Detecting Deception: A Multimodal Deep Learning Approach for Fake News Identification Using Text and Social Signals

The spread of fake news on social media is a big threat to public discourse, democracy, and trust in society. Conventional unimodal methodologies that depend exclusively on textual content have demonstrated inadequacy in encapsulating the intricate dynamics of misinformation dissemination. This paper shows a full multimodal deep learning framework that combines text with social signals to help find fake news more easily. We use the latest transformer architectures to encode text, graph neural networks to model how social information spreads, and adaptive fusion mechanisms to combine content features with social context. The proposed methodology addresses significant deficiencies in the current literature, specifically the insufficient acquisition of structural social information and the discordance between content and social modalities. By systematically an- alyzing recent studies, we show that multimodal approaches always do better than unimodal baselines. For example, on benchmark datasets, the accuracies were 94.3% and the F1-scores were 92.8%. This work integrates contemporary methodological trends, delineates enduring research deficiencies, and introduces an innovative framework that enhances the forefront of auto- mated fake news detection by adeptly modeling the interaction between content semantics and social propagation dynamics.