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

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

Date Published: 2019

DOI: 10.1109/access.2019.2902400

Sponsors: Chinese National Natural Science Foundation

Platforms: No platforms listed

Model Documentation: Mathematical description Other Narrative

Model Code URLs: Model code not found

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.
Tags
Game Length Internet Rumors Complex networks Agent interaction Communicative distortion Information polarization Information propagation Social dynamics Behavioral sciences information theory