AI-Driven Detection of Fake News and Sentiment Analysis: A Framework for Truth Verification and Emerging Pattern Recognition
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P126Keywords:
AI, Fake News Detection, Sentiment Analysis, Transformer Models, Graph Pattern Analysis, Truth VerificationAbstract
The rapid spread of misinformation on digital platforms poses a significant threat to societal stability and democratic discourse. This paper proposes an artificial intelligence (AI)-driven framework for fake news detection, sentiment analysis, and truth verification. The model integrates transformer-based natural language processing (NLP) architectures with graph-based pattern recognition to distinguish factual content from fictitious narratives. We also introduce a novel hybrid model combining contextual embeddings and graph topology learning to identify emerging patterns in information dissemination
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