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

Are Opinions Based on Science: Modelling Social Response to Scientific Facts

As scientists we like to think that modern societies and their members base their views, opinions and behaviour on scientific facts. This is not necessarily the case, even though we are all (over-) exposed to information flow through various channels of media, i.e. newspapers, television, radio, internet, and web. It is thought that this is mainly due to the conflicting information on the mass media and to the individual attitude (formed by cultural, educational and environmental factors), that is, one external factor and another personal factor. In this paper we will investigate the dynamical development of opinion in a small population of agents by means of a computational model of opinion formation in a co-evolving network of socially linked agents. The personal and external factors are taken into account by assigning an individual attitude parameter to each agent, and by subjecting all to an external but homogeneous field to simulate the effect of the media. We then adjust the field strength in the model by using actual data on scientific perception surveys carried out in two different populations, which allow us to compare two different societies. We interpret the model findings with the aid of simple mean field calculations. Our results suggest that scientifically sound concepts are more difficult to acquire than concepts not validated by science, since opposing individuals organize themselves in close communities that prevent opinion consensus.Comment: 21 pages, 5 figures. Submitted to PLoS ON

Statistical Physics of Opinion and Social Conflict

The rise and development of opinion groups, just as their clash in social conflict, are notoriously difficult to study due to a complex interplay between structure and dynamics. The intricate feedback between psychological and sociological processes, tied with an ample variability of individual traits, makes these systems challenging both intellectually and methodologically. Yet regular patterns do emerge from the collective behavior of dissimilar people, seen in population and crime rates, in protest movements and the adoption of innovations. Statistical physics comes then as an apt and successful framework for their study, characterizing society as the common product of single wills, interactions among people and external effects. The work in this Thesis provides mathematical descriptions for the evolution of opinions in society, based on simple mechanisms of individual conduct and group influence. Such models abstract the inherent complexity of human behavior by reducing people to opinion variables spread over a network of social interactions, with variables and interactions changing in time at the pace of a handful of equations. Their macroscopic properties are interpreted as the emergence of social groups and of conflict between them due to opinion disagreement, and compared with small controlled experiments or with large online records of social activity. The extensive analysis of these models, both numerical and analytical, leads to a couple of generic observations on the link between opinion and social conflict. First, the emergence of consensual groups in society may be regulated by well-separated time scales of opinion dynamics and network evolution, and by a distribution of personality traits in the population. Our social environment can then be fragmented as more people turn against the collective mood, ultimately forming minorities as a response to external influence. Second, the exchange of views in collaborative tasks may lead not only to the rise and resolution of opinion issues, but to an intermediate state where conflicts appear periodically. In this way strife and cooperation, so much a part of human nature, can be emulated by surprisingly simple interactions among individuals

Opinion formation on social networks with algorithmic bias: Dynamics and bias imbalance

We investigate opinion dynamics and information spreading on networks under the influence of content filtering technologies. The filtering mechanism, present in many online social platforms, reduces individuals' exposure to disagreeing opinions, producing algorithmic bias. We derive evolution equations for global opinion variables in the presence of algorithmic bias, network community structure, noise (independent behavior of individuals), and pairwise or group interactions. We consider the case where the social platform shows a predilection for one opinion over its opposite, unbalancing the dynamics in favor of that opinion. We show that if the imbalance is strong enough, it may determine the final global opinion and the dynamical behavior of the population. We find a complex phase diagram including phases of coexistence, consensus, and polarization of opinions as possible final states of the model, with phase transitions of different order between them. The fixed point structure of the equations determines the dynamics to a large extent. We focus on the time needed for convergence and conclude that this quantity varies within a wide range, showing occasionally signatures of critical slowing down and meta-stability

Opinion dynamics in social networks: From models to data

Opinions are an integral part of how we perceive the world and each other. They shape collective action, playing a role in democratic processes, the evolution of norms, and cultural change. For decades, researchers in the social and natural sciences have tried to describe how shifting individual perspectives and social exchange lead to archetypal states of public opinion like consensus and polarization. Here we review some of the many contributions to the field, focusing both on idealized models of opinion dynamics, and attempts at validating them with observational data and controlled sociological experiments. By further closing the gap between models and data, these efforts may help us understand how to face current challenges that require the agreement of large groups of people in complex scenarios, such as economic inequality, climate change, and the ongoing fracture of the sociopolitical landscape.Comment: 22 pages, 3 figure