scholarly journals Cascades across networks are sufficient for the formation of echo chambers: An agent-based model

2020 ◽  
Author(s):  
Jan-Philipp Fränken ◽  
Toby Pilditch
Keyword(s):  
Climate Change ◽  
Social Networks ◽  
Social Network ◽  
Individual Differences ◽  
Peer To Peer ◽  
Psychological Explanation ◽  
Agent Based ◽  
Echo Chambers ◽  
Core Components ◽  
Echo Chamber

Investigating how echo chambers emerge in social networks is increasingly crucial, given their role in facilitating the retention of misinformation, inducing intolerance towards opposing views, and misleading public and political discourse (e.g., disbelief in climate change). Previously, the emergence of echo chambers has been attributed to psychological biases and inter-individual differences, requiring repeated interactions among network-users. In the present work we show that two core components of social networks—users self-select their networks, and information is shared laterally (i.e. peer-to-peer)—are causally sufficient to produce echo chambers. Crucially, we show that this requires neither special psychological explanation (e.g., bias or individual differences), nor repeated interactions—though these may be exacerbating factors. In fact, this effect is made increasingly worse the more generations of peer-to-peer transmissions it takes for information to permeate a network. This raises important questions for social network architects, if truly opposed to the increasing prevalence of deleterious societal trends that stem from echo chamber formation.

Ideology, communication and polarization

2021 ◽  
Vol 376 (1822) ◽  
pp. 20200133
Author(s):  
Yoshihisa Kashima ◽  
Andrew Perfors ◽  
Vanessa Ferdinand ◽  
Elle Pattenden
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Political Discourse ◽  
Network Dynamics ◽  
Political Polarization ◽  
Connectionist Model ◽  
Theme Issue ◽  
Social Network Dynamics ◽  
Dynamic Social Networks ◽  
Echo Chamber

Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular, we explicitly model ideologically filtered interpretation of social information, ideological commitment to initial opinion, and communication on dynamically evolving social networks, and examine how these factors combine to generate ideologically divergent and polarized political discourse. The results show that ideological interpretation and commitment tend towards polarized discourse. Nonetheless, communication and social network dynamics accelerate and amplify polarization. Furthermore, when agents sever social ties with those that disagree with them (i.e. structure their social networks by homophily), even non-ideological agents may form an echo chamber and form a cluster of opinions that resemble an ideological group. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.

scholarly journals Individual Differences in Learning Social and Non-Social Network Structures

2017 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Individual Differences ◽  
Social Contexts ◽  
Higher Order ◽  
Social Orientation ◽  
Order Structure ◽  
Network Learning ◽  
Social Traits ◽  
The Impact

Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network structure. We examined individual differences in the ability to learn network topology for which stimuli were social versus non-social. Although participants were able to learn both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. Importantly, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social networks but not the learning of non-social networks. Taken together, our results suggest that the process of learning higher-order structure in social networks is independent from the process of learning higher-order structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes. Implications for how people learn and adapt to new social contexts that require integration into a new social network are discussed.

Cooperation Among Members of Online Communities

2011 ◽  
Vol 1 (3) ◽  
pp. 1-14
Author(s):  
M. L. Merani ◽  
M. Capetta ◽  
D. Saladino
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Video Streaming ◽  
Online Social Networks ◽  
Peer To Peer ◽  
Critical Conditions ◽  
The Internet ◽  
Performance Improvements ◽  
P2p Video Streaming ◽  
P2p Overlay

Today some of the most popular and successful applications over the Internet are based on Peer-to-Peer (P2P) solutions. Online Social Networks (OSN) represent a stunning phenomenon too, involving communities of unprecedented size, whose members organize their relationships on the basis of social or professional friendship. This work deals with a P2P video streaming platform and focuses on the performance improvements that can be granted to those P2P nodes that are also members of a social network. The underpinning idea is that OSN friends (and friends of friends) might be more willing to help their mates than complete strangers in fetching the desired content within the P2P overlay. Hence, an approach is devised to guarantee that P2P users belonging to an OSN are guaranteed a better service when critical conditions build up, i.e., when bandwidth availability is scarce. Different help strategies are proposed, and their improvements are numerically assessed, showing that the help of direct friends, two-hops away friends and, in the limit, of the entire OSN community brings in considerable advantages. The obtained results demonstrate that the amount of delivered video increases and the delay notably decreases, for those privileged peers that leverage their OSN membership within the P2P overlay.

scholarly journals Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study (Preprint)

2018 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Bojan Simoski ◽  
Eric Fernandes de Mello Araújo ◽  
Kirsten E Bevelander ◽  
William J Burk ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Selection Criteria ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Network Characteristics ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention

BACKGROUND Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. CONCLUSIONS Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

scholarly journals Creativity in temporal social networks: how divergent thinking is impacted by one’s choice of peers

2020 ◽  
Vol 17 (171) ◽  
pp. 20200667
Author(s):  
Raiyan Abdul Baten ◽  
Daryl Bagley ◽  
Ashely Tenesaca ◽  
Famous Clark ◽  
James P. Bagrow ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Divergent Thinking ◽  
Self Organization ◽  
Agent Based Simulation ◽  
Web Based ◽  
Agent Based ◽  
High Performing ◽  
Simulation Parameters

Creativity is viewed as one of the most important skills in the context of future-of-work. In this paper, we explore how the dynamic (self-organizing) nature of social networks impacts the fostering of creative ideas. We run six trials ( N = 288) of a web-based experiment involving divergent ideation tasks. We find that network connections gradually adapt to individual creative performances, as the participants predominantly seek to follow high-performing peers for creative inspirations. We unearth both opportunities and bottlenecks afforded by such self-organization. While exposure to high-performing peers is associated with better creative performances of the followers, we see a counter-effect that choosing to follow the same peers introduces semantic similarities in the followers’ ideas. We formulate an agent-based simulation model to capture these intuitions in a tractable manner, and experiment with corner cases of various simulation parameters to assess the generality of the findings. Our findings may help design large-scale interventions to improve the creative aptitude of people interacting in a social network.

scholarly journals Echo Chambers

2020 ◽  
Author(s):  
J. Anthony Cookson ◽  
Joseph E. Engelberg ◽  
William Mullins
Keyword(s):  
Social Network ◽  
Trading Volume ◽  
Selective Exposure ◽  
Earnings Announcement ◽  
Echo Chambers ◽  
Echo Chamber

We find evidence of selective exposure to confirmatory information among 300,000 users on the investor social network StockTwits. Self-described bulls are 5 times more likely to follow a user with a bullish view of the same stock than self-described bears. This tendency is strong even among professional investors and is more pronounced on earnings announcement days. Placing oneself in an information “echo chamber” generates significant differences in the newsfeeds of bulls and bears: over a 50-day period, a bull will see 70 more bullish messages and 15 fewer bearish messages than a bear over the same period. Selective exposure creates “information silos” in which the diversity of received signals is high across users' newsfeeds but is low within users' newsfeeds. Finally, we show that this siloing of information is positively related to trading volume.

Cooperation Among Members of Online Communities

2013 ◽  
pp. 170-183
Author(s):  
M. L. Merani ◽  
M. Capetta ◽  
D. Saladino
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Video Streaming ◽  
Online Social Networks ◽  
Peer To Peer ◽  
Critical Conditions ◽  
The Internet ◽  
Performance Improvements ◽  
P2p Video Streaming ◽  
P2p Overlay

Today some of the most popular and successful applications over the Internet are based on Peer-to-Peer (P2P) solutions. Online Social Networks (OSN) represent a stunning phenomenon too, involving communities of unprecedented size, whose members organize their relationships on the basis of social or professional friendship. This work deals with a P2P video streaming platform and focuses on the performance improvements that can be granted to those P2P nodes that are also members of a social network. The underpinning idea is that OSN friends (and friends of friends) might be more willing to help their mates than complete strangers in fetching the desired content within the P2P overlay. Hence, an approach is devised to guarantee that P2P users belonging to an OSN are guaranteed a better service when critical conditions build up, i.e., when bandwidth availability is scarce. Different help strategies are proposed, and their improvements are numerically assessed, showing that the help of direct friends, two-hops away friends and, in the limit, of the entire OSN community brings in considerable advantages. The obtained results demonstrate that the amount of delivered video increases and the delay notably decreases, for those privileged peers that leverage their OSN membership within the P2P overlay.

scholarly journals Profile-Based Ad Hoc Social Networking Using Wi-Fi Direct on the Top of Android

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Nagender Aneja ◽  
Sapna Gambhir
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Social Services ◽  
Ad Hoc ◽  
Peer Group ◽  
Peer To Peer ◽  
Profile Matching ◽  
Nearby Region ◽  
Common Interests

Ad hoc social networks have become popular to support novel applications related to location-based mobile services that are of great importance to users and businesses. Unlike traditional social services using a centralized server to fetch location, ad hoc social network services support infrastructure-less real-time social networking. It allows users to collaborate and share views anytime anywhere. However, current ad hoc social network applications either are not available without rooting the mobile phones or do not filter the nearby users based on common interests without a centralized server. This paper presents an architecture and implementation of social networks on commercially available mobile devices that allow broadcasting name and a limited number of keywords representing users’ interests without any connection in a nearby region to facilitate matching of interests. The broadcasting region creates a digital aura and is limited by the Wi-Fi region that is around 200 meters. The application connects users to form a group based on their profile or interests using the peer-to-peer communication mode without using any centralized networking or profile-matching infrastructure. The peer-to-peer group can be used for private communication when the network is not available.

scholarly journals Distributed recommender system using multi-agent for social networks

2021 ◽  
Author(s):  
Lubaid Ahmed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
Network Simulator ◽  
Clustering Methods ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Data Mining Algorithms ◽  
Multi Agent

Social networks have become significant tools due to the vast and useful information existing in them. The social platforms also act as the storage of entered choices of millions of users for various applications such as political surveys, research studies, marketing product preferences and many more. Social network recommender systems exploit this information and direct users in selecting their choices. It is clear that recommender systems should be efficient enough to be able to process the huge magnitude of data that has been generated in recent years by social network users. This research proposes a foundation of an efficient and scalable recommender system to be able to process large amount of data (i.e. Big data) in a short amount of time. The main goal is providing scalability and efficiency of the recommender system. The simulation of the prototype of such a distributed recommender system by using multi-agent based technologies shows promising results. These prototypes provide recommendations to users about other users with the similar interests in online and distributed manner as real recommender systems. The agents can simulate users or can be used as the containers of algorithms for comparing the similarity between users by different approaches, such as cosine similarity and clustering methods for testing and examining real scenarios. To be able to test these prototypes in agent-based simulation environment an agent-based framework is developed. This framework has three modules named social network crawler, social network simulator and employed prototype of the distributed recommender system that use different text and data mining algorithms. Finally, newly developed performance metric (called Scalability Factor) is introduced that shows the minimum number of servers needed to be able to run the agent systems in parallel. This thesis shows using a distributed and parallel model for recommender systems is the key to increase the speed of recommendation convergence and as a result to provide scalability. Multi-agent based simulation results, coupled with numerical analysis affirm that the proposed solution provides scalability and efficiency for recommender systems.

scholarly journals Distributed recommender system using multi-agent for social networks

2021 ◽  
Author(s):  
Lubaid Ahmed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
Network Simulator ◽  
Clustering Methods ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Data Mining Algorithms ◽  
Multi Agent

Social networks have become significant tools due to the vast and useful information existing in them. The social platforms also act as the storage of entered choices of millions of users for various applications such as political surveys, research studies, marketing product preferences and many more. Social network recommender systems exploit this information and direct users in selecting their choices. It is clear that recommender systems should be efficient enough to be able to process the huge magnitude of data that has been generated in recent years by social network users. This research proposes a foundation of an efficient and scalable recommender system to be able to process large amount of data (i.e. Big data) in a short amount of time. The main goal is providing scalability and efficiency of the recommender system. The simulation of the prototype of such a distributed recommender system by using multi-agent based technologies shows promising results. These prototypes provide recommendations to users about other users with the similar interests in online and distributed manner as real recommender systems. The agents can simulate users or can be used as the containers of algorithms for comparing the similarity between users by different approaches, such as cosine similarity and clustering methods for testing and examining real scenarios. To be able to test these prototypes in agent-based simulation environment an agent-based framework is developed. This framework has three modules named social network crawler, social network simulator and employed prototype of the distributed recommender system that use different text and data mining algorithms. Finally, newly developed performance metric (called Scalability Factor) is introduced that shows the minimum number of servers needed to be able to run the agent systems in parallel. This thesis shows using a distributed and parallel model for recommender systems is the key to increase the speed of recommendation convergence and as a result to provide scalability. Multi-agent based simulation results, coupled with numerical analysis affirm that the proposed solution provides scalability and efficiency for recommender systems.

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