Predicting potential real-time donations in YouTube live streaming services via continuous-time dynamic graphs

Abstract

Online live streaming platforms, such as YouTube Live and Twitch, have seen a surge in popularity in recent years. These platforms allow viewers to send real-time gifts to streamers, which can bring significant profits and fame. However, there has been little research on the donation system used on live streaming platforms. This paper aims to fill this gap by building a continuous-time dynamic graph to model the interactions among viewers based on real-time chat messages and predict the real-time donations on live streaming platforms. To achieve this, we propose a novel model called the Temporal Difference Graph Neural Network (TDGNN) that incorporates imbalanced learning strategies to identify potential donors during live streaming. Our model can predict the exact time when donations will appear. We conduct extensive experiments on three live streaming video datasets and demonstrate that our proposed model is more effective and robust than other baseline methods from other fields.