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Verbesserung der Vorhersage der Authentizität von Nachrichten durch einen maschinellen Lernansatz
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ISBN10: 6630212460
ISBN13: 9786630212464
Publisher: Verlag Unser Wissen
Published: Jul 1 2026
Pages: 60
Weight: 0.21
Height: 0.14 Width: 6.00 Depth: 9.00
Language: German
ISBN13: 9786630212464
Publisher: Verlag Unser Wissen
Published: Jul 1 2026
Pages: 60
Weight: 0.21
Height: 0.14 Width: 6.00 Depth: 9.00
Language: German
Die rasante Verbreitung von Desinformationen auf digitalen Plattformen stellt eine ernsthafte Bedrohung für den öffentlichen Diskurs, demokratische Prozesse und das gesellschaftliche Vertrauen dar. Manuelle Überprüfungssysteme können mit der Menge und der Geschwindigkeit, mit der Fake News generiert werden, nicht Schritt halten, weshalb die Entwicklung automatisierter Erkennungsmechanismen erforderlich ist. Diese Arbeit stellt einen effizienten Ansatz des maschinellen Lernens zur Erkennung von Fake News vor, der einen Passive-Aggressive Classifier in Kombination mit TF-IDF-Vektorisierung nutzt; das Modell wurde auf dem Benchmark-Datensatz WELFake trainiert und evaluiert, welcher 72.119 Nachrichtenartikel aus verschiedenen Quellen (Zeitraum 2016-2020) umfasst. Das vorgeschlagene System erreicht eine Klassifikationsgenauigkeit von 96,19 % bei 14.424 Testartikeln, wobei Precision- und Recall-Werte von 0,96-0,97 für beide Klassen sowie ein ausgewogener F1-Score von 0,96 erzielt werden; die Validierung erfolgte mittels eines CalibratedClassifierCV-Wrappers, der kalibrierte Wahrscheinlichkeitswerte liefert. Über die reine Klassifikation hinaus integriert das System LIME-basierte Erklärbarkeit zur Nachvollziehbarkeit der Vorhersagen auf Wortebene, eine Echtzeit-URL-Überprüfung mit Analyse der Glaubwürdigkeit der Quelle, eine Clickbait-Erkennung sowie die Generierung evidenzbasierter Bewertungen unter Verwendung der Gemini-API.
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