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Améliorer la prédiction de l'authenticité des informations grâce à une approche d'apprentissage automatique
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ISBN10: 6630217586
ISBN13: 9786630217582
Publisher: Editions Notre Savoir
Published: Jul 6 2026
Pages: 60
Weight: 0.21
Height: 0.14 Width: 6.00 Depth: 9.00
Language: French
ISBN13: 9786630217582
Publisher: Editions Notre Savoir
Published: Jul 6 2026
Pages: 60
Weight: 0.21
Height: 0.14 Width: 6.00 Depth: 9.00
Language: French
La prolifération rapide de la désinformation sur les plateformes numériques constitue une menace critique pour le débat public, les processus démocratiques et la confiance au sein de la société. Les systèmes de vérification manuelle ne peuvent suivre le rythme du volume et de la vitesse de propagation des fausses informations, ce qui rend nécessaire le développement de mécanismes de détection automatisés. Ces travaux présentent une approche d'apprentissage automatique légère pour la détection de fausses informations, reposant sur un classifieur Passive-Aggressive combiné à une vectorisation TF-IDF; le modèle a été entraîné et évalué sur le jeu de données de référence WELFake, qui comprend 72 119 articles issus de diverses plateformes et couvrant la période 2016-2020. Le système proposé atteint une précision de classification de 96,19 % sur 14 424 articles de test, avec des scores de précision et de rappel compris entre 0,96 et 0,97 pour les deux classes et un score F1 équilibré de 0,96, le tout validé à l'aide d'un wrapper CalibratedClassifierCV fournissant des probabilités calibrées. Au-delà de la classification, le système intègre des fonctionnalités d'explicabilité basées sur LIME pour justifier les prédictions au niveau des mots, une vérification des URL en temps réel avec analyse de la crédibilité des sources, une détection des titres racoleurs ( clickbait ) ainsi que la génération de verdicts étayés par des preuves grâce à l'API Gemini.
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