Construction and Representation Learning of Social Heterogeneous Information Networks Based on Multimodal Fusion and Enhanced‐ <scp>HGCN</scp>

ABSTRACT During public health events, social media platforms serve as key channels for disseminating multimodal information especially from government departments. These data are crucial for enhancing public understanding and emergency preparedness. This study proposes a novel framework for constructing and learning representations from social heterogeneous information networks (SHINs) based on multimodal fusion and an Enhanced‐HGCN (Hyperbolic Graph Convolutional Network) model. Approximately 74,403 flu‐related microblog posts were collected, from which multimodal features were extracted to construct a heterogeneous network linking users, posts, and topics. Furthermore, the Enhanced‐HGCN model with a two‐layer graph convolution structure is proposed to learn node embeddings in the SHINs. Experimental results show that our approach significantly outperforms other baseline models including in clustering performance. This research validates the feasibility of multimodal SHINs construction and the effectiveness of the Enhanced‐HGCN, providing a foundation for future applications such as knowledge recommendation and cross‐platform information collaboration.