Introduction to statistical physics of media processes: Mediaphysics

TL;DR

This paper introduces mediaphysics, a statistical physics framework for analyzing mass communication processes in social systems, using population distribution models influenced by various external and internal factors.

Contribution

It proposes a novel statistical physics approach to model media processes, incorporating elements from sociophysics and kinetic equations, and applies it successfully to real-world media efficiency problems.

Findings

Effective modeling of media influence using a Schrödinger-type equation.

Good predictive results despite low initial correlations.

Integration of Ising-spin and kinetic models into social process analysis.

Abstract

Processes of mass communications in complicated social or sociobiological systems such as marketing, economics, politics, animal populations, etc. as a subject for the special scientific discipline - "mediaphysics" - are considered in its relation with sociophysics. A new statistical physics approach to analyze these phenomena is proposed. A keystone of the approach is an analysis of population distribution between two or many alternatives: brands, political affiliations, or opinions. Relative distances between a state of a "person's mind" and the alternatives are measures of propensity to buy (to affiliate, or to have a certain opinion). The distribution of population by those relative distances is time dependent and affected by external (economic, social, marketing, natural) and internal (mean-field influential propagation of opinions, synergy effects, etc.) factors, considered as fields. Specifically, the interaction and opinion-influence field can be generalized to incorporate important elements of Ising-spin based sociophysical models and kinetic-equation ones. The distributions were described by a Schrodinger-type equation in terms of Green's functions. The developed approach has been applied to a real mass-media efficiency problem for a large company and generally demonstrated very good results despite low initial correlations of factors and the target variable.