Stochastic events can explain sustained clustering and polarisation of opinions in social networks

AbstractUnderstanding the processes underlying development and persistence of polarised opinions has been one of the key challenges in social networks for more than two decades. While plausible mechanisms have been suggested, they assume quite specialised interactions between individuals or groups that may only be relevant in particular contexts. We propose that a more broadly relevant explanation might be associated with the influence of external events. An agent-based bounded-confidence model has been used to demonstrate persistent polarisation of opinions within populations exposed to stochastic events (of positive and negative influence) even when all interactions between individuals are noisy and assimilative. Events can have a large impact on the distribution of opinions because their influence acts synchronistically across a large proportion of the population, whereas an individual can only interact with small numbers of other individuals at any particular time.

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THE EFFECT OF IN-GROUP FAVORITISM ON THE COLLECTIVE BEHAVIOR OF INDIVIDUALS' OPINIONS

Advances in Complex Systems ◽

10.1142/s0219525915500022 ◽

2015 ◽

Vol 18 (01n02) ◽

pp. 1550002 ◽

Cited By ~ 8

Author(s):

MEYSAM ALIZADEH ◽

CLAUDIO CIOFFI-REVILLA ◽

ANDREW CROOKS

Keyword(s):

Collective Behavior ◽

Cluster Formation ◽

Opinion Dynamics ◽

Agent Based ◽

Global Consensus ◽

Group Cooperation ◽

Bounded Confidence ◽

Rejection Mechanism ◽

Group Members ◽

Bounded Confidence Model

Empirical findings from social psychology show that sometimes people show favoritism toward in-group members in order to reach a global consensus, even against individuals' own preferences (e.g., altruistically or deontically). Here we integrate ideas and findings on in-group favoritism, opinion dynamics, and radicalization using an agent-based model entitled cooperative bounded confidence (CBC). We investigate the interplay of homophily, rejection, and in-group cooperation drivers on the formation of opinion clusters and the emergence of extremist, radical opinions. Our model is the first to explicitly explore the effect of in-group favoritism on the macro-level, collective behavior of opinions. We compare our model against the two-dimentional bounded confidence model with rejection mechanism, proposed by Huet et al. [Adv. Complex Syst.13(3) (2010) 405–423], and find that the number of opinion clusters and extremists is reduced in our model. Moreover, results show that group influence can never dominate homophilous and rejecting encounters in the process of opinion cluster formation. We conclude by discussing implications of our model for research on collective behavior of opinions emerging from individuals' interaction.

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An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks

10.31235/osf.io/gcxnf ◽

2021 ◽

Author(s):

Unchitta Kan ◽

Michelle Feng ◽

Mason A. Porter

Keyword(s):

Social Networks ◽

Early Time ◽

Opinion Dynamics ◽

Convergence Time ◽

Confidence Bound ◽

Neighboring Node ◽

Popular Approach ◽

Time Dynamics ◽

Bounded Confidence ◽

Bounded Confidence Model

Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to studying the dynamics of opinion spread on networks is by examining bounded-confidence (BC) models, in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other opinions if they lie within some confidence bound of their own opinion. We extend the Deffuant--Weisbuch (DW) model, which is a well-known BC model, by studying opinion dynamics that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinion when they interact with a neighboring node and (2) break a connection with a neighbor based on an opinion tolerance threshold and then form a new connection to a node following the principle of homophily. This opinion tolerance threshold acts as a threshold to determine if the opinions of adjacent nodes are sufficiently different to be viewed as discordant. We find that our adaptive BC model requires a larger confidence bound than the standard DW model for the nodes of a network to achieve a consensus. Interestingly, our model includes regions with `pseudo-consensus' steady states, in which there exist two subclusters within an opinion-consensus group that deviate from each other by a small amount. We conduct extensive numerical simulations of our adaptive BC model and examine the importance of early-time dynamics and nodes with initial moderate opinions for achieving consensus. We also examine the effects of coevolution on the convergence time of the dynamics.

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Measuring the role of two competing groups of informed agents in opinion formation

SIMULATION ◽

10.1177/0037549718800583 ◽

2018 ◽

Vol 95 (8) ◽

pp. 753-766

Author(s):

Kamal S Selim ◽

Ahmed E Okasha ◽

Fatma R Farag

Keyword(s):

Opinion Dynamics ◽

The Other ◽

Social Characteristics ◽

Opinion Formation ◽

Agent Based ◽

Bounded Confidence ◽

Significant Difference ◽

The Impact ◽

Bounded Confidence Model

For politicians, to promote intended messages to different groups of individuals, they could employ strategic individuals called “informed agents.” The aim of this article is to explore and measure the impact of two competing groups of informed agents on opinion dynamics within a society exposed to two extreme opinions. Thus, an agent-based model is developed as an extension to the bounded confidence model by assuming the existence of two groups of informed agents. The impact of these agents with respect to their social characteristics, such as, their size in the society, how tolerant they are, their self-weight and attitudes about others’ opinions is explored. Different assumptions about the initial opinion distributions and their effect are also investigated. Due to the difficulty of observing a real society, social simulation experiments are constructed based on artificial societies.The simulations conducted resulted in some interesting findings. With no dominating group of the two informed agents, the society will be ended up concentrated around a moderate position. On the other hand, with significant difference between the two group sizes, the larger group will polarize the population towards its opinion. However, this conclusion will not apply if the population is skewed towards the other opinion. In such case, the larger group will only succeed to turn some of the society to be more moderate. In a society skewed towards extreme opinion, dominant informed agents adopting the other extreme will not be able to shift the society towards their opinion. Finally, in radical societies informed agents could turn most of the society to be extremists.

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