Detecting Fake News on Social Media via Multimodal Semantic Understanding and Enhanced Transformer Architectures

With the rapid development of social media, multimodal news combining text and images has become a primary vehicle for the spread of misinformation due to its strong dissemination power and broad audience reach.Existing fake news detection methods overly rely on textual analysis, making it difficult to effectively capture the deep semantic relationships between visual and textual modalities.Moreover, traditional neural networks often suffer from limited robustness and weak noise resistance when handling heterogeneous multimodal data.Although multimodal learning has recently been introduced into this field, challenges such as insufficient text-image semantic matching and a lack of architectural innovation persist.To address these issues, this study proposes a detection framework that integrates multimodal semantic understanding with an enhanced Transformer architecture.Specifically, a cross-modal attention mechanism is constructed to strengthen the interactive representation of visual and textual features through semantic alignment.Additionally, a hierarchical Transformer structure with a dynamic gating mechanism is designed to optimize the multimodal information fusion strategy, significantly improving the model's adaptability to complex social media scenarios.Experiments conducted on public datasets demonstrate that the proposed method achieves a noticeable improvement in detection accuracy compared to traditional models, while also exhibiting superior recall and noise robustness.This research offers a more efficient technological pathway for fake news detection on social media and promotes the broader application of multimodal learning in the domain of information content security.