Linking theory and empirics: a general framework to model opinion formation processes

TL;DR

This paper presents a flexible opinion formation model that bridges theoretical and empirical social dynamics, capable of reproducing diverse social phenomena and calibrated with real online social network data.

Contribution

The authors introduce a minimal, adaptable opinion formation model that can be calibrated with real data and incorporates communication features like selectivity and personalization.

Findings

Model reproduces fragmented and polarizing social systems.

Artificial society exhibits properties similar to real-world social systems.

Model calibrated successfully with empirical online social network data.

Abstract

We introduce a minimal opinion formation model, which is quite flexible and can reproduce a broad variety of the existing micro-influence assumptions and models. At the same time, the model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically. Our analysis is exemplified by recently reported empirical data drawn from an online social network. Employing these data for the model calibration, we demonstrate that the model may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.