Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models
Layla Bouzoubaa, Elham Aghakhani, Rezvaneh Rezapour

TL;DR
This paper analyzes how stigma manifests on social media regarding substance use and introduces a large language model-based approach to transform stigmatizing language into empathetic expressions, aiming to reduce online stigma.
Contribution
It presents a computational framework for identifying and destigmatizing language about substance use on social media using large language models, with practical tools for fostering support.
Findings
Analyzed over 1.2 million Reddit posts for stigmatizing language.
Developed a model that rephrases stigmatizing expressions into empathetic language.
Produced 1,649 pairs of reformed phrases for destigmatization.
Abstract
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…
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Taxonomy
TopicsSocial Media and Politics
