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Spreading of Competing Information in a Network

by 1,2,†, 1,*,† and 3,†
Dipartimento di Ingegneria, Università di Palermo, Viale delle Scienze, I–90128 Palermo, Italy
I.N.F.N- Sezione di Napoli, 80126 Napoli, Italy
Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università di Messina, Viale F. Stagno d’Alcontres 31, I–98166 Messina, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2020, 22(10), 1169;
Received: 22 September 2020 / Accepted: 13 October 2020 / Published: 17 October 2020
(This article belongs to the Special Issue Quantum Models of Cognition and Decision-Making)
We propose a simple approach to investigate the spreading of news in a network. In more detail, we consider two different versions of a single type of information, one of which is close to the essence of the information (and we call it good news), and another of which is somehow modified from some biased agent of the system (fake news, in our language). Good and fake news move around some agents, getting the original information and returning their own version of it to other agents of the network. Our main interest is to deduce the dynamics for such spreading, and to analyze if and under which conditions good news wins against fake news. The methodology is based on the use of ladder fermionic operators, which are quite efficient in modeling dispersion effects and interactions between the agents of the system. View Full-Text
Keywords: operatorial models; (H,ρ)-induced dynamics; spreading of news operatorial models; (H,ρ)-induced dynamics; spreading of news
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MDPI and ACS Style

Bagarello, F.; Gargano, F.; Oliveri, F. Spreading of Competing Information in a Network. Entropy 2020, 22, 1169.

AMA Style

Bagarello F, Gargano F, Oliveri F. Spreading of Competing Information in a Network. Entropy. 2020; 22(10):1169.

Chicago/Turabian Style

Bagarello, Fabio, Francesco Gargano, and Francesco Oliveri. 2020. "Spreading of Competing Information in a Network" Entropy 22, no. 10: 1169.

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