In the last decade, the United States has lost more than 500,000 people from
an overdose involving prescription and illicit opioids
(https://www.cdc.gov/drugoverdose/epidemic/index.html) making it a national
public health emergency (USDHHS, 2017). To more effectively prevent
unintentional opioid overdoses, medical practitioners require robust and timely
tools that can effectively identify at-risk patients. Community-based social
media platforms such as Reddit allow self-disclosure for users to discuss
otherwise sensitive drug-related behaviors, often acting as indicators for
opioid use disorder. Towards this, we present a moderate size corpus of 2500
opioid-related posts from various subreddits spanning 6 different phases of
opioid use: Medical Use, Misuse, Addiction, Recovery, Relapse, Not Using. For
every post, we annotate span-level extractive explanations and crucially study
their role both in annotation quality and model development. We evaluate
several state-of-the-art models in a supervised, few-shot, or zero-shot
setting. Experimental results and error analysis show that identifying the
phases of opioid use disorder is highly contextual and challenging. However, we
find that using explanations during modeling leads to a significant boost in
classification accuracy demonstrating their beneficial role in a high-stakes
domain such as studying the opioid use disorder continuum. The dataset will be
made available for research on Github in the formal version.Comment: Work in progres