Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection

Many recent studies have demonstrated that the propagation patterns of news on social media can facilitate the detection of fake news. Most of these studies rely on the complete propagation networks to build their model, which is not fully available in the early stages and may take a long time to complete. Hence, relying on the complete propagation network is not ideal for fake news early detection. However, detecting fake news as early as possible is important due to their fast-spreading nature and the significant harm they can cause. In addition, most existing propagation network-based fake news detection techniques are not explicitly designed to jointly emphasise informative cascades and nodes in the propagation networks to detect fake news. To bridge these research gaps, this work proposes Propagation2Vec, a novel fake news early detection technique, which assigns varying levels of importance for the nodes and cascades in propagation networks, and reconstructs the knowledge of complete propagation networks based on their partial propagation networks at an early detection deadline. Our experiments show that our model can achieve state-of-the-art performance while only having access to the early stage propagation networks. Furthermore, we devise general explanations for the underlying logic of Propagation2Vec based on its attention weights assigned to different nodes and cascades, which improves the applicability of our approach and facilitates future research on propagation network-based fake news detection.