Prediction Model for Information Diffusion in Social Robot Networks for Dynamic Behavior Recognition

Facing the problem that social robots cause false information to spread, this paper 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 a graph attention network (GAT) and a bidirectional long-term and short-term memory network (BiLSTM) is built, and the time sequence characteristics of user behavior, the 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.