Live video streaming over a Peer-to-Peer (P2P) architecture is promising due to its scalability and ease of deployment. Nevertheless, P2P-based video streaming systems still face some challenges regarding their performance. These systems are in fact overlays of users who control peers. As peers depend upon each other for receiving the video stream, the user behavior has an impact over the performance of the system. We collect the user behavior studies over live video streaming systems and identify the impact of different user activities on the performance. Based on this information, we propose a Bayesian network that models a generic user behavior initially and then adapts itself to individuals through learning from observations. We validate our model through simulations.