Mass Media and Polarisation Processes in the Bounded Confidence Model of Opinion Dynamics

This paper presents a social simulation in which we add an additional layer of mass media communication to the social network \'bounded confidence\' model of Deffuant et al (2000). A population of agents on a lattice with continuous opinions and bounded confidence adjust their opinions on the basis of binary social network interactions between neighbours or communication with a fixed opinion. There are two mechanisms for interaction. \'Social interaction\' occurs between neighbours on a lattice and \'mass communication,\' adjusts opinions based on an agent interacting with a fixed opinion. Two new variables are added, polarisation: the degree to which two mass media opinions differ, and broadcast ratio: the number of social interactions for each mass media communication. Four dynamical regimes are observed, fragmented, double extreme convergence, a state of persistent opinion exchange leading to single extreme convergence and a disordered state. Double extreme convergence is found where agents are less willing to change opinion and mass media communications are common or where there is moderate willingness to change opinion and a high frequency of mass media communications. Single extreme convergence is found where there is moderate willingness to change opinion and a lower frequency of mass media communication. A period of persistent opinion exchange precedes single extreme convergence, it is characterized by the formation of two opposing groups of opinion separated by a gradient of opinion exchange. With even very low frequencies of mass media communications this results in a move to central opinions followed by a global drift to one extreme as one of the opposing groups of opinion dominates. A similar pattern of findings is observed for Neumann and Moore neighbourhoods.Opinion Dynamics, Mass Media, Polarisation, Extremists, Consensus

Hydrodynamic models of preference formation in multi-agent societies

In this paper, we discuss the passage to hydrodynamic equations for kinetic models of opinion formation. The considered kinetic models feature an opinion density depending on an additional microscopic variable, identified with the personal preference. This variable describes an opinion-driven polarisation process, leading finally to a choice among some possible options, as it happens e.g. in referendums or elections. Like in the kinetic theory of rarefied gases, the derivation of hydrodynamic equations is essentially based on the computation of the local equilibrium distribution of the opinions from the underlying kinetic model. Several numerical examples validate the resulting model, shedding light on the crucial role played by the distinction between opinion and preference formation on the choice processes in multi-agent societies.Comment: 30 pages, 15 figure

Effects of noise and confidence thresholds in nominal and metric Axelrod dynamics of social influence

We study the effects of bounded confidence thresholds and of interaction and external noise on Axelrod's model of social influence. Our study is based on a combination of numerical simulations and an integration of the mean-field Master equation describing the system in the thermodynamic limit. We find that interaction thresholds affect the system only quantitatively, but that they do not alter the basic phase structure. The known crossover between an ordered and a disordered state in finite systems subject to external noise persists in models with general confidence threshold. Interaction noise here facilitates the dynamics and reduces relaxation times. We also study Axelrod systems with metric features, and point out similarities and differences compared to models with nominal features. Metric features are used to demonstrate that a small group of extremists can have a significant impact on the opinion dynamics of a population of Axelrod agents.Comment: 15 pages, 12 figure

Using Agent-Based Modelling to Investigate Intervention Algorithms to Reduce Polarisation in Online Social Networks

Across much of the western world, political polarisation is on the rise. This has the effect of hindering political discourse, stifling open discussion, and in extreme cases has led to violence. The process of polarising and radicalising vulnerable individuals has migrated to social media websites, which have been implicated in several high profile terror attacks. Within this thesis we model and investigate various algorithms to prevent the spread of polarisation and extremist ideology by employing agent-based modelling techniques from the field of opinion dynamics. The contributions of our work include the following aspects. Firstly, we have developed a unified framework for opinion dynamics, allowing us to experiment easily on a number of different existing models and bringing together sometimes disparate innovations from across the field into one system. Secondly, this unified framework has been implemented in a modular simulator able to perfectly replicate results from purpose-built, stand-alone simulators for two widely used models, namely Relative Agreement and CODA, and then released to the public as the first general-purpose opinion dynamics simulator. Thirdly, we have developed two new intervention algorithms, along with a new metric for measuring the effectiveness of an intervention strategy, which aim to reduce the spread of polarisation across a network with low computational cost. These methods are compared to existing centrality-based methods upon a random network. The experimental results show our proposed approaches outperform centrality measures. We find that our ii iii algorithms are able to prevent up to 40% of non-extremist agents becoming extreme by removing only 10% of the network’s edges. Fourthly, we have investigated the efficacy of these intervention algorithms on polarisation under different scenarios (e.g. variable costs, different network structures). The experimental validation proves the proposed approach is robust and has performed favourably compared existing methods such as centrality-based methods especially on the second type of network. Finally, we have developed a broadcast-based communication system for agents, designed to mimic the one-way broadcast nature of a public social media post such as Twitter, in contrast to the existing model which emulates a two-way private conversation. The experimental result shows a lessening of the impact of our interventions, demonstrating the need for further investigation of such communication methods