From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications.
Agent-based models
Competence
Data uncertainty
Fake news spreading
Kinetic models
Learning dynamics
Social closure
Journal
SN partial differential equations and applications
ISSN: 2662-2971
Titre abrégé: SN Partial Differ Equ Appl
Pays: Germany
ID NLM: 101770736
Informations de publication
Date de publication:
2022
2022
Historique:
received:
21
02
2022
accepted:
02
08
2022
entrez:
10
10
2022
pubmed:
11
10
2022
medline:
11
10
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36213149
doi: 10.1007/s42985-022-00194-z
pii: 194
pmc: PMC9527739
doi:
Types de publication
News
Langues
eng
Pagination
68Informations de copyright
© The Author(s) 2022.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare no competing interests.
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