Identifying substance use risk based on deep neural networks and Instagram social media data.

Identifying substance use risk based on deep neural networks and Instagram social media data.
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DOI:
10.1038/s41386-018-0247-x
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发表时间:
2019-03
期刊:
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子:
--
通讯作者:
Marsch LA
Marsch LA
中科院分区:
其他
文献类型:
--
作者:
Hassanpour S;Tomita N;DeLise T;Crosier B;Marsch LA

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社交媒体可能为我们对物质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,可以根据个人 Instagram 个人资料中的内容自动对个人的饮酒、吸烟和吸毒风险进行分类。共有 2287 名 Instagram 活跃用户参与了这项研究。使用图像深度卷积神经网络和文本长短期记忆 (LSTM) 从这些数据中提取预测特征以进行风险评估。对 228 人测试集的方法进行的评估表明,在我们评估的物质中,我们的方法可以估计酒精滥用的风险,并具有统计显着性。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药物使用风险行为,例如饮酒。自动估计技术的利用可以为下一代人群层面的风险评估和干预措施提供新的见解。
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.
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