A data-driven kinetic model for opinion dynamics with social network contacts

TL;DR

This paper introduces a new data-driven kinetic model for opinion dynamics that incorporates social media influence, calibrated with real Twitter data to analyze how influencers shape public opinions.

Contribution

It presents a novel mathematical model for opinion formation that explicitly accounts for social network effects and influencer roles, validated with real social media data.

Findings

Influencers significantly impact opinion shifts.

The model accurately reflects social media opinion dynamics.

Social network structure influences collective opinion formation.

Abstract

Opinion dynamics is an important and very active area of research that delves into the complex processes through which individuals form and modify their opinions within a social context. The ability to comprehend and unravel the mechanisms that drive opinion formation is of great significance for predicting a wide range of social phenomena such as political polarization, the diffusion of misinformation, the formation of public consensus, and the emergence of collective behaviors. In this paper, we aim to contribute to that field by introducing a novel mathematical model that specifically accounts for the influence of social media networks on opinion dynamics. With the rise of platforms such as Twitter, Facebook, and Instagram and many others, social networks have become significant arenas where opinions are shared, discussed, and potentially altered. To this aim after an analytical construction of our new model and through incorporation of real-life data from Twitter, we calibrate the model parameters to accurately reflect the dynamics that unfold in social media, showing in particular the role played by the so-called influencers in driving individual opinions towards predetermined directions.