A Rumor Detection Model Based on Graph Convolutional Networks and Multimodal Features

The rapid development of social media has allowed people to access information through multiple channels, but social media has also become a breeding ground for rumors. Rumor detection models can effectively assess the credibility of information. However, current research mainly relies on text or combined text and image features, which may not be sufficient to capture complex feature information. Therefore, this paper proposes a rumor detection model based on the graph convolutional network (GCN) and multi-modal features. The proposed model constructs a knowledge graph (KG) and leverages the GCN to extract complex relationships between its nodes. Then, an interactive attention network is adopted to deeply integrate features. Furthermore, ResNet101 is utilized to extract salient features from images, addressing the challenges related to fully utilizing additional feature information and capturing text and image features at a deeper level to some extent. Multiple experiments conducted on datasets from Twitter and Weibo platforms demonstrate the efficacy of the proposed approach.