Authors: Durafe Sejal Dattatray, Prof. N. G. Rokade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Fake news and misinformation on digital platforms threaten public trust and information integrity, and manual verification cannot keep pace with the volume of content created daily. This paper reviews AI-based fake news detection beyond simple text classification, focusing on deep learning and social-context methods. It examines early credibility features on social media, recurrent networks that model the time series of user responses to rumours, hybrid models that combine article text with user behaviour, explainable models that use user comments to justify decisions, and graph-based geometric deep learning that analyses how news spreads. Machine-generated fake news and multimodal datasets combining text and images are also discussed, together with support vector machines and random forests as baselines. A five-layer real-time detection architecture is proposed that fuses content, user and propagation signals, followed by research gaps, objectives, a comparison of approaches and a discussion of challenges including early detection before content spreads, cross-platform generalization, adversarial manipulation and the need for explainability. The review concludes that combining what is said, who says it and how it spreads produces more accurate and trustworthy fake news detection than content analysis alone.
DOI: https://doi.org/10.5281/zenodo.24220335
