From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications

TL;DR

This paper develops simplified models for fake news spread on social networks, incorporating agent competence and learning, to better understand and mitigate misinformation effects using data-driven approaches.

Contribution

It introduces reduced-order models derived from kinetic multi-agent frameworks, integrating social closure and mean-field approximation for data compatibility.

Findings

Models capture the influence of competence on fake news spread

Application to Twitter data demonstrates model relevance

Simplified models facilitate intervention strategies

Abstract

Fake news spreading, with the aim of manipulating individuals' perceptions of facts, is now recognized as a major problem in many democratic societies. Yet, to date, little has been understood about how fake news spreads on social networks, what the influence of the education level of individuals is, when fake news is effective in influencing public opinion, and what interventions might be successful in mitigating their effect. In this paper, starting from the recently introduced kinetic multi-agent model with competence by the first two authors, we propose to derive reduced-order models through the notion of social closure in the mean-field approximation that has its roots in the classical hydrodynamic closure of kinetic theory. This approach allows to obtain simplified models in which the competence and learning of the agents maintain their role in the dynamics and, at the same time, the structure of such models is more suitable to be interfaced with data-driven applications. Examples of different Twitter-based test cases are described and discussed.