Rumor Detection Model

Rumor detection models aim to automatically identify false information spreading on social media, focusing on early detection and robust performance against adversarial attacks. Current research heavily utilizes graph neural networks and other deep learning architectures to analyze both the textual content of posts and their propagation patterns across social networks, incorporating user interactions and semantic evolution over time. Improved model generalization to unseen rumors and mitigation of biases in training data remain significant challenges, impacting the reliability and practical applicability of these models for combating misinformation.