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        <title>Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction
%A Ding, Tao
%A Bickel, Warren K.
%A Pan, Shimei
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F ding-etal-2017-multi
%X 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.
%R 10.18653/v1/D17-1241
%U https://aclanthology.org/D17-1241
%U https://doi.org/10.18653/v1/D17-1241
%P 2275-2284

Markdown (Informal)

[Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction](https://aclanthology.org/D17-1241) (Ding et al., EMNLP 2017)

• Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction (Ding et al., EMNLP 2017)

ACL

• Tao Ding, Warren K. Bickel, and Shimei Pan. 2017. Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2275–2284, Copenhagen, Denmark. Association for Computational Linguistics.