123,918 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 individualsâ 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
Link updating strategies influence consensus decisions as a function of the direction of communication
Consensus decision-making in social groups strongly depends on communication
links that determine to whom individuals send, and from whom they receive,
information. Here, we ask how consensus decisions are affected by strategic
updating of links and how this effect varies with the direction of
communication. We quantified the co-evolution of link and opinion dynamics in a
large population with binary opinions using mean-field numerical simulations of
two voter-like models of opinion dynamics: an Incoming model (where individuals
choose who to receive opinions from) and an Outgoing model (where individuals
choose who to send opinions to). We show that individuals can bias group-level
outcomes in their favor by breaking disagreeing links while receiving opinions
(Incoming Model) and retaining disagreeing links while sending opinions
(Outgoing Model). Importantly, these biases can help the population avoid
stalemates and achieve consensus. However, the role of disagreement avoidance
is diluted in the presence of strong preferences - highly stubborn individuals
can shape decisions to favor their preferences, giving rise to non-consensus
outcomes. We conclude that collectively changing communication structures can
bias consensus decisions, as a function of the strength of preferences and the
direction of communication
Dynamics of Ideological Biases of Social Media Users
Humanity for centuries has perfected skills of interpersonal interactions and
evolved patterns that enable people to detect lies and deceiving behavior of
others in face-to-face settings. Unprecedented growth of people's access to
mobile phones and social media raises an important question: How does this new
technology influence people's interactions and support the use of traditional
patterns? In this paper, we answer this question for homophily driven patterns
in social media. In our previous studies, we found that, on a university
campus, changes in student opinions were driven by the desire to hold popular
opinions. Here, we demonstrate that the evolution of online platform-wide
opinion groups is driven by the same desire. We focus on two social media:
Twitter and Parler, on which we tracked the political biases of their users. On
Parler, an initially stable group of right-biased users evolved into a
permanent right-leaning echo chamber dominating weaker, transient groups of
members with opposing political biases. In contrast, on Twitter, the initial
presence of two large opposing bias groups led to the evolution of a bimodal
bias distribution, with a high degree of polarization. We capture the movement
of users from the initial to final bias groups during the tracking period. We
also show that user choices are influenced by side-effects of homophily. The
users entering the platform attempt to find a sufficiently large group whose
members hold political bias within the range sufficiently close to the new
user's bias. If successful, they stabilize their bias and become a permanent
member of the group. Otherwise, they leave the platform. We believe that the
dynamics of users uncovered in this paper create a foundation for technical
solutions supporting social groups on social media and socially aware networks.Comment: 7 pages, 4 figures, submitted to IEEE Communications Magazin
Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network
We study the problem of optimally investing in nodes of a social network in a
competitive setting, where two camps aim to maximize adoption of their opinions
by the population. In particular, we consider the possibility of campaigning in
multiple phases, where the final opinion of a node in a phase acts as its
initial biased opinion for the following phase. Using an extension of the
popular DeGroot-Friedkin model, we formulate the utility functions of the
camps, and show that they involve what can be interpreted as multiphase Katz
centrality. Focusing on two phases, we analytically derive Nash equilibrium
investment strategies, and the extent of loss that a camp would incur if it
acted myopically. Our simulation study affirms that nodes attributing higher
weightage to initial biases necessitate higher investment in the first phase,
so as to influence these biases for the terminal phase. We then study the
setting in which a camp's influence on a node depends on its initial bias. For
single camp, we present a polynomial time algorithm for determining an optimal
way to split the budget between the two phases. For competing camps, we show
the existence of Nash equilibria under reasonable assumptions, and that they
can be computed in polynomial time
Attitude Dynamics with Limited Verbalisation Capabilities
This article offers a new perspective for research on opinion dynamics. It demonstrates the importance of the distinction of opinion and attitude, which originally has been discussed in literature on consumer behaviour. As opinions are verbalised attitudes not only biases in interpretation and adoption processes have to be considered but also verbalisation biases should be addressed. Such biases can be caused by language deficits or social norms. The model presented in this article captures the basic features of common opinion dynamic models and additionally biases in the verbalisation process. Further, it gives a first analysis of this model and shows that precision as bias in the verbalisation process can influence the dynamics significantly. Presenting and applying the concept of area of influential attitudes the impact of each parameter (selective attitude, selective interpretation, and precision) is analysed independently. Some preliminary results for combined effects are presented.Opinion dynamics, attitude dynamics, verbalisation, selective attitude, selective interpretation, area of influential attitudes
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