Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities
Journal:
arXiv
Published Date:
Apr 8, 2025
Abstract
Widespread stigma, both in the offline and online spaces, acts as a barrier
to harm reduction efforts in the context of opioid use disorder (OUD). This
stigma is prominently directed towards clinically approved medications for
addiction treatment (MAT), people with the condition, and the condition itself.
Given the potential of artificial intelligence based technologies in promoting
health equity, and facilitating empathic conversations, this work examines
whether large language models (LLMs) can help abate OUD-related stigma in
online communities. To answer this, we conducted a series of pre-registered
randomized controlled experiments, where participants read LLM-generated,
human-written, or no responses to help seeking OUD-related content in online
communities. The experiment was conducted under two setups, i.e., participants
read the responses either once (N = 2,141), or repeatedly for 14 days (N =
107). We found that participants reported the least stigmatized attitudes
toward MAT after consuming LLM-generated responses under both the setups. This
study offers insights into strategies that can foster inclusive online
discourse on OUD, e.g., based on our findings LLMs can be used as an
education-based intervention to promote positive attitudes and increase
people's propensity toward MAT.
