Link recommendation algorithms and dynamics of polarization in online social networks

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

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Multiple social network influences can generate unexpected environmental outcomes

Scientific Reports ◽

10.1038/s41598-021-89143-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

J. Yletyinen ◽

G. L. W. Perry ◽

P. Stahlmann-Brown ◽

R. Pech ◽

J. M. Tylianakis

Keyword(s):

Social Networks ◽

Complex Adaptive Systems ◽

Adaptive Systems ◽

Social Influences ◽

Private Land ◽

Environmental Interventions ◽

Social Ecological Systems ◽

Private Land Conservation ◽

Social Ecological ◽

Complex Adaptive

AbstractUnderstanding the function of social networks can make a critical contribution to achieving desirable environmental outcomes. Social-ecological systems are complex, adaptive systems in which environmental decision makers adapt to a changing social and ecological context. However, it remains unclear how multiple social influences interact with environmental feedbacks to generate environmental outcomes. Based on national-scale survey data and a social-ecological agent-based model in the context of voluntary private land conservation, our results suggest that social influences can operate synergistically or antagonistically, thereby enabling behaviors to spread by two or more mechanisms that amplify each other’s effects. Furthermore, information through social networks may indirectly affect and respond to isolated individuals through environmental change. The interplay of social influences can, therefore, explain the success or failure of conservation outcomes emerging from collective behavior. To understand the capacity of social influence to generate environmental outcomes, social networks must not be seen as ‘closed systems’; rather, the outcomes of environmental interventions depend on feedbacks between the environment and different components of the social system.

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Collective computation and the emergence of hunter-gatherer small-worlds

10.1101/2021.08.16.456574 ◽

2021 ◽

Author(s):

Marcus J. Hamilton

Keyword(s):

Social Networks ◽

Complex Adaptive Systems ◽

Adaptive Systems ◽

Multiple Scales ◽

Energy Budgets ◽

Small Worlds ◽

Local Interactions ◽

Form Complex ◽

Macroscopic Structure ◽

Complex Adaptive

AbstractTwo defining features of human sociality are large brains with neurally-dense cerebral cortices and the propensity to form complex social networks with non-kin. Large brains and the social networks in which they are embedded facilitate flows of fitness-enhancing information at multiple scales, but are also energetically expensive. In this paper, we consider how flows of energy and information interact to shape the macroscopic features of hunter-gatherer socioecology. Collective computation is the processing of information by complex adaptive systems to generate inferences in order to solve adaptive problems. In hunter-gatherer societies the adaptive problem is how to maximize fitness by optimizing information processing given the energy constraints of complex environments. The solution is the emergent macroscopic structure of the socioecology. Here we show how computation is extended across social networks to form the decentralized knowledge systems characteristic of hunter-gatherer societies. Data show that hunter-gatherer bands of co-residing families constitute computationally powerful networks that are embedded within hierarchically modular social networks that form complex metapopulations bound by fission-fusion dynamics at multiple scales facilitating the flow of information far beyond local interactions. These dynamics lead to the emergence of hunter-gatherer small-worlds where highly clustered local interactions are embedded within much larger, but sparsely connected metapopulations. Hunter-gatherers optimize local energy budgets in small groups but maintain interactions with much larger social networks while avoiding many of the ecological costs.

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