Human emotion is one important underlying force affecting and affected by the dynamics of social networks. An interesting question is "can we predict a person's mood based on his historic emotion log and his social network?". In this paper, we propose a MoodCast method based on a dynamic continuous factor graph model for modeling and predicting users' emotions in a social network. MoodCast incorporates users' dynamic status information (e.g., locations, activities, and attributes) and social influence from users' friends into a unified model. Based on the historical information (e.g., network structure and users' status from time 0 to t-1), MoodCast learns a discriminative model for predicting users' emotion status at time t. To the best of our knowledge, this work takes the first step in designing a principled model for emotion prediction in social networks. Our experimental results on both real social network and virtual web-based network show that we can accurately predict emotion status of more than 62% of users and 8+% improvement than the baseline methods.