Resilient Consensus for Expressed and Private Opinions

Shang, Yilun

doi:10.1109/tcyb.2019.2939929

Cited by 50 publications

(24 citation statements)

References 41 publications

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“…borders. 13 Our findings gain traction in light of the advent of social media, which facilitated communication among likeminded people and decentralized information distribution. In the model, this facilitation of public opinion expression can be accounted for by reduced costs, potentially enabling certain opinion groups to speak out (see Figs.…”

Section: A the Spiral Of Silence And Beyondmentioning

confidence: 71%

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Dynamics of opinion expression

2020

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Modeling efforts in opinion dynamics have to a large extent ignored that opinion exchange between individuals can also have an effect on how willing they are to express their opinion publicly. Here, we introduce a model of public opinion expression. Two groups of agents with different opinion on an issue interact with each other, changing the willingness to express their opinion according to whether they perceive themselves as part of the majority or minority opinion. We formulate the model as a multigroup majority game and investigate the Nash equilibria. We also provide a dynamical systems perspective: Using the reinforcement learning algorithm of Q-learning, we reduce the N-agent system in a mean-field approach to two dimensions which represent the two opinion groups. This two-dimensional system is analyzed in a comprehensive bifurcation analysis of its parameters. The model identifies social-structural conditions for public opinion predominance of different groups. Among other findings, we show under which circumstances a minority can dominate public discourse.

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Section: A the Spiral Of Silence And Beyondmentioning

confidence: 71%

“…Models have been developed which distinguish between internal and publicly revealed opinion of agents [7][8][9][10][11][12][13][14][15], often building on the seminal experiments of Asch [16] (see also [17]). As a reaction to peer pressure, agents might publicly display conformity, even though their internal opinion remains unchanged.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of opinion expression

2020

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“…In Theorem 2, the number r is a given parameter, which is assumed to be known for each normal node (some estimates are given in Section II). We here assume that r is known as it is related to the robustness condition of the network; see also [19] for a discrete time system. In practice, it would be possible to obtain an agreed r in a distributed manner via some consensus protocol coupled with the opinion consensus process.…”

Section: Consensus Of Expressed and Private Opinionsmentioning

confidence: 99%

Consensus and Clustering of Expressed and Private Opinions in Dynamical Networks Against Attacks

Shang

2020

IEEE Systems Journal

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A continuous-time opinion dynamics with both expressed and private opinions on a given topic is introduced. An opinion consensus strategy is proposed to achieve resilience consensus against Byzantine attacks in dynamical networks. Necessary and sufficient criteria are established for guaranteeing consensus among normal nodes when the attacks are bounded in each neighborhood of normal nodes. A modification that allows opinion clustering, featuring non-global consensus, is presented. Numerical examples are worked out to illustrate the effectiveness of our theoretical results.

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“…It has been shown, that social networks have a strong influence on political decisions. One example for this was the American presidential election in 2008, where many people perceived a strong influence of Twitter on the elections (Hughes and Palen, 2009;Shang, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…While this makes the search for information on the Internet and in social media easier for users at first, this selection of information can also have negative consequences. In the past, for example, the number of voices that view social networks as something negative has increased due to the fact that political opinions have been deliberately influenced and political results manipulated (Stark et al, 2017;Shang, 2019).…”

Section: Introductionmentioning

confidence: 99%

Opinion Formation on the Internet: The Influence of Personality, Network Structure, and Content on Sharing Messages Online

Burbach

Halbach

Ziefle

et al. 2020

Front. Artif. Intell.

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Today the majority of people uses online social networks not only to stay in contact with friends, but also to find information about relevant topics, or to spread information. While a lot of research has been conducted into opinion formation, only little is known about which factors influence whether a user of online social networks disseminates information or not. To answer this question, we created an agent-based model and simulated message spreading in social networks using a latent-process model. In our model, we varied four different content types, six different network types, and we varied between a model that includes a personality model for its agents and one that did not. We found that the network type has only a weak influence on the distribution of content, whereas the message type has a clear influence on how many users receive a message. Using a personality model helped achieved more realistic outcomes.

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Resilient Consensus for Expressed and Private Opinions

Cited by 50 publications

References 41 publications

Dynamics of opinion expression

Dynamics of opinion expression

Consensus and Clustering of Expressed and Private Opinions in Dynamical Networks Against Attacks

Opinion Formation on the Internet: The Influence of Personality, Network Structure, and Content on Sharing Messages Online

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