Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug-abuse risk behavior, given tweets. This is because: (1) tweets usually are noisy and sparse and (2) the availability of labeled data is limited. To address these challenging problems, we propose a deep self-taught learning system to detect and monitor drug-abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) to improve the classification performance and (ii) to capture the evolving picture of drug abuse on online social media. Our extensive experiments have been conducted on three million drug-abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug-abuse risk behaviors.

中文翻译：

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通过自学式深度学习对Twitter上的药物滥用风险行为进行洞察力分析和检测

药物滥用继续加速发展，成为美国最严重的公共卫生问题。在诸如Twitter用户人群之类的人口规模上检测药物滥用风险行为的能力可以帮助我们监控药物滥用事件的趋势。不幸的是，给定鸣叫，传统方法无法有效检测药物滥用风险行为。这是因为：（1）推文通常嘈杂且稀疏，并且（2）标记数据的可用性受到限制。为了解决这些具有挑战性的问题，我们提出了一个深度自学的学习系统，以利用大量未标记的数据来检测和监视Twitter领域的药物滥用风险行为。我们的模型会自动添加带注释的数据：（i）改善分类效果，以及（ii）在在线社交媒体上捕捉不断发展的药物滥用情况。我们已对三百万条与滥用药物相关的推文（具有地理位置信息）进行了广泛的实验。结果表明，我们的方法在检测药物滥用风险行为方面非常有效。