The Dynamics of Public Opinion in Complex Networks

This paper studies the problem of public opinion formation and concentrates on the interplays among three factors: individual attributes, environmental influences and information flow. We present a simple model to analyze the dynamics of four types of networks. Our simulations suggest that regular communities establish not only local consensus, but also global diversity in public opinions. However, when small world networks, random networks, or scale-free networks model social relationships, the results are sensitive to the elasticity coefficient of environmental influences and the average connectivity of the type of network. For example, a community with a higher average connectivity has a higher probability of consensus. Yet, it is misleading to predict results merely based on the characteristic path length of networks. In the process of changing environmental influences and average connectivity, sensitive areas are discovered in the system. By sensitive areas we mean that interior randomness emerges and we cannot predict unequivocally how many opinions will remain upon reaching equilibrium. We also investigate the role of authoritative individuals in information control. While enhancing average connectivity facilitates the diffusion of the authoritative opinion, it makes individuals subject to disturbance from non-authorities as well. Thus, a moderate average connectivity may be preferable because then the public will most likely form an opinion that is parallel with the authoritative one. In a community with a scale-free structure, the influence of authoritative individuals keeps constant with the change of the average connectivity. Provided that the influence of individuals is proportional to the number of their acquaintances, the smallest percentage of authorities is required for a controlled consensus in a scale free network. This study shows that the dynamics of public opinion varies from community to community due to the different degree of impressionability of people and the distinct social network structure of the community.Public Opinion, Complex Network, Consensus, Agent-Based Model

Complex networks in nature and society

The first chapter of this thesis provides an introduction to fundamental concepts concerning econophysics, Ising model, and opinion networks. After a glance in a field of econophysics, Chapter 2 illustrates the economic behaviour via the implementation of two methods. The statistical analysis of real economic data will be briefly stated and followed by the agent-based dynamic model describing the commercial activities. Agent-based dynamic model investigates the intrinsic dynamics of trading behaviour and individual income by modelling transaction processes among agents as a network in the economic system. To take a further look into the network, we introduce a mathematical model of ferromagnetism in statistical mechanics which is called Ising model. Every element in the network can be treated as a two-state ({+1,-1} or sometimes {+1,0}) node. The similar methodology is used in the three-or-more-state situation. This kind of modelling method is widely applied in networks of neurosciences, economics, and social sciences. Chapter 3 implements and modifies Ising model of a random neuron network with two types of neurons: inhibitory and excitatory. We numerically studied two mutually coupled networks through mean-field interactions. After 3-step alternation, the model provides some fascinating insights into the neuronal behavior via simulation. In particular, it determinates factors that lead to emergent phenomena in dynamics of neural networks. On the other hand, it also plays a vital role in building up the opinion network. We first show the development of Ising model to opinion network. Then the coupled opinion network model and some of the analytical results are carefully given in Chapter 4. Two opinion networks are interfering each other in the system. This model can describe the opinion network more precisely and give more accurate predictions of the final state. At last, a case of U.S. presidential campaign in 2016 is studied. To investigate a complex system which is associated with a multi-party election campaign, we have focused on the situation when we have two competing parties. We compare the prediction of the theory with data describing the dynamics of the average opinion of the U.S. population collected on a daily basis by various media sources during the last 500 days before the final Trump-Clinton election. The qualitative outcome is in reasonable agreement with the prediction of our theory. In fact, the analyses of these data made within the paradigm of our theory indicate that even in this campaign there were chaotic elements where the public opinion migrated in an unpredictable chaotic way. The existence of such a phase of social chaos reflects the main feature of the human beings associated with some doubts and uncertainty and especially associated with contrarians which undoubtedly exist in any society. Besides, a modern tool, Twitter, with rapid information spreading speed affects the whole procedure substantially. We also take a closer look at the influence of the usage of Twitter on competitors, Trump and Clinton. Once the first sign from Trump began stirring on Twitter, it quickly began to ferment. Using Twitter not only brings strength to Trump as he wished, but also sending potentially backward to Clinton in this nationwide competition

Spread of decisions in the corporate board network

Boards of large corporations sharing some of their directors are connected in complex networks. Boards are responsible for corporations' long-term strategy and are often involved in decisions about a common topic related to the belief in economical growth or recession. We are interested in understanding under which conditions a large majority of boards making a same decision can emerge in the network. We present a model where board directors are engaged in a decision making dynamics based on "herd behavior". Boards influence each other through shared directors. We find that imitation of colleagues and opinion bias due to the interlock do not trigger an avalanche of identical decisions over the board network, whereas the information about interlocked boards' decisions does. There is no need to invoke global public information, nor external driving forces. This model provides a simple endogenous mechanism to explain the fact that boards of the largest corporations of a country can, in the span of a few months, take the same decisions about general topics.Comment: to appear in Advances in Complex Systems, accepted on 27 Nov 200

A network model of mass media opinion dynamics

The coexistence of diverse opinions is necessary for a pluralistic society in which people can confront ideas and make informed choices. The media functions as a primary source of information, and diversity across news sources in the media forms the basis for wider discourse in the public. However, due to numerous economic and social pressures, news sources frequently co-orient their content through what is known as intermedia agenda-setting. Past research on the subject has examined relationships between individual news sources. However, to understand emergent behaviour such as opinion diversity, we cannot simply analyse individual relationships in isolation, but instead need to view the media as a complex system of many interacting entities. The aim of this thesis is to develop and empirically test a method for understanding the network effects that intermedia agenda-setting has on the diversity of expressed opinions within the media. Utilising latent signals extracted from news articles, we put forward a methodology for inferring networks that capture how agendas propagate between news sources via the opinions they express on various topics. By applying this approach to a large dataset of news articles published by globally and locally prominent news organisations, we identify how the structure of intermedia networks is indicative of the level of opinion diversity across various topics. We then develop a theoretical model of opinion dynamics in noisy domains that is motivated by the empirical observations of intermedia agenda formation. From this, we derive a general analytical expression for opinion diversity that holds for any network and depends on the network's topology through its spectral properties alone. Finally, we validate the analytical expression in a linear model against empirical data. This thesis aids our understanding of how to model emergent behaviour of the media and promote diversity

Dynamics of deceptive interactions in social networks

In this paper we examine the role of lies in human social relations by implementing some salient characteristics of deceptive interactions into an opinion formation model, so as to describe the dynamical behaviour of a social network more realistically. In this model we take into account such basic properties of social networks as the dynamics of the intensity of interactions, the influence of public opinion, and the fact that in every human interaction it might be convenient to deceive or withhold information depending on the instantaneous situation of each individual in the network. We find that lies shape the topology of social networks, especially the formation of tightly linked, small communities with loose connections between them. We also find that agents with a larger proportion of deceptive interactions are the ones that connect communities of different opinion, and in this sense they have substantial centrality in the network. We then discuss the consequences of these results for the social behaviour of humans and predict the changes that could arise due to a varying tolerance for lies in society.Comment: 17 pages, 8 figures; Supplementary Information (3 pages, 1 figure