On the Integration of Social Context for Enhanced Fake News Detection Using Multimodal Fusion Attention Mechanism

Detecting fake news has become a critical challenge in today's information-dense society. Existing research on fake news detection predominantly emphasizes multi-modal approaches, focusing primarily on textual and visual features. However, despite its clear importance, the integration of social context has received limited attention in the literature. To address this gap, this study proposes a novel three-dimensional multimodal fusion framework that integrates textual, visual, and social context features for effective fake news detection on social media platforms. The proposed methodology leverages an advanced Vision-and-Language Bidirectional Encoder Representations from Transformers multi-task model to extract fused attention features from text and images concurrently, capturing intricate inter-modal correlations. Comprehensive experiments validate the efficacy of the proposed approach. The results demonstrate that the proposed solution achieves the highest balanced accuracy of 77%, surpassing other baseline models. Furthermore, the incorporation of social context features significantly enhances model performance. The proposed multimodal architecture also outperforms state-of-the-art approaches, providing a robust and scalable framework for fake news detection using artificial intelligence. This study contributes to advancing the field by offering a comprehensive and practical engineering solution for combating fake news.