Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models
This addresses the issue of stigma as a barrier to treatment for individuals with substance use disorders, providing practical tools for fostering a more supportive digital environment, though it is incremental in applying existing methods to a specific domain.
The study tackled the problem of stigma in online conversations about substance use by analyzing over 1.2 million Reddit posts, identifying 3,207 with stigmatizing language, and using large language models to de-stigmatize them into 1,649 reformed phrase pairs.
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.