Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use


  • Zi-Yi Dou Carnegie Mellon University
  • Anamika Barman-Adhikari University of Denver
  • Fei Fang Carnegie Mellon University
  • Amulya Yadav Pennsylvania State University




Computational Social Science


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 self-reports 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.




How to Cite

Dou, Z.-Y., Barman-Adhikari, A., Fang, F., & Yadav, A. (2021). Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14748-14756. https://doi.org/10.1609/aaai.v35i17.17732



AAAI Special Track on AI for Social Impact