advanced innovations in social media rumor detection: integrating graph neural networks and deep learning - a review

This study aimed to review the impact of deep learning (dl) techniques on rumor detection in social media platforms, focusing on the distinctive features and user interactions on twitter and sina weibo. we have endeavored to compare the outcomes obtained from recurrent neural networks (rnn), convolutional neural networks (cnn), and graph neural networks (gnn). beyond a cursory review of existing methods, we briefly investigate the structure of two approaches, graph robot aware (sbag) and graph convolutional rumor detection system (gcres), both of which employ the graph neural networks (gnn) method. these two approaches are significant because, in addition to examining the content of rumors, they pay attention to the pattern of their spread through graph neural networks (gnn) for rumor detection. these advancements underscore the potential of dl and gnn in addressing the challenge of rumor detection in social media and emphasize the importance of continuing innovation in this rapidly evolving field.