Drug abuse continues to accelerate toward 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 proposed 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 experiment has been conducted on 3 million drug abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug abuse risk behaviors.