Fake News Detection Using Generative Artificial Intelligence

Author(s)	Ms. Muskan Shaikh, Prof. Shakila Siddavatam
Country	India
Abstract	The rapid growth of digital and social media platforms has accelerated information dissemination while simultaneously enabling the large-scale spread of fake news and misinformation, which can influence public opinion and undermine trust in credible institutions. Traditional detection approaches that rely on surface-level textual features often fail to capture deeper semantic intent and contextual cues. To address these limitations, this research proposes a hybrid fake news detection framework that integrates Generative Artificial Intelligence with transformer-based deep learning models. A generative model (T5-small) is used to summarize and semantically interpret news articles, and the resulting representation is then evaluated by a BERT-based classifier to determine authenticity. The system further incorporates metadata such as source credibility and publication details to enhance reliability. Implemented using a Flask backend, React frontend, and PostgreSQL database, the proposed architecture is scalable and user-friendly. Experimental results using benchmark datasets such as LIAR and FakeNewsNet indicate improved accuracy, generalization, and explainability compared to single-model approaches.
Keywords	Fake News, Generative AI, BERT, T5, Deep Learning, NLP, Explainable AI
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-16

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International Journal For Multidisciplinary Research