Live Streaming Viewer

Research on live streaming viewers focuses on understanding and predicting viewer behavior to improve platform performance and user experience. Current efforts concentrate on accurately modeling viewer engagement (e.g., watch time, comments), identifying and mitigating biases in content moderation systems, and developing robust metrics for evaluating audio-visual synchronization. These advancements leverage techniques like quantile regression, machine learning for sentiment analysis and preference prediction, and large-scale datasets to achieve more accurate predictions and fairer content moderation, ultimately leading to enhanced user experience and platform optimization.