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Preprint
Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models
arXiv.org
15 Aug 2024
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 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.
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Details
- Title
- Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models
- Creators
- Layla BouzoubaaElham AghakhaniRezvaneh Rezapour
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991021898972004721