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 Testosterone-mediated behavior shapes the emergent properties of social networks

2019 ◽  
Author(s):  
Roslyn Dakin ◽  
Ignacio T. Moore ◽  
Brent M. Horton ◽  
Ben J. Vernasco ◽  
T. Brandt Ryder
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Dynamics ◽  
Bird Species ◽  
Evolution Of Cooperation ◽  
Emergent Properties ◽  
List Type ◽  
Form Complex ◽  
Individual Level ◽  
Social Network Dynamics

AbstractSocial networks can vary in their organization and dynamics, with implications for ecological and evolutionary processes. Understanding the mechanisms that drive social network dynamics requires integrating individual-level biology with comparisons across multiple social networks.Testosterone is a key mediator of vertebrate social behavior and can influence how individuals interact with social partners. Although the effects of testosterone on individual behavior are well established, no study has examined whether hormone-mediated behavior can scale up to shape the emergent properties of social networks.We investigated the relationship between testosterone and social network dynamics in the wire-tailed manakin, a lekking bird species in which male-male social interactions form complex social networks. We used an automated proximity system to longitudinally monitor several leks and we quantified the social network structure at each lek. Our analysis examines three emergent properties of the networks: social specialization (the extent to which a network is partitioned into exclusive partnerships), network stability (the overall persistence of partnerships through time), and behavioral assortment (the tendency for like to associate with like). All three properties are expected to promote the evolution of cooperation. As the predictor, we analyzed the collective testosterone of males within each social network.Social networks that were composed of high-testosterone dominant males were less specialized, less stable, and had more negative behavioral assortment, after accounting for other factors. These results support our main hypothesis that individual-level hormone physiology can predict group-level network dynamics. We also observed that larger leks with more interacting individuals had more positive behavioral assortment, suggesting that small groups may constrain the processes of homophily and behavior-matching.Overall, these results provide evidence that hormone-mediated behavior can shape the broader architecture of social groups. Groups with high average testosterone exhibit social network properties that are predicted to impede the evolution of cooperation.

The Politics of Communist Economic Reform

2012 ◽  
Author(s):  
John F. Padgett
Keyword(s):  
Social Network ◽  
Economic Reform ◽  
Network Dynamics ◽  
Mao Zedong ◽  
The Political ◽  
Mikhail Gorbachev ◽  
East European ◽  
Social Network Dynamics ◽  
Nikita Khrushchev ◽  
Political Economic

This chapter compares the political, economic, and social-network dynamics of major economic reform campaigns within communism itself by Joseph Stalin, Nikita Khrushchev, Mao Zedong, Deng Xiaoping, and Mikhail Gorbachev. Over their histories, Soviet, Chinese, and East European communisms frequently had tried to reform themselves economically in a wide variety of ways. The dynamics of economic reform in the climactic 1980s were not as different from what had preceded it as is commonly assumed. It was the outcome more than the process that differed. Hence, the chapter analyzes the transition from communism to “capitalism” not from the outside perspective of capitalism but from the internal perspective of communism.

Large-scale dynamic social data representation for structure feature learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5253-5262
Author(s):  
Xiaoxian Zhang ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Dynamic Change ◽  
Feature Learning ◽  
Data Representation ◽  
Feature Representation ◽  
Small Scale ◽  
Dynamic Feature ◽  
Dynamic Social Networks

The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

scholarly journals Social network dynamics throughout clinical training—Distance matters

2020 ◽  
Author(s):  
Layla Badawy ◽  
Priyanka Oza ◽  
Rohan Shankarghatta ◽  
Elisa Merlini
Keyword(s):  
Social Network ◽  
Clinical Training ◽  
Network Dynamics ◽  
Social Network Dynamics

Dynamics of stakeholder engagement in mega transport infrastructure projects

2019 ◽  
Vol 13 (7) ◽  
pp. 1465-1495 ◽  
Author(s):  
Mehmet Erkul ◽  
Ibrahim Yitmen ◽  
Tahir Celik
Keyword(s):  
Social Network ◽  
Stakeholder Engagement ◽  
Structural Equation ◽  
Project Success ◽  
Network Dynamics ◽  
Transport Infrastructure ◽  
Infrastructure Projects ◽  
Content Type ◽  
Stakeholder Satisfaction ◽  
Social Network Dynamics

Purpose The purpose of this paper is to investigate the practice of stakeholder engagement as a social network dynamics for stakeholder satisfaction and project success in the lifecycle of mega transport infrastructure projects (MTIPs). Design/methodology/approach Hypotheses indicating the positive relationships between stakeholders’ effective attributes, stakeholder engagement as social network dynamics and project success through stakeholders’ satisfaction have been developed. Based on a questionnaire survey and semi-structured interviews, responses have been gathered from the representative groups and organizations on their social network dynamics for their satisfaction and project success. A hypothesized structural equation model has been tested using AMOS statistical software package. Findings The analysis highlighted the engagement of the stakeholders within the strategic intents of the project with the public needs and expectations. The model depicts the processes of building social network models based on the capturing of the project’s data in relation to the stakeholders’ communication and satisfaction across the key issues for success in the lifecycle of MTIP. Practical implications The model is applicable on most MTIP with a diverse stakeholder base and the underlying complexity associated with the community participation and consultation processes. The model will also support wider stakeholder engagement in the planning of MTIP with optimal operationalization and service delivery from a community perspective. Originality/value The research involves an approach for rationalizing the stakeholder engagement policies of the MTIPs by providing an empirically grounded model simultaneously linking various aspects of stakeholder effective attributes, stakeholder engagement and their relationships to stakeholder satisfaction and project success in MTIPs.

scholarly journals Volunteerism: Social Network Dynamics and Education

2014 ◽  
Vol 71 (2) ◽  
pp. 309-319 ◽  
Author(s):  
Kristine J. Ajrouch ◽  
Toni C. Antonucci ◽  
Noah J. Webster
Keyword(s):  
Social Network ◽  
Network Dynamics ◽  
Social Network Dynamics
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