Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models

Stigma is a barrier to treatment for individuals struggling with substance use disorders (SUD), which leads to significantly lower treatment engagement rates. With only 7% of those affected receiving any form of help, societal stigma not only discourages individuals with SUD from seeking help but isolates them, hindering their recovery journey and perpetuating a cycle of shame and self-doubt. This study investigates how stigma manifests on social media, particularly Reddit, where anonymity can exacerbate discriminatory behaviors. We analyzed over 1.2 million posts, identifying 3,207 that exhibited stigmatizing language towards people who use substances (PWUS). Using Informed and Stylized LLMs, we develop a model for de-stigmatization of these expressions into empathetic language, resulting in 1,649 reformed phrase pairs. Our paper contributes to the field by proposing a computational framework for analyzing stigma and destigmatizing online content, and delving into the linguistic features that propagate stigma towards PWUS. Our work not only enhances understanding of stigma's manifestations online but also provides practical tools for fostering a more supportive digital environment for those affected by SUD. Code and data will be made publicly available upon acceptance.

Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit

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.

The Evolution of Substance Use Coverage in the Philadelphia Inquirer

The media's representation of illicit substance use can lead to harmful stereotypes and stigmatization for individuals struggling with addiction, ultimately influencing public perception, policy, and public health outcomes. To explore how the discourse and coverage of illicit drug use changed over time, this study analyzes 157,476 articles published in the Philadelphia Inquirer over a decade. Specifically, the study focuses on articles that mentioned at least one commonly abused substance, resulting in a sample of 3,903 articles. Our analysis shows that cannabis and narcotics are the most frequently discussed classes of drugs. Hallucinogenic drugs are portrayed more positively than other categories, whereas narcotics are portrayed the most negatively. Our research aims to highlight the need for accurate and inclusive portrayals of substance use and addiction in the media.