Opinion Dynamics and Communication Networks

TL;DR

This paper models opinion exchange as interactions on a network based on similarity thresholds, revealing how different parameters lead to fragmented or consensus states and how social structures emerge from simple rules.

Contribution

It introduces an abstract opinion formation model using bit-strings and analyzes the transition between opinion fragmentation and consensus, including network structure emergence.

Findings

Low similarity thresholds lead to opinion fragmentation

Higher thresholds result in consensus formation

Emerging networks display non-trivial social structures

Abstract

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as $k$--dimensional bit--strings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold $d_I$. Depending on $d_I$, different behaviour of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters $d_I$ and $k$, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analysing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that non--trivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real--world communication patterns.