Social networks play a central role in individual interactions and decision making. While it is recognized that networks can correlate behaviors and preferences among connected agents, relatively little work has considered mechanisms for social choice on such networks. We introduce a model for social choice---specifically, consensus decision making---on social networks that reflects dependence among the utilities of connected agents. We define an empathetic social choice framework in which agents derive utility based on both their own intrinsic preferences and the satisfaction of their neighbors. We translate this problem into a weighted form of classical preference aggregation (e.g., social welfare maximization or voting), and develop scalable optimization algorithms for this task. Empirical results validate the effectiveness of our methods and the value of empathetic preferences.