Facing the problem that social robots cause false information to spread, this article constructs an information diffusion prediction model for dynamic behavior recognition. The model combines deep learning with spatio-temporal modeling technology, aiming at improving the recognition accuracy of social robots with strong camouflage and realizing the dynamic prediction of their information propagation paths. In this study, a multimodal recognition model based on graph attention network (GAT) and bidirectional long-term and short-term memory network (BiLSTM) is built, and the time sequence characteristics of user behavior, semantic content of text and social network structure are modeled by combining them. Based on this, a spatio-temporal graph convolution network (ST-GCN) is designed to model the information diffusion process, capture the spatio-temporal dependence in communication, and predict the future diffusion trend. The results show that the F1-score of the proposed recognition model reaches 0.892, which is about 8% higher than that of the traditional graph convolution network (GCN). In the diffusion prediction task, ST-GCN can respond to sudden propagation events more accurately. This study provides a technical route with dynamic perception and prediction ability for social platforms.
Zhang et al. (Thu,) studied this question.