User Behavior Prediction on Social Hotspots Using Multi-Message Interaction and Neural Networks

This study employs a Recurrent Neural Network (RNN) architecture and multi-message interaction data to develop a predictive model for social hotspot behavior. Social hotspot dynamics are influenced by temporal trends that are captured by the model through the analysis of patterns in user-generated interactions. With the addition of an Attention Mechanism, the RNN efficiently ranks important user interactions, enabling more accurate hotspot development prediction. This framework supports real-time applications in social media analytics and user engagement monitoring by providing a precise, data-driven approach to social behavior trend prediction.