Multimodal fake news detection using graph neural networks and attention mechanisms

The rapid spread of fake news across digital platforms poses a significant challenge to societies, leading to a growing demand for robust detection mechanisms. Traditional fake news detection methods often rely on unimodal data, such as textual content, limiting their effectiveness in addressing the complex, and multimodal nature of fake news. This paper introduces a Multimodal Fake News Detector (MFND) that integrates textual, visual, and social context features to enhance detection accuracy. This makes classification tasks more accurate and reliable. The MFND was evaluated using the FakeNewsNet and Sina Weibo datasets, achieving high accuracy and outperforming existing models. The experimental results highlight the importance of multimodal fusion and attention-based weighting mechanisms in improving detection performance, particularly in complex social media environments. This research demonstrates the potential of multimodal approaches for more accurate and reliable fake news detection.