A normative approach to radicalization in social networks

Bouttier, Vincent; Leclercq, Salome; Jardri, Renaud; Denève, Sophie

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1007/s42001-024-00267-6

Permalink :

http://hdl.handle.net/20.500.12210/114217

Title :

A normative approach to radicalization in social networks

Author(s) :

Bouttier, Vincent [Auteur]
Laboratoire de Neurosciences Cognitives & Computationnelles [LNC2]
Leclercq, Salome [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Jardri, Renaud [Auteur]

Lille Neurosciences & Cognition (LilNCog) - U 1172
Denève, Sophie [Auteur]
Laboratoire de Neurosciences Cognitives & Computationnelles [LNC2]

Journal title :

Journal of Computational Social Science

Abbreviated title :

J. Comput. Soc. Sci.

Volume number :

Pages :

Publication date :

2024-04-10

ISSN :

2432-2717

English keyword(s) :

Bayes
Belief
Circular
Inference
Radicalization
Polarization

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

In recent decades, the massification of online social connections has made information globally accessible in a matter of seconds. Unfortunately, this has been accompanied by a dramatic surge in extreme opinions, without ...
Show more >In recent decades, the massification of online social connections has made information globally accessible in a matter of seconds. Unfortunately, this has been accompanied by a dramatic surge in extreme opinions, without a clear solution in sight. Using a model performing probabilistic inference in large-scale loopy graphs through exchange of messages between nodes, we show how circularity in the social graph directly leads to radicalization and the polarization of opinions. We demonstrate that these detrimental effects could be avoided if the correlations between incoming messages could be decreased. This approach is based on an extension of Belief Propagation (BP) named Circular Belief Propagation (CBP) that can be trained to drastically improve inference within a cyclic graph. CBP was benchmarked using data from Facebook© and Twitter©. This approach could inspire new methods for preventing the viral spreading and amplification of misinformation online, improving the capacity of social networks to share knowledge globally without resorting to censorship.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
Inserm
CHU Lille

Collections :

Lille Neurosciences & Cognition (LilNCog) - U 1172

Submission date :

2024-05-06T23:15:16Z
2024-06-10T10:51:57Z

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Except where otherwise noted, this item's license is described as Attribution 3.0 United States

Version	Link	Modification date
2	20.500.12210/114217*	2024-06-10T10:38:56Z
1	20.500.12210/114217.1	2024-05-06T23:15:16Z

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