Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908319943229440 |
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| author | Mittal, Shravika Shah, Darshi Do, Shin Won ElSherief, Mai Mitra, Tanushree De Choudhury, Munmun |
| author_facet | Mittal, Shravika Shah, Darshi Do, Shin Won ElSherief, Mai Mitra, Tanushree De Choudhury, Munmun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities Mittal, Shravika Shah, Darshi Do, Shin Won ElSherief, Mai Mitra, Tanushree De Choudhury, Munmun Social and Information Networks Computation and Language Computers and Society Human-Computer Interaction 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. |
| title | Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities |
| topic | Social and Information Networks Computation and Language Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.10501 |