Online communities such as drug-related subreddits serve as safe spaces for
people who use drugs (PWUD), fostering discussions on substance use
experiences, harm reduction, and addiction recovery. Users' shared narratives
on these forums provide insights into the likelihood of developing a substance
use disorder (SUD) and recovery potential. Our study aims to develop a
multi-level, multi-label classification model to analyze online user-generated
texts about substance use experiences. For this purpose, we first introduce a
novel taxonomy to assess the nature of posts, including their intended
connections (Inquisition or Disclosure), subjects (e.g., Recovery, Dependency),
and specific objectives (e.g., Relapse, Quality, Safety). Using various
multi-label classification algorithms on a set of annotated data, we show that
GPT-4, when prompted with instructions, definitions, and examples, outperformed
all other models. We apply this model to label an additional 1,000 posts and
analyze the categories of linguistic expression used within posts in each
class. Our analysis shows that topics such as Safety, Combination of
Substances, and Mental Health see more disclosure, while discussions about
physiological Effects focus on harm reduction. Our work enriches the
understanding of PWUD's experiences and informs the broader knowledge base on
SUD and drug use.

在线社区、物质使用经历、多层次分类模型、GPT-4、PWUD 经历。