Progressive Information Polarization in a Complex-Network Entropic Social Dynamics Model

Chao Wang,Kang Hao Cheong,Neng-Gang Xie,Jin Ming Koh

doi:10.1109/access.2019.2902400

Chao Wang, Kang Hao Cheong + Show 2 more

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https://doi.org/10.1109/access.2019.2902400

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Abstract

The advent of social media and technologies augmenting social communication has dramatically amplified the role of rumor spreading in shaping society, via means of misinformation and fact distortion. Existing research commonly utilize contagion mechanisms, statistical mechanics frameworks, or complex-network opinion dynamics models. In this paper, we incorporate information distortion and polarization effects into an opinion dynamics model based on information entropy, modeling imprecision in human memory and communication, and the consequent progressive drift of information toward subjective extremes. Simulation results predict a wide variety of possible system behavior, heavily dependent on the relative trust placed on individuals of differing social connectivity. Mass-polarization toward a positive or negative consensus occurs when a synergistic mechanism between preferential trust and polarization tendencies is sustained; a division of the population into segregated groups of different polarity is also possible under certain conditions. These results may aid in the analysis and prediction of opinion polarization phenomena on social platforms, and the presented agent-based modeling approach may aid in the simulation of complex-network information systems.

Highlights

The information age is characterized by a distinct shift towards computerization and interpersonal networking technology, with a natural consequence of vastly accelerated information uptake and sharing by the average individual [1], [2]
The intrinsic relationship between polarization probability ξ and the bias factor γ is first explored in Section III-A, thereby providing a basis to facilitate the qualitative understanding of the various phenomena emergent from the propagation model
In the present study, the semantics of propagated information is taken to be categorizable into binary opposing extrema, in particular, a negative polarization extreme represented as an information bit string of minimal value, and a positive polarization extreme represented as one of maximal value

Summary

Introduction

The information age is characterized by a distinct shift towards computerization and interpersonal networking technology, with a natural consequence of vastly accelerated information uptake and sharing by the average individual [1], [2]. Non-hierarchical content distribution, common on large-scale unrestricted social network platforms, have vastly accentuated the role of rumor spreading in social communication, with potential implications including the skewing of political alignments and election results [3]–[5], the molding of public opinion in countries [6], [7], and even the manipulation of financial markets [8], [9]. A standard model of rumor spreading, known as the Daley-Kendall (DK) model [18], [19], is well-established and has been used extensively in the study of opinion dynamics. Various extensions of the model have since been reported, including the incorporation of complex network topologies [20]–[22], and the development of the stochastic Maki-Thompson model variant [23], [24], with analytical solutions derived via means of interacting Markov chains [25]. The effects of memory-facilitated opinion contagion have been investigated [26]–[29]; in the

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