Fake News Detection System Using Machine Learning

This paper reviewed text-based and evidence-assisted approaches to fake-news detection and described a prototype with two complementary components. The first uses TF-IDF and Multinomial Naive Bayes and achieved 95.23% accuracy on the reported ISOT test split. That score reflects classification within the dataset and does not establish factual correctness for unseen news. The second component retrieves material from Google News RSS, NewsAPI, and Wikipedia and asks Gemini to assess a claim as REAL, FAKE, or UNVERIFIED. This component has been manually tested but has not yet undergone formal accuracy evaluation. The main priorities are to test generalisation across sources and time periods, measure live-verification performance with labelled claims, improve reproducibility, and communicate uncertainty responsibly. These limitations reflect broader challenges involving accuracy, interpretability, evidence quality, and computational cost.

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