Social media may provide new insight into our understanding of substance use and addiction. In this study, we developed a deep-learning method to automatically classify individuals' risk for alcohol, tobacco, and drug use based on the content from their Instagram profiles. In total, 2287 active Instagram users participated in the study. Deep convolutional neural networks for images and long short-term memory (LSTM) for text were used to extract predictive features from these data for risk assessment. The evaluation of our approach on a held-out test set of 228 individuals showed that among the substances we evaluated, our method could estimate the risk of alcohol abuse with statistical significance. These results are the first to suggest that deep-learning approaches applied to social media data can be used to identify potential substance use risk behavior, such as alcohol use. Utilization of automated estimation techniques can provide new insights for the next generation of population-level risk assessment and intervention delivery.

中文翻译：

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根据深度神经网络和Instagram社交媒体数据识别物质使用风险。

社交媒体可能会为我们对毒品使用和成瘾的理解提供新的见解。在这项研究中，我们开发了一种深度学习方法，可根据其Instagram个人资料中的内容自动将其饮酒，吸烟和吸毒的风险分类。共有2287个活跃的Instagram用户参加了该研究。用于图像的深度卷积神经网络和用于文本的长短期记忆（LSTM）用于从这些数据中提取预测特征以进行风险评估。对我们的方法进行的对228人的持久测试集的评估表明，在我们评估的物质中，我们的方法可以估计具有统计学意义的酗酒风险。这些结果首次表明，应用于社交媒体数据的深度学习方法可用于识别潜在的物质使用风险行为，例如饮酒。利用自动估计技术可以为下一代人口级风险评估和干预措施提供新的见解。