  In this paper, we demonstrate how the state-of-the-art machine learning and
text mining techniques can be used to build effective social media-based
substance use detection systems. Since a substance use ground truth is
difficult to obtain on a large scale, to maximize system performance, we
explore different feature learning methods to take advantage of a large amount
of unsupervised social media data. We also demonstrate the benefit of using
multi-view unsupervised feature learning to combine heterogeneous user
information such as Facebook `"likes" and "status updates" to enhance system
performance. Based on our evaluation, our best models achieved 86% AUC for
predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which
significantly outperformed existing methods. Our investigation has also
uncovered interesting relations between a user's social media behavior (e.g.,
word usage) and substance use.
