20 research outputs found
Opinion Behavior Analysis in Social Networks Under the Influence of Coopetitive Media
Both interpersonal communication and media contact are important information sources and play a significant role in shaping public opinions of large populations. In this paper, we investigate how the opinion-forming process evolves over social networks under the media influence. In addition to being affected by the opinions of their connected peers, the media cooperate and/or compete mutually with each other. Networks with mixed cooperative and competitive interactions are said to be coopetitive . In this endeavor, a novel mathematical model of opinion dynamics is introduced, which captures the information diffusion process under consideration, makes use of the community-based network structure, and takes into account personalized biases among individuals in social networks. By employing port-Hamiltonian system theory to analyze the modeled opinion dynamics, we predict how public opinions evolve in the long run through social entities and find applications in political strategy science. A key technical observation is that as a result of the port-Hamiltonian formulation, the mathematical passivity property of individuals’ self-dynamics facilitates the convergence analysis of opinion evolution. We explain how to steer public opinions towards consensus, polarity, or neutrality, and investigate how an autocratic media coalition might emerge regardless of public views. We also assess the role of interpersonal communication and media exposure, which in itself is an essential topic in mathematical sociology
Squared-down passivity based H∞ almost synchronization of homogeneous continuous-time multi-agent systems with partial-state coupling via static protocol
This paper studies H∞ almost state and output synchronizations of homogeneous multi-agent systems (MAS) with partial-state coupling with general linear agents affected by external disturbances. We will characterize when static linear protocols can be designed for state and output synchronization for a MAS such that the impact of disturbances on the network disagreement dynamics, expressed in terms of the H∞ norms of the corresponding closed-loop transfer function, is reduced to any arbitrarily small value. Meanwhile, the static protocol only needs rough information on the network graph, that is a lower bound for the real part and an upper bound for the modulus of the non-zero eigenvalues of the Laplacian matrix associated with the network graph. Our study focuses on three classes of agents which are squared-down passive, squared-down passifiable via output feedback and squared-down minimum-phase with relative degree 1
Opinion Dynamics in Social Networks with Hostile Camps: Consensus vs. Polarization
Most of the distributed protocols for multi-agent consensus assume that the
agents are mutually cooperative and "trustful," and so the couplings among the
agents bring the values of their states closer. Opinion dynamics in social
groups, however, require beyond these conventional models due to ubiquitous
competition and distrust between some pairs of agents, which are usually
characterized by repulsive couplings and may lead to clustering of the
opinions. A simple yet insightful model of opinion dynamics with both
attractive and repulsive couplings was proposed recently by C. Altafini, who
examined first-order consensus algorithms over static signed graphs. This
protocol establishes modulus consensus, where the opinions become the same in
modulus but may differ in signs. In this paper, we extend the modulus consensus
model to the case where the network topology is an arbitrary time-varying
signed graph and prove reaching modulus consensus under mild sufficient
conditions of uniform connectivity of the graph. For cut-balanced graphs, not
only sufficient, but also necessary conditions for modulus consensus are given.Comment: scheduled for publication in IEEE Transactions on Automatic Control,
2016, vol. 61, no. 7 (accepted in August 2015
Opinion Dynamics in Two-Step Process: Message Sources, Opinion Leaders and Normal Agents
According to mass media theory, the dissemination of messages and the
evolution of opinions in social networks follow a two-step process. First,
opinion leaders receive the message from the message sources, and then they
transmit their opinions to normal agents. However, most opinion models only
consider the evolution of opinions within a single network, which fails to
capture the two-step process accurately. To address this limitation, we propose
a unified framework called the Two-Step Model, which analyzes the communication
process among message sources, opinion leaders, and normal agents. In this
study, we examine the steady-state opinions and stability of the Two-Step
Model. Our findings reveal that several factors, such as message distribution,
initial opinion, level of stubbornness, and preference coefficient, influence
the sample mean and variance of steady-state opinions. Notably, normal agents'
opinions tend to be influenced by opinion leaders in the two-step process. We
also conduct numerical and social experiments to validate the accuracy of the
Two-Step Model, which outperforms other models on average. Our results provide
valuable insights into the factors that shape social opinions and can guide the
development of effective strategies for opinion guidance in social networks
Opinion Dynamics With Cross-Coupling Topics: Modeling and Analysis
To model the cross couplings of multiple topics, we develop a set of rules for opinion updates of a group of agents. The rules are used to design or assign values to the elements of weighting matrices. The cooperative and anticooperative couplings are modeled in both the inverse-proportional and proportional structures. The behaviors of opinion dynamics are analyzed using a nullspace property of the state-dependent matrix-weighted Laplacian matrices and a Lyapunov candidate. Various consensus properties of the state-dependent matrix-weighted Laplacian matrices are predicted according to the interagent network topology and interdependent topical coupling topologies
Learning Hidden Influences in Large-Scale Dynamical Social Networks: A Data-Driven Sparsity-Based Approach, in Memory of Roberto Tempo
The processes of information diffusion across social networks (for example, the spread of opinions and the formation of beliefs) are attracting substantial interest in disciplines ranging from behavioral sciences to mathematics and engineering (see "Summary"). Since the opinions and behaviors of each individual are infl uenced by interactions with others, understanding the structure of interpersonal infl uences is a key ingredient to predict, analyze, and, possibly, control information and decisions [1]. With the rapid proliferation of social media platforms that provide instant messaging, blogging, and other networking services (see "Online Social Networks") people can easily share news, opinions, and preferences. Information can reach a broad audience much faster than before, and opinion mining and sentiment analysis are becoming key challenges in modern society [2]. The first anecdotal evidence of this fact is probably the use that the Obama campaign made of social networks during the 2008 U.S. presidential election [3]. More recently, several news outlets stated that Facebook users played a major role in spreading fake news that might have infl uenced the outcome of the 2016 U.S. presidential election [4]. This can be explained by the phenomena of homophily and biased assimilation [5]-[7] in social networks, which correspond to the tendency of people to follow the behaviors of their friends and establish relationships with like-minded individuals