Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction
Tao Ding, Warren Kurt Bickel, Shimei Pan · 2017
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 unsupervised 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 illicit 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.