Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on selfreports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect 135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of 0.77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of 0.83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people's Facebook posts and substance use and provide insights about the problem.