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Poprawa przewidywania autentyczności wiadomości za pomocą metod uczenia maszynowego
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ISBN10: 6630222709
ISBN13: 9786630222708
Publisher: Wydawnictwo Nasza Wiedza
Published: Jul 1 2026
Pages: 60
Weight: 0.21
Height: 0.14 Width: 6.00 Depth: 9.00
Language: Polish
ISBN13: 9786630222708
Publisher: Wydawnictwo Nasza Wiedza
Published: Jul 1 2026
Pages: 60
Weight: 0.21
Height: 0.14 Width: 6.00 Depth: 9.00
Language: Polish
Gwaltowne rozprzestrzenianie się dezinformacji na platformach cyfrowych stanowi poważne zagrożenie dla debaty publicznej, procesów demokratycznych oraz zaufania spolecznego. Systemy ręcznej weryfikacji nie są w stanie nadążyc za skalą i tempem generowania falszywych wiadomości, co wymusza opracowanie zautomatyzowanych mechanizmów ich wykrywania. W niniejszej pracy przedstawiono lekkie rozwiązanie oparte na uczeniu maszynowym, wykorzystujące klasyfikator Passive-Aggressive oraz wektoryzację TF-IDF; model ten zostal wytrenowany i oceniony na zbiorze testowym WELFake, obejmującym 72 119 artykulów pochodzących z różnych platform i opublikowanych w latach 2016-2020. Proponowany system osiąga skutecznośc klasyfikacji na poziomie 96,19% dla 14 424 artykulów testowych, uzyskując wskaźniki precyzji i pelności (recall) w przedziale 0,96-0,97 dla obu klas oraz zrównoważoną miarę F1 równą 0,96, przy czym walidację przeprowadzono z użyciem mechanizmu CalibratedClassifierCV, zapewniającego skalibrowane wartości prawdopodobieństwa. Oprócz samej klasyfikacji, system oferuje funkcje wyjaśnialności oparte na metodzie LIME (umożliwiające zrozumienie przeslanek decyzji na poziomie poszczególnych slów), weryfikację adresów URL w czasie rzeczywistym wraz z analizą wiarygodności źródla, wykrywanie clickbaitów oraz generowanie werdyktów popartych dowodami przy użyciu interfejsu API modelu Gemini.
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