic-based communities. These discrete communities can choose to explicitly define how community members should behave. In this paper we examine two communities : r/The_Donald, which demands echo chamber behaviour, and r/changemyview which actively discourages it. By taking communities which self-categorise their (anti-)echo chamber-ness we are able to determine whether users who choose to participate in an echo chamber or an anti-echo chamber show different levels of participation in other communities. Ultimately, our preliminary research suggests participants of the echo chamber, r/The_Donald are no less active across other communities than the average reddit user. However, we find evidence that participants of the anti-echo chamber r/changemyview are more active than the average platform user.

Since 2016, online social networks (OSNs), especially their “big data” algorithms, have been intensively blamed in popular news discourse for acting as echo chambers. These chambers entrap like-minded voters in closed ideological circles that cause serious damage to democratic processes. This study examines this “echo chamber” argument through the rather divisive case of EU politics among EU citizens. Based on an exploratory secondary analysis of the Eurobarometer 86.2 survey dataset, we investigate whether the reliance on OSNs as a primary EU political news source can lead people to more polarisation in EU-related political beliefs and attitudes than a reliance on traditional media. We found little evidence for this polarisation, lending credence to a rejection of social media’s “echo chamber” effect.

Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and non-academic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process. Furthermore, we derive that no level of positive algorithmic bias in the form of rewiring is capable of preventing fragmentation and its effect on reducing the fragmentation speed is negligible.

BackgroundThe dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users’ cognition as well as decision-making and interaction behaviors.ObjectiveThis study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users’ responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber’s impact on the information characteristics of user interaction content.MethodsWe used Sina Weibo’s application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals’ attitudes when retweeting or commenting on others’ tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users’ interaction content from three dimensions—sentiment expression, information seeking and sharing, and civility—to test the impact of the echo chamber effect.ResultsOur results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users’ information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility.ConclusionsOur findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. We propose a model that introduces the dynamics of radicalization as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users' engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.

The role social media platforms play on the emergence of polarisation is an ongoing debate in the political communication literature. Social media’s filter bubbles and online echo chambers shape people’s opinions by curating the information they have available. However, the extent to which this is the case remains unclear. Social simulation scholars have provided valuable insights into the subject through opinion dynamics models and agent-based modelling approaches. This article proposes a social simulation approach to the topic of opinion dynamics from a political communication perspective to understand how social network configurations and the media environment contribute to the emergence of national identity polarisation. We built an agent-based simulation model of national identity dynamics with a multilayer multiplex network of interacting agents in a hybrid media environment of both, traditional media and social media platforms. We use the Catalan secessionist movement to ground, contextualise and empirically inform parts of our model. We found that the initial social network setup conditions had a large impact on the emergence of polarisation amongst agents. In particular, homophily-based social networks composed of a majority of like-minded individuals produced greater polarisation compared to random networks. This was aggravated in the presence of social media filtering algorithms, selectively exposing agents to supportive information. These results emphasise the importance of both the selective exposure by social media filtering algorithms and one’s social networks (echo chambers) for polarisation to emerge. This interaction reinforces the influence of social media platforms and social networks have on the emergence of polarisation.

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks.

This article examines the role of the new media and social networks as a factor in the development of deliberative democracy. In the late 1990s, it was thought that the communication revolution would be a way to overcome the crisis of representative democracy. With the development of the Internet, the transition from a hierarchical model of communications to a network was supposed to bring changes to the sphere of politics. New media and social networks should guarantee ac- cess of civil society to the political public sphere and help narrow the gap between the elite and the people. Requirements are being formulated to increase the transparency and accountability of state institutions, and to strengthen policy control. It was believed that the Internet will become a place where the principles of deliberative democracy will be embodied. But now we should admit that the new media development causes disappointment, and a times anxiety. Instead of the growth of public discussions, we may encounter “echo chambers”, a victim of the essence of information for effect, which as a result, leads to the radicalization of citizens. Governments and IT-corporations exploit the increased capabilities of social networks and Big Data to control and manipulate information, which jeopardizes the possibility of free discussion. The impact on emotions and simple slogans turned out to be more acceptable in the Internet age than a reflec- tive discussion on social issues. The communication revolution has led to changes in the public sphere, but at the same time, it can lead to the erosion of democracy.

This paper describes a multi-method approach to delineate a “real world” community of practice from a large N dataset derived from the social networking site Twitter. The starting point is previous qualitative research of a virtual community of independent (“indie”) developers who create software for Apple's Macintosh and iPhone platforms. Indie developers have been active on Twitter from an early stage on and they use Twitter to sustain interactions between peers, exchange technical information and for viral “echo chamber” marketing. The publicly available Twitter API is used to mine a network consisting of several million edges, which is sized down to a large network containing roughly 1 million edges through several pruning methods. The fast greedy algorithm is then used to detect subgraphs within this large network. Triangulation with qualitative data proves that the fast greedy algorithm is able to distill meaningful communities from a large, noisy and ill-delineated network. The accuracy of this approach gives rise to the discussion of the value for businesses and market research, since it offers opportunities to identify and monitor target audiences at a finely grained level. However, we should be wary of the serious consequences with regard to privacy and ethics. The proposed multi-method approach allows micro level inferences from a macro dataset of which the individual Twitter user might be completely unaware. The results could have consequences for the anonymity of key persons behind the scenes of social and political movements or any other communities whose members are active on Twitter or other social networks.

People are commonly expected not to waste their vote on parties with small probabilities of being elected. Yet, many end up voting for underdogs. We argue that voters gauge the popular support for their preferred party from their social networks. When social networks function as echo chambers, a feature observed in real-life networks, voters overestimate underdogs’ chances of winning. We conduct voting experiments in which some treatment groups receive signals from a simulated network. We compare the effect of networks with a high degree of homogeneity against random networks. We find that homophilic networks increase the level of support for underdogs, which provides evidence to back up anecdotal claims that echo chambers foster the development of fringe parties.

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings, such as social networks, where users are interacting and influencing one another, may violate conventional assumptions of no interference for credible causal inference. Existing solutions to the network setting include accounting for the fraction or count of treated neighbors in a user's network, yet most current methods do not account for the local network structure beyond simply counting the number of neighbors. Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the treatment assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ "causal network motifs", which are network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. Our approach can account for social network theories, such as structural diversity and echo chambers, and also can help specify network interference conditions that are suitable to each experiment. We test our method on a synthetic network setting and on a real-world experiment on a large-scale network, which highlight how accounting for local structures can better account for different interference patterns in networks.

The over-exposure to information facilitated by the hybrid media system and social networks is a key factor contributing to the increasing polarization of public opinion on major political issues. The European integration project is one of the major political processes affected by information manipulation and disinformation. In this regard, social networks have become powerful tools for nurturing news siloes or “echo chambers,” influencing people’s perceptions of important political issues in a manner that could have a destabilizing effect on democratic processes and institutions. In this context, the role of media discourses and their circulation among networked publics has become particularly relevant, leading audiences to adopt different views supporting or rejecting the European project. This thematic issue features a range of articles considering how the Europeanization process is impacted by discourses circulating in the hybrid media system or threatened by the destructive dynamics of disinformation and polarization.