@inproceedings{ding-etal-2017-multi,
    title = "Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction",
    author = "Ding, Tao  and
      Bickel, Warren K.  and
      Pan, Shimei",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D17-1241/",
    doi = "10.18653/v1/D17-1241",
    pages = "2275--2284",
    abstract = "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."
}