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Migliorare la previsione dell'autenticità delle notizie con un approccio basato sull'apprendimento automatico
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ISBN10: 6630220145
ISBN13: 9786630220148
Publisher: Edizioni Sapienza
Published: Jul 1 2026
Pages: 56
Weight: 0.19
Height: 0.13 Width: 6.00 Depth: 9.00
Language: Italian
ISBN13: 9786630220148
Publisher: Edizioni Sapienza
Published: Jul 1 2026
Pages: 56
Weight: 0.19
Height: 0.13 Width: 6.00 Depth: 9.00
Language: Italian
La rapida proliferazione di disinformazione sulle piattaforme digitali rappresenta una minaccia critica per il dibattito pubblico, i processi democratici e la fiducia nella società . I sistemi di verifica manuale non riescono a tenere il passo con il volume e la velocità di generazione delle fake news, rendendo necessario lo sviluppo di meccanismi di rilevamento automatizzati. Questo lavoro presenta un approccio di machine learning leggero per il rilevamento di fake news, basato su un classificatore *Passive-Aggressive* combinato con la vettorizzazione TF-IDF; il modello è stato addestrato e valutato sul dataset di riferimento WELFake, composto da 72.119 articoli provenienti da diverse piattaforme e pubblicati tra il 2016 e il 2020. Il sistema proposto raggiunge un'accuratezza di classificazione del 96,19% su 14.424 articoli di test, con punteggi di precisione e *recall* compresi tra 0,96 e 0,97 per entrambe le classi e un punteggio F1 bilanciato di 0,96, validato mediante un *wrapper* CalibratedClassifierCV che fornisce output di probabilità calibrati. Oltre alla classificazione, il sistema integra funzionalità di spiegabilità basate su LIME per comprendere le motivazioni delle previsioni a livello di singola parola, la verifica degli URL in tempo reale con analisi della credibilità della fonte, il rilevamento di *clickbait* e la generazione di verdetti supportati da evidenze tramite le API di Gemini.
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