Substance use disorders (SUDs) are a growing concern globally, necessitating
enhanced understanding of the problem and its trends through data-driven
research. Social media are unique and important sources of information about
SUDs, particularly since the data in such sources are often generated by people
with lived experiences. In this paper, we introduce Reddit-Impacts, a
challenging Named Entity Recognition (NER) dataset curated from subreddits
dedicated to discussions on prescription and illicit opioids, as well as
medications for opioid use disorder. The dataset specifically concentrates on
the lesser-studied, yet critically important, aspects of substance use--its
clinical and social impacts. We collected data from chosen subreddits using the
publicly available Application Programming Interface for Reddit. We manually
annotated text spans representing clinical and social impacts reported by
people who also reported personal nonmedical use of substances including but
not limited to opioids, stimulants and benzodiazepines. Our objective is to
create a resource that can enable the development of systems that can
automatically detect clinical and social impacts of substance use from
text-based social media data. The successful development of such systems may
enable us to better understand how nonmedical use of substances affects
individual health and societal dynamics, aiding the development of effective
public health strategies. In addition to creating the annotated data set, we
applied several machine learning models to establish baseline performances.
Specifically, we experimented with transformer models like BERT, and RoBERTa,
one few-shot learning model DANN by leveraging the full training dataset, and
GPT-3.5 by using one-shot learning, for automatic NER of clinical and social
impacts. The dataset has been made available through the 2024 SMM4H shared
tasks.

通过使用社交媒体数据，特别是由有实际使用经验的人生成的数据，来更好地了解物质使用障碍的临床和社会影响，并开发能够自动检测社交媒体数据中物质使用的临床和社会影响的系统，以帮助制定有效的公共卫生战略。