  The opioid epidemic, referring to the growing hospitalizations and deaths
because of overdose of opioid usage and addiction, has become a severe health
problem in the United States. Many strategies have been developed by the
federal and local governments and health communities to combat this crisis.
Among them, improving our understanding of the epidemic through better health
surveillance is one of the top priorities. In addition to direct testing,
machine learning approaches may also allow us to detect opioid users by
analyzing data from social media because many opioid users may choose not to do
the tests but may share their experiences on social media anonymously. In this
paper, we take advantage of recent advances in machine learning, collect and
analyze user posts from a popular social network Reddit with the goal to
identify opioid users. Posts from more than 1,000 users who have posted on
three sub-reddits over a period of one month have been collected. In addition
to the ones that contain keywords such as opioid, opiate, or heroin, we have
also collected posts that contain slang words of opioid such as black or
chocolate. We apply an attention-based bidirectional long short memory model to
identify opioid users. Experimental results show that the approaches
significantly outperform competitive algorithms in terms of F1-score.
Furthermore, the model allows us to extract most informative words, such as
opiate, opioid, and black, from posts via the attention layer, which provides
more insights on how the machine learning algorithm works in distinguishing
drug users from non-drug users.
